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Analytics Cloud

Learn Modern Data Visualization with Oracle Analytics

Are you a business analyst curious about what Oracle Analytics can do? We recommend a new online course designed to provide you with the essentials of augmented data visualization. It's called Modern Data Visualization with Oracle Analytics, and you can find it online here. If you want to check it out, please enroll—it's free. I know it's good because I volunteered to help Product Management build this online series as a Massive Open Online Course (MOOC) for Oracle Analytics. What will you learn? In this course, you will broaden your understanding of modern—or augmented—data visualization concepts through hands-on training with Oracle Analytics. And we designed this course so that you can jump right into a technical, hands-on product experience. You know how sometimes you volunteer for something at work and then almost immediately regret it? This isn't one of those times; in fact, quite the opposite. And why, you might ask, would you care that I have trouble turning down new work projects? Because you get something really useful out of it--a solid hand-on product tour of Oracle Analytics for analysts and business people. It was an intense project to get it all done and published, but as we built it, we came together as a team. I also learned a lot about MOOCs and now have a shiny new skill myself. Then the enrollments started coming in, and we're having a great time seeing who is taking the course, and what feedback they're sharing with us. We're creating a community, and that's so cool. I'd like to invite you to be part of that. Now down to business. Why a MOOC? Why this particular MOOC? How does it work? What's in it? Answering the "why" question is simple. With the breakneck pace of change in the analytics world, you need access to training that's self-paced, accessible, low cost, detailed, scalable, hands-on, and available anywhere: Helloooo, MOOC on Udemy.com. "Why this particular MOOC" became a subject of some heated debate, as we all realized that if we included everything we wanted, it would be far too long. Many discussions were had to finally nail down what topics to include.  We hope we hit the nail on the head with topics ranging from machine learning-driven visualizations, data blending and mash-ups, augmented data enrichment, advanced maps and charts, and how to narrate and present your analyses. "How it works" is simple as well. Just click here and enroll.   And look what's included in the course: 4 hours of learning videos and screencasts 45 lectures 5 business use cases 4 bonus projects Also, did I mention? It's free! We're targeting this first course for analysts and business people who want to see what Oracle Analytics can do first-hand. Every section is packed with both video and screencasts to showcase each analytics capability. There are also demo files and scripts to download so that you can try it yourself, for real, with the actual product, no marketing fluff. You'll master everything from built-in functions for advanced visualizations, machine learning-based visualizations, and how to collaborate and share your discoveries. Check out the course outline for the full list of topics. Since we can't guess which use case you're itching to try out, we've packed the course with different projects like sales analysis, school donation analysis, HR attrition analysis, as well as advanced projects such as machine learning models for predictive analysis. Curious about some other application for analytics? You can try it with your data too. I'll freely admit the full course is a time commitment. To help you get through it, we've split it up so you can do it in sections. Plus, Udemy has this terrific "offline" capability, perfect for long airplane business trips. Just think how well-honed your analytics chops will be once you're done. I hope you enjoy the course and gain many useful skills from it. This was a fun course to build, in a slightly insane kind of way, and I'm looking forward to creating more content in the future. Once you get started, let us know what you think and how we might improve Modern Data Visualization with Oracle Analytics going forward and visit the Oracle Analytics Cloud website.

Are you a business analyst curious about what Oracle Analytics can do? We recommend a new online course designed to provide you with the essentials of augmented data visualization. It's called Modern...

Analytics Cloud

Analytics Helps Power HR Transformation

Human Resources managers often have a difficult time finding, keeping, and developing talented employees, but the tools they can now use to transform their business have never been more enabling, according to a principal with advisory services firm KPMG. While HR managers use their usual toolsets for employee acquisition and retention, data analytics tools are helping companies keep on top of market trends, discover underlying factors for worker success, and understand how future worker opportunities can be developed into revenue streams.  We asked KPMG's Todd Randolph to join us for a discussion about improving business through analytics. Randolph is a huge proponent of analytics in HR and has written several articles on the subject. Feeling the drive to be competitive, many organizations are moving their core transactional systems to the cloud. In the HR department, KPMG’s 2017 HR Transformation Survey found that 75 percent of surveyed companies reported a successful implementation of cloud technology after undertaking HR transformation. Randolph says it all starts by understanding leading practice HR processes. "Our group does a lot of what we consider HR analytics or human capital management (HCM) analytics," Randolph says. "This involves exploring ways to improve the overall employee experience using analytics. And what we see is the idea of the whole, e.g., self-service or directed analytics. With self-service, it's giving employees the opportunity to help themselves. Rather than waiting for someone to pull data from a number of different data sources to deliver a report, you allow people to go out to a dashboard or even be sent information on their cell phone or mobile device, greatly improving the employee experience. We advise workers or managers and even executives to know what they should be looking at. So, they are reviewing data from areas where the company needs to focus and can take meaningful actions." Often times, Randolph says companies start their analytics journey with a dashboard supplying data from a source or sources that are transparent to them. They may review key data points, compare competitive information, and be guided to indicators that recommend a change or that action be taken. If action is necessary, the user should be seamlessly routed to the transaction system to initiate or approve the required action.     Randolph says that new technologies like Oracle Analytics Cloud are driving his clients to look at how they're using data, specifically in three major categories: embedded analytics, strategic analytics, and advanced analytics. With embedded analytics, Randolph says the business decision to move to the cloud allows embedded analytics to thrive outside the IT department and become a real competitive advantage. "It creates an opportunity to start looking at how you pull data together into a single source of truth for operational reporting," Randolph says. "Strategic analytics—with maybe a product like Oracle Analytics Cloud where we can have that single source of truth pull data from multiple data sources—gives executives and managers the opportunity to look at the organization as a whole from a reporting and analytics perspective. We're also seeing a lot more advanced analytics over the last few years as well as the tools are getting better and easier to use, giving people who are not necessarily data scientists the ability to create some more advanced analytic functions or embedding things like artificial intelligence and other advanced functions." By using embedded analytics, strategic analytics, and advanced analytics, companies can avoid keeping information separate (in silos) and even develop more data transparency. This can include transactional data that can be reviewed, analyzed, reported, and shared so that the data helps validate the right business decision in a very intuitive way. As Randolph explains, "You go into an organization because there’s an issue or some sort of problem.” When that’s about reporting or analytics, it often involves either moving to the cloud or some other ERP type implementation. The client knows they're having issues or can get better at doing their reporting and analytics function.  A lot of times, we find that the problem is siloes. Even within one function such as HR (with its sub functions such as recruiting, payroll, and so on), things can be extremely siloed. You can look across finance or supply chain or any other parts of the organization and see the same thing. There are often different source systems, and you have someone manually pulling data from all these solutions to try to get to that single source of the truth. Then it's typically offline, with people developing some sort of Excel and/or PowerPoint reporting and then delivering that out to stakeholders. So much of what we do is streamlining the process of getting to that single source of truth for our clients.” To hear the conversation with KPMG's Todd Randolph in its entirety, click on the photo below to play the podcast. To find out more about how Oracle Analytics Cloud can best complement your company's business process strategy, visit our website.

Human Resources managers often have a difficult time finding, keeping, and developing talented employees, but the tools they can now use to transform their business have never been more...

Analytics Cloud

Solving the World Bee Crisis with Oracle Analytics

Bees and other insects that pollinate flowers are being driven toward extinction, according to the World Bee Project (WBP), so the global community launched a new initiative in partnership with Oracle using analytics to help slow the decline. At the "Nature of Data" customer event during Oracle OpenWorld, WBP Founder Sabiha Malik discussed the increasingly inhospitable conditions for bees, driven by loss of flower habitats, intensified farming methods, climate change, and increased use of pesticides. "The more we understand the relationships between pollination, food, and human well-being, the more we can do to protect bees and pollinators—and help protect our planet and ourselves," Malik said. Consider the following: Bees are responsible for pollinating one-third of the global food supply Of the 100 crop species that feed 90 percent of the world's population, bees pollinate 70 percent England's honey bees are vanishing faster than anywhere else in Europe, with a 54-percent decline between 1985 and 2005 "Imagine a farmer whose crops yield one-third less than they did before. That is what we are faced with if bee colonies continue to decline." “The World Bee Project Hive Network will remotely collect data using a network of connected beehives,” Malik added. The data will then be fed into an Oracle Analytics Cloud, which will use analytics tools, including artificial intelligence (AI) and data visualization, to give researchers new insights into the relationships between honey bees and their environments. WBP's partnership with Oracle and the University of Reading School of Agriculture, Policy, and Development (SAPD) will allow researchers to 'listen' to the honey bees and analyze intricate acoustic data captured inside the smart hives, including the movement of bees' wings and feet. Combined with other precision measurements—including temperature, humidity, and honey yield—researchers will be able to closely monitor bee colonies, detecting patterns and predicting behaviors. This will enable conservationists and bee keepers to act to protect colonies, such as preventing swarming at the wrong time of year or removing predators like the invasive Asian hornet. The WBP Hive Network launched in the United Kingdom with expansion expected in the United States and Africa next. The value of the data is in informing beekeepers of the various different states of the colony throughout the year to aid colony management. Check out the following video to see how the project came together. Are you ranking higher than your competition? They utilize data analytics--Should you? Take a quick 2-minute assessment and find out your results.

Bees and other insects that pollinate flowers are being driven toward extinction, according to the World Bee Project (WBP), so the global community launched a new initiative in partnership with Oracle...

Analytics Cloud

7 Steps to Building an Actionable Marketing Dashboard

Every forward-thinking business today is focusing on being more data-driven. This holds true even for Marketing departments which are often assumed to be more creatively-focused. In fact, according to Forbes, businesses that have more data-driven marketing are up to six times more profitable year over year than business that have marketing teams that don't run by data. That's a huge incentive to care more about your data. But what good is gathering all that data without a way to compare and contrast the information and come up with valuable insights required to be competitive? "The North America Field Marketing team tackled this head on, we've moved from insights from weeks to seconds," says Dave Ewart, Director of Digital Marketing in Oracle's North America Field Marketing team. "We wanted to gain better visibility and understand the levers that run our business to help us make smarter decisions and more quickly optimize Marketing's investments to drive incremental revenue for the Sales organization." Even a company like Oracle encountered these marketing challenges when it came to becoming more data-centric around our lead-flow engine. First off, our traditional dashboards were limited and difficult to customize to meet different stakeholders' needs. Secondly, because of the limited customization functionality on our dashboards, the team ended up exporting massive amounts of data on a weekly basis, making our data reports up to seven days old. Finally, I relied on a team of up to six people to regularly pull lead reports for different stakeholders, making this effort manual, time-consuming and resource-heavy. Since having access to our business intelligence tools, Oracle Autonomous Analytics Cloud has completely changed the way our marketing organization looks at lead data. Our data is now visible in real-time, customizable and completely low-effort, freeing up human resources. Instead of constantly building reports, we can now extract more insights and inferences. "The beauty of these dashboards is that they equip us with predictive insights as to which marketing programs or channels are performing best—and therefore, should be actioned first by the Inbound BDCs," says Sami Halabi, Oracle Lead Development Specialist Here are the seven views we included in our MQL dashboard in order to make our marketing dashboards more actionable and provide better insights: Track lead volume over time Being able to track lead volume on an even day-to-day basis is crucial for our business. Daily lead volumes can dramatically impact a sales representative's productivity if either too many or too few leads are being sent over. Having this visibility helps marketing managers better pace their campaigns. Breakout lead channels Identifying which channels are driving the most leads - and the top converting leads—is great feedback for marketing. Having the insight to tell if the social media tactics are driving demand, or if our paid channels aren't optimized, helps marketers react more quickly and adjust tactics. Top programs sourcing leads Marketing programs can be costly, so being able to see which programs are hitting the mark and which ones aren't on a regular cadence gives marketers greater control to turn off initiatives that aren't working and double-down on the ones that are. Regions and cities with most leads Seeing where on the map most of our leads and top converting leads are coming from is not only interesting, it can inform where we may choose to host our next event or spend more of our advertising budget to target. Track converted leads over time Most marketers have pipeline and opportunity targets to meet throughout the fiscal year or quarter, so a regular pulse check on where we are towards hitting our goal is critical for running our business. Seeing trends over time can help us foresee if there are roadblocks ahead and help us course-correct. Track conversion rate by channel While lead volume is very important, equally important is understanding what channels are driving more high quality and high converting leads. Top converting channels and programs Finally, visibility into what channels and programs are sourcing the highest volume of converted leads and pipeline for our business is data every marketer should track regularly. Including spend data could also help us calculate ROI and prioritize our programs that way. This dashboard is just one of many marketing analytics dashboards we're building for our Field Marketing organization using  the data visualization capability in Oracle Analytics Cloud. While according to Econsultancy, only 33 percent of marketers have the right technologies for data collection and analysis, that's not the case at Oracle. Start your Oracle Analytics Cloud trial today and get one step closer to becoming a more data-centric marketer.   Guest author, Veronica French is a digital marketing manager with Oracle

Every forward-thinking business today is focusing on being more data-driven. This holds true even for Marketing departments which are often assumed to be more creatively-focused. In fact, according to F...

Analytics Cloud

Oracle Analytics Cloud Gets a Visualization Tune-Up

The latest version of Oracle Autonomous Analytics Cloud includes a refreshed data visualization a capability that empowers business users to explore, discover and visualize their data.  This marks its fourth year since becoming generally available with Oracle's initial cloud analytics offerings (Business Intelligence Cloud Service and Data Visualization Cloud Service).  Oracle Analytics Cloud's data visualization feature addressed that primary shift to user self-service with a data-driven approach to creating compelling analytics.  Let's face it, data visualization in its most rudimentary form has become table stakes with many vendors (like Tableau, Qlik, Microsoft) offering equally good tools.  Every data visualization tool provides the basics of self-service.  Uses can connect their data sources and create great looking charts, or whatever their mood dictates that day.  There is virtually no compelling differentiation between the vendors on this level.  Oracle has taken the next step by heading into autonomous analytics.  Going beyond the common capabilities of centralized business intelligence provided by on-premises core BI systems, then going further beyond the now common self-service capabilities provided by current cloud analytics services.  Autonomous analytics provides capabilities that assist the end users with finding, compiling, and creating compelling stories powered by machine learning that then describes their findings.  Some of these autonomous capabilities have been released into the product already. So, what's coming in this latest version of Oracle data visualization capabilities and what's different from any other data visualization tool on the market?  Oracle Analytics Cloud release version 5 (v5) has many updates in every aspect of the product making it far more flexible with even more capabilities to customize and tune the visualizations to better fit your visual needs.  For instance, we introduce great enhancements to our mobile application, entitled "Day by Day."  We improved search capabilities and newly recognized query terms including top, bottom, first, last and most for queries like "Who were my top 10 performing customers last quarter?"  We also introduce new built-in visualizations that include dynamic heatmap, multiple data layers, 100 percent stacked bar and picto charts (as seen in the images below).                       Data Preparation Enhancements New with v5, the data flows section of Oracle data visualization capabilities introduces smart data types.  This autonomous capability allows the tool to recognize certain data types and intelligently make recommendations.  For example, if "city" appears in your dataset, other related attributes such as population, GPS location, county associated with that city will be recommended, thus autonomously enriching your data.  This data could comprise demographics, weather, and commodity prices that analysts would otherwise usually manually blend from their own personal data sources.  Another aspect is autonomous recommendations for obfuscation of sensitive data columns.  Credit card details for example, should never be display in clear text.  In some regions, this may breach privacy laws.  Therefore, these data columns like credit card numbers or social security numbers are detected and autonomous recommendations will be made to obfuscate those details (i.e., for credit cards) only the last 4 digits remain (as seen in the image below).  There are many such new smart data types available that not only enrich your overall dataset but by removing the otherwise tedious and manual process makes the overall time to decision faster, more efficient.  Along with Smart data types, functions are shipped to the most optimal place to execute them.  If you have a data lake then users can continue to create data flows to enrich their data from their big data source, but will experience optimal performance by having all processing executed by the most appropriate big data engine in the most appropriate location.  Incremental data processing means that existing data flows can be rerun on data sources and only new data will be processed and joined with existing data.  No need to completely rerun the whole process.  Data flow branching allows users to split their data into subsets that can produce a group of related output results or load that data to multiple different locations (as seen in the image below). Automatic Oracle application data replication provides the capability for data visualization to take copies of data sets automatically from your favourite Oracle cloud applications and create an analytic sandbox to enrich it and then perform your analytics without any disruption to the main application or the core operational data. I've only exposed the tip of the iceberg on the changes already set for Oracle Analytics Cloud release 5.  Check out the video below to see these changes in action.   Visit us for more information about Oracle Analytics Cloud.

The latest version of Oracle Autonomous Analytics Cloud includes a refreshed data visualization a capability that empowers business users to explore, discover and visualize their data.  This marks its...

Analytics Cloud

Fuel Innovation with an Analytics Cloud

If you've ever had to chase down data for a report, you know it requires multiple steps, perhaps multiple trips to IT, and possibly multiple software programs to come to a single source of the truth. Successful data analytics doesn't need to be so complicated. It turns out that all you really need is a single, cloud-based platform, whether the data is in a spreadsheet or a data warehouse. "Most business people don't wake up one day and decide to do analytics, they have other jobs to do," says Matt Milella, VP of Development Oracle Analytics. "But they need analytics to do their jobs. We want to make the steps easier and easier, so you can take advantage of the really important information." Milella recently spoke during a webcast entitled, "Make Innovation Your Business with Oracle Analytics Cloud," which outlined the benefits of a cloud-based analytics platform that makes it easier to get to actionable insights. The presentation also included a demonstration of the latest version of Oracle Analytics Cloud. Historically, your path between all your relevant data and the valuable insights you are looking for has been full of obstacles such as outdated or complex data, blending disparate data sets, business modeling, aggregating the information, and publishing among your peers or even customers. Cloud-based analytics platforms overcome these obstacles by allowing for personal and proactive experiences thanks to data visualizations and improved data modeling capabilities. Cloud implementations for analytics platforms continue to outpace on premises business intelligence systems. Researchers estimate the market size will double from its current levels of $57.4 billion to $100.3 billion by the end of 2025. While North America leads the way in implementations, European and Asia-Pacific businesses are also building their analytics in the cloud to glean insight from social media, mobile devices, and other forms of unstructured data. And while business people have tried to take a do-it-yourself attitude with standalone desktop analytics programs to augment their spreadsheet reports, more successful organizations rely on cloud-based analytics platforms to inspect, prep, and deliver relevant data in real time. "The 'one version of the truth' dilemma still exists and it is really important that data sets can be accessed and curated to improve time to insight, deep pattern profiling," Milella says. Instead of multiple vendor solutions cobbled together, Milella suggests a single analytics platform is beneficial in three ways. First, you absolutely need a proactive and actionable analytics platform where the system automatically alerts you to changes, has context at every level, and is accessible and actionable. Second, the analytics platform needs to provide self-service insights. The user should have the freedom to explore with reliable data quality. And third, this platform should have embedded machine learning and advanced analytics that connect to all forms of data and allows for improved exploration and data discovery. "One of the benefits of an integrated analytics platform is that it does a lot of the heavy lifting for you," Milella says Register here to learn more how Oracle Analytics Cloud makes it easier than ever to use data so you can take the guesswork out of innovation.

If you've ever had to chase down data for a report, you know it requires multiple steps, perhaps multiple trips to IT, and possibly multiple software programs to come to a single source of the truth. Su...

Data Lab

Business Analytics' Next Big Thing: Inline Data Prep

Data preparation—also known as data enrichment—isn't new. In fact, I can almost guarantee that every analytics deployment out there has their users doing some kind of data preparation to support their visualizations.  They might be using a specialized tool, like Alteryx, or a bespoke home-grown process that leverages several tools, Microsoft Excel, coding, and sometimes even Microsoft Access. However, the fundamental problem stems from the fact that source data systems don't provide the information in a form to support the required analytics. Also, joining data from multiple sources is almost always required to gain a complete view.  Using multiple tools to extract, transform, enrich, and visualize the data introduces multiple points of potential failure and much greater potential to introduce human error. This results in numbers that remain somewhat inaccurate, stale, or inconsistent between different users or departments. Ultimately, this leaves management sceptical of reports created and they resort back to gut feel decisions to drive their business.  Not exactly a modern or data-driven approach. The trend today is to incorporate data preparation capabilities inline. Simply put, it involves a unified tool and interface to source the data, enrich it, and visualize it. As such, it removes the multiple tools, which reduces cost massively, and increases productivity, efficiency, and accuracy.  Numbers seen in the visualizations are traceable back to their source (data lineage), not lost in confusing Excel-based processing that only some users comprehend or could decipher.  Any analytics vendor worth noting is providing inline data preparation. Qlik has it as part of their most expensive package Qlik Sense, which requires you to contact them for their price—always a bad sign.  Tableau recently released Tableau Prep, a new tool launched this year and only comes with the most expensive option, Tableau Creator. Microsoft with Power BI openly acknowledges that this is the next big thing and are actively looking to invest in this space (i.e. they don't have anything, yet). For the longest time, Power BI users considered that Excel is the data preparation tool for Power BI. Granted, Excel is a pretty powerful data enrichment tool, but that just brings us back to all the issues Excel creates with bespoke home grown hard to manage processes.  Silo tools like Alteryx should begin shaking in their boots.  Who will they work with when all business intelligence tools have built-in data preparation capabilities.  So, what is the correct approach? Well, for one, we understand that our analytics should be a unified system from extraction through enrichment to visualization. And secondly, we don't want to be spending double the money to do something we most likely already have in place.  Only Oracle Autonomous Analytics Cloud, with embedded machine learning, provides access to varied data sources, enrichment and data preparation, along with visualization and mobile access in the same product.  Visit our Oracle Cloud Analytics site to learn more.

Data preparation—also known as data enrichment—isn't new. In fact, I can almost guarantee that every analytics deployment out there has their users doing some kind of data preparation to support their...

Analytics Cloud

Take Business Analytics to the Next Level with Machine Learning

The convergence of big data, analytics, data science, and cloud is creating a need by business managers to optimize their investments with a comprehensive way to derive value from data. Successful companies know how to strategy that includes applying machine learning to their processes, so they can automate and speed their time to decision. For example, companies like yours use machine learning in the following ways: A large bank used machine learning to analyze its collection activities and learned it could eliminate more than 40 percent of customer calls with better outcomes. A global retailer used advanced machine learning to forecast customer demand cutting forecast error in half. A telecom company found that its machine learning yielded a 75x reduction in "false alarms" for churn and instead focused its resources on those truly at risk of leaving. To better illustrate the business applications of machine learning and how it affects you, Oracle sponsored a webcast entitled: "Where Will Machine Learning Take You? See Your Future with Oracle Analytics." Rich Clayton, VP Product Strategy, Oracle Analytics and Mike Lehmann, VP of Product Marketing for Oracle Big Data and Machine Learning lead you through the process, where you can hear how Oracle Analytics uses machine learning to help you understand more, faster and understand how to get started on that path immediately. Every industry has been transformed by applying machine learning to its data analytics strategy, including automotive, healthcare, media, energy, communications, and government. Likewise, ML can be applied to all business divisions such as HR, finance, sales, marketing, and IT. Whether it's investigating customer churn, text sentiment analysis, forecasting and modeling, data discovery and auditing, or transactional data extraction and transformation, Clayton notes that machine learning enables better business data visibility. "Traditionally what we see is that people not being able to work together," Clayton says. "What adding machine learning to Oracle Analytics Cloud does is ultimately help them organize their work, build, train and deploy these data models. It's a collaboration tool whose value is that it accelerates the process and allows different parts of the business to collaborate, giving you better quality and models for you to deploy." One of the barriers to has been the multiple layers that data must pass through before it is processed, and its value derived. There is an extraction layer, data blending, modeling, aggregating and publishing long before there is a discovery mode. Clayton argues that by automating and embedding machine learning allows for faster time to decision. "There is a foundational opportunity by taking some of these components and embedding it into a value chain from an analytics perspective," Clayton says. A typical finance department is routinely burdened by repeating a variance analysis process—a comparison between what is actual and what was forecast. It's a low-cognitive application that screams for assistance of machine learning, Clayton notes. "By embedding machine learning, finance can work faster and smarter and pick up only where the machine left off," Clayton says. To hear the entire conversation, register for the webcast and see where your future will go with machine learning as part of your data analytics strategy. For more information about Oracle Analytics Cloud, visit our website.

The convergence of big data, analytics, data science, and cloud is creating a need by business managers to optimize their investments with a comprehensive way to derive value from data. Successful...

Analytics Cloud

Data Scientist Kirk Borne Discusses Business Impact of AI and ML

Adding artificial intelligence, machine learning, and other cognitive interactions to traditional business processes and applications enables greatly improved user experience and productivity. These technologies are already impacting all levels of business including finance, marketing, human resources, and sales. Proponents point to incremental changes in the way we work with AI and ML. From the auto-complete or autocorrect on your phone, to chatbots, and virtual assistants like Siri and Alexa. But larger implementations are also taking hold. The New York Metropolitan Transit Authority (MTA) is using machine learning to understand rider patterns and will be changing color coding of their maps to help commuters and tourists get to their destinations faster. However, these visible AI and ML projects are more the exception than the norm. Industry experts point to massive interest and investments over the next few years: 61 percent organizations surveyed by O'Reilly Media identify machine learning as the most significant data initiative for 2019 $57.6 billion in spending on AI and machine learning will happen by 2021 compared to $12 billion in 2017, estimates analysts with Deloitte. $3.5 to $5.8 trillion in potential annual AI-derived business value across 19 industries is very possible, according to the McKinsey Global Institute research paper. To help put these trends in perspective, we invited Kirk Borne to the Oracle Analytics Advantage podcast to discuss the value of AI and Machine Learning for business decision-making. Borne is often touted as a worldwide influencer on data science and one of the leading thinkers on the topic of large scientific databases and information systems. Borne currently consults clients for Booz Allen Hamilton. He's a researcher, blogger, data literacy advocate, TEDx speaker, and author of several books. He's even a project scientist for NASA as part of its Space Telescope Science Institute. During his interview, Borne explained that the successful evolution of AI and ML in business should be linked to solving the questions at hand "I like to remind people that machine learning is an algorithm that learns from experience—it detects and recognizes patterns in data," Borne says. "So, if you are classifying a disease or if you are classifying a customer, or information in your weblog, once you see that pattern, you can take the appropriate action." While the promise of AI and ML are encouraging, one strong barrier to adoption is a cultural one, according to Borne. "If you are going to have AI help you in decision making, that means letting go of some authority of your own," Borne noted with a nod to top executives in contrast to mid-level managers who may see AI and ML as a threat to their jobs. To help mitigate cultural changes, Borne advocates for a group of data experts instead of a single decision-maker when it comes to implementing AI and ML. "Whether the person is the chief data officer, chief data scientist or chief algorithm officer—which is a term I heard the other day—this person should have the view that people should be empowered to speak up when they see something in the data that can help improve the business they are empowered to bring that forward – it's not just the executive suite or the data scientists." Thankfully, many companies are already setting themselves up for success by adopting a cloud-based analytics infrastructure, which he says is needed to keep costs down.  "If all you need are a few minutes to process data and some of these major cloud providers are providing the tools that also use the cloud—then you don't have to incur any more costs," he says. Borne is also eager to change the meaning behind the acronym of AI. Whereas most people identify AI as artificial intelligence, Borne suggests businesses think of decision-making with the "new AI" where the letter A could mean Accelerated, Actionable, Adaptable, Amplified, Assisted, or even Augmented. "I think taking full advantage of artificial intelligence is a stretch even for the major companies that announce they are 'AI First,'" Borne says. "Even the ones that are fully using AI tell me they are still on the growth curve. Your average company has typically not even started with AI, which means there are a lot of opportunities to get started." To hear the full interview, check out the Oracle Analytics Advantage podcast by clicking on the podcast photo below and visit the Oracle Analytics Cloud website to see how to apply AI and machine learning to your data strategy.

Adding artificial intelligence, machine learning, and other cognitive interactions to traditional business processes and applications enables greatly improved user experience and productivity. These...

Analytics Cloud

Build the 'Right' Company Culture with Oracle Analytics Cloud

After moving to the Silicon Valley, I immediately noticed the dedication employees have to their work. Seeking the right company culture in the Silicon Valley seems the norm yet giving 110 percent no longer differentiates you from other workers. This type of determination led the Silicon Valley to major success, however, it could be detrimental to HR managers who need to keep great talent and prevent burnout or employee churn. At the moment, HR managers are at a disadvantage. The spirit and enthusiasm employees have for the endless amounts of job opportunities is at an all-time high, yet Deloitte's 2016 Global Human Capital Trends reports only 12 percent of executives believe their companies are driving the 'right' culture for their employees. Currently, there's a related phenomenon to this executive disconnect. Have you heard about employee ghosting? With more jobs than there are people out of work, it has become common for potential candidates to have an offer from one company but start elsewhere without any notification, even after emailing, or calling multiple times. All of that energy put into finding the right candidate gets wasted. Were there clues in the candidate's data that could have prevented this? Therefore, HR executives need accurate data to flag up any concerns in the hiring process or even after onboarding. HR needs to spot the common trends and correlations to make the right staffing choices. While the human resources department supports a positive working environment, it is also inevitable that they hold the most weight when the 'right' culture isn't so right anymore. The definition of the right culture varies. However, Deloitte's report determines that a successful company culture is determined by "patterns of accepted behavior, and the beliefs and values that reinforce them." Accepted behavior is gained from colleagues through a team effort. To measure what factors contribute to the 'right' overall company culture, it is important to remove bias of opinion and turn to numbers to identify success. Analytics is the single source of truth to gain visibility into validating or negating what factors contribute to overall company culture. Based on the way company culture has shaped, HR executives require people analytics to gain insight and thrive the workplace. The ability to understand the "why" behind employee performance is key to addressing productive and positive change. Deloitte states, only 8 percent of companies report they have usable data and 30 percent of HR executives list difficulties in assessing what data is truly useful. Oracle Cloud Analytics empowers HR leaders to understand workforce data, explore workforce trends, dig into the root causes of personnel issues, and take action into a strategy that benefits the business and more importantly, the people. Create a smart business value with operational reporting to improve performance. Accurately engage with useful data and create the 'right' company culture by becoming data-driven today. Visit our Oracle Cloud Analytics site to read more.

After moving to the Silicon Valley, I immediately noticed the dedication employees have to their work. Seeking the right company culture in the Silicon Valley seems the norm yet giving 110 percent no...

Analytics Cloud

Take a Practice Swing at Data using Oracle Analytics Cloud

You can learn a lot about your business analytics strategy by testing out the tools on readily-available data. This time around, we'll use information from the British Open and run it through Oracle Analytics Cloud to glean valuable insight and take a few practice swings at an analytics tool for the modern business manager. Outside of the United States, the one men's golf championship that counts the most is the one happening this week at the Carnoustie Golf Links in Angus, Scotland. Indeed, the British Open is where it all started. The oldest major. The original. The Claret Jug is one of the most iconic trophies in all of sports. As a golf nut, I am looking forward to Sunday, July 22 as it is a tradition for the whole family to watch the final round. Furthermore, I am excited to create my fantasy team for this event and watch my picks play their hearts out! But who should I pick? Who has had the most success at The British Open? Who will make the cut? As a numbers guy, I naturally turn to statistics, metrics, and data to help me decide. This year, I am relying on numbers I found that focuses on past leaderboards. From there, I created a spreadsheet of the players expected to show up at Carnoustie. I then uploaded this information to Oracle Analytics Cloud and began to create some data visualizations. This is how I typically make my decisions for my fantasy golf team. The same can be applied to most questions in the business world. How are my sales? Did that marketing plan work? Will I be able to hire more people? All of these important questions can be answered through analytics. Back to golf… the first thing I want to know is which golfers have had the most success making the cut over the past ten years? This is important because if a player isn't golfing on Saturday or Sunday, they can't contribute to my fantasy team score. So…after I upload my spreadsheet into Oracle Analytics Cloud, I use the filter function to only show me rounds one and two. One item of note. Because my spreadsheet uses raw information from The Open website, I had to replace designations for situations such as disqualification with a numerical value. For example, I replaced "M/C" (missed cut) with 73 since only the top 72 and ties will make the cut. Next, I would like to understand who has the best finishes on average over the past ten years? This is critical because fantasy points are directly related to how well a golfer is playing. Using the same data set in Oracle Analytics Cloud, I simply focus on the best finishers. Finally, I would like to identify the best of the best. The players who average 72 or less in each round and see their total number of strokes taken in the last ten years. This data is not easily apparent in any spreadsheet as it relies on several filters and then an aggregate total from several data points. These players are certainly "needles in a haystack". So…I leverage Oracle Analytics Cloud to make this happen. As you can see, using data visualization can help you make informed decisions, quickly! This is the value of Oracle Analytics Cloud…deep insights obtained in a very easy, quick manner. I hope you found this blog informative and that you make use of the spreadsheet I attached above. To see how easy it is to use data visualization, here is a link to download the software. Better yet... sign-up for a trial version of Oracle Autonomous Analytics Cloud. Lastly, for current Oracle Analytics  customers using Data Visualization, be sure to visit the Oracle Analytics Library and download sample files to take your analytics to a new level.

You can learn a lot about your business analytics strategy by testing out the tools on readily-available data. This time around, we'll use information from the British Open and run it through Oracle...

Analytics Cloud

The World Cup: Analyzing the Finals and Players

The 21st World Cup in Russia, having held us captive for two weeks, is soon coming to an end. At the time of writing, we've seen 56 games, 146 goals, 189 yellow cards and some 'upsets' with favorites Spain, last time winners - Germany and last time runners-up—Argentina going home early on. Continuing our series taking data sets from 'less typical' sources and analyzing with Oracle Analytics Cloud tools, it felt remiss—in the wake of some shocks—not to follow up on my blog written ahead of the tournament. The teams left in the competition might not be those we'd had expected, but otherwise how is the tournament comparing with previous years?    Teaming up with Ismail Syed, Oracle UK Intern, we took the results data and ran the analysis.    Player Ratings Firstly, we wanted to analyze player performance against expectation and found some relevant stats from a 2018 data set. It's interesting that those teams with players rated higher than the rest of their teams (Argentina—Messi, Portugal—Ronaldo) surprisingly left relatively early on. Indeed, Brazil and Uruguay are the only teams remaining with a player rated higher than 9. Teams like Russia, Croatia and England, whose players are more evenly rated, have progressed perhaps more than expected, pulling together to make it that step further. Maybe this year we'll see one of these teams succeed and show collective teamwork really pays off. Keeping it in the region   In our previous blog, we identified hosting the tournament has its advantages—and, with Russia causing one of the major upsets, knocking out favorites Spain, this is still proving true. We also discovered that generally countries perform better when the World Cup was hosted in their region. And, the local teams are doing well in Russia—6/8 of the quarter finalists are from Europe, with the final two from South America.     Are complaints VAR-fetched? It's been a HUGE discussion point of the competition, but how much impact is VAR having on the number of goals scored this year? Actually, not as much as you might expect. The goals scored on average so far are pretty much in line with those scored in the last six tournaments. It'll be interesting to see how many goals come in the later stages. However, more penalties have been awarded in open play than in any previous tournament. This year, we've seen a penalty awarded every 0.55 matches so far. If they continue at that rate, there will be a total of 35 penalties—much higher than the current record, a mere 18 for an entire competition.   Who's a Shoe-In for a Golden Boot? But who has been scoring the goals? This graph takes data from the initial group stages to show which countries have found the net the most. Note, the biggest goal scorers are in the bottom right-hand corner—Belgium had the best goal difference, with England, Croatia and Russia close behind. Incidentally, England, Croatia and Russia all progressed through to the quarter finals through penalty kicks, so maybe all those on-target shots during the group stages was good practice for their players to hit the back of the net when, quite literally, put on the spot.  And when it comes to the Golden Boot? England's Harry Kane (6) and Belgium's Romelu Lukaku (4) are in prime position, playing their key part for their countries in this tournament. Indeed, if Harry Kane continues to be top, it will be the first time for an English player since 1986. Final Whistle Interestingly, while the teams who are succeeding in this tournament might not be those we'd have initially thought, the data shows that performances overall have been relatively average to what we've become used to in the most recent competitions. Emotional shocks don't necessarily equate to a difference in execution overall—and that's as much true in business as it is in the world of sport. Thankfully, data will always be there to show us what's really happening underneath the surface—and that's why it's so important for everyone to have access to a self-service data visualization tool. Allowing them to make sense of their information and challenge their assumptions—even if data skills isn't necessarily their strength. So, why not see what surprises you can find in your data. Watch our short demo and sign up for an Oracle Analytics Cloud trial to learn how to create data visualizations. It's much easier than wrestling with spreadsheets and you might find something that 'upsets' your initial beliefs for a better business outcome. So let's see who's going to take home the World Cup this year. If your countries team is still involved, keep dreaming! For the first time in a long while, it could be anyone's….    

The 21st World Cup in Russia, having held us captive for two weeks, is soon coming to an end. At the time of writing, we've seen 56 games, 146 goals, 189 yellow cards and some 'upsets' with...

Analytics Cloud

Analyzing the Blockchain Advantage

Blockchain is making an impact everywhere around us. From its origins as a cryptography method debuting ten years ago, the practice of securing data blocks in a chain led the way to developing digital currencies, authenticating crowdfunding, and verifying company governance documents, among other uses. You don't have to look far to see how companies are investing and growing their business with the help of blockchain technology. California recently passed legislation to encompass blockchain technology for electronic signatures and smart contracts. Walmart is using blockchain to track groceries along its supply chain. The related market is forecast to gain revenue worth $20 billion by 2024, up from the $315 million in revenue companies realized in 2015, according to estimates with Transparency Market Research. But as in all things data, analyzing the information helps keep companies competitive. There's a lot of hype around blockchain thanks to Bitcoin and other cryptocurrencies. However, we know the technology has practical uses in business and is even more valuable when using cloud-based analytics to derive insights. To help make sense of it all, Oracle Analytics invited Vinny Lingham to our podcast. Lingham is a well-recognized South African Internet entrepreneur and an active technology investor focused primarily on Bitcoin and Blockchain projects. He's also an investor on the South Africa version of the television show, Shark Tank, where they have dubbed him the "Bitcoin Oracle." Lingham is currently the co-founder and CEO of Civic Technologies, a blockchain-powered startup company that specializes in digital identity. Before Civic, he was the co-founder and CEO of Gyft, a mobile gift card company founded in 2012 and backed by Google Ventures. As part of our conversation, we asked about the balance of enterprise adoption of blockchain verses the consumer adoption of blockchain applications. Lingham recounted his company's work with beermaker Budweiser to make a prototype crypto beer vending machine, showcased at a recent conference (Consensus 2018). While it sounds like a fantastic idea at a sporting event or music festival, it could be a legal nightmare if someone under legal drinking age got served a Bud. One solution might be to enforce strict identification rules and monitor the vending machine with a guard. Lingham suggested a different tactic. "We were in discussions about age verification with Budweiser, but for them, the blind spot is that they can't sell beer in vending machines because you can't tell how old the person is when they order," Lingham said. "We have a technology that verifies a person's identity using their phone and blockchain technology." And while a vending machine for beer is more proof-of-concept than ready to deploy, Lingham says companies need to invest now into blockchain and learn how to analyze the data to establish a foothold on the future. "It's going to be a long-term thing in trying to institutionalize blockchain," Lingham said. "This kind of technology could be used for alcohol, sure… but what about vending specific amounts of prescription medication to avoid overdosing, or verifying documents for General Data Protection Regulation (GDPR)." Click on the photo to listen to the full podcast. To see how easy it is to use analytics in your enterprise, visit our website, take one of our quick tours, or try the data visualization simulator. Better yet, sign-up for a trial version of Oracle Autonomous Analytics Cloud

Blockchain is making an impact everywhere around us. From its origins as a cryptography method debuting ten years ago, the practice of securing data blocks in a chain led the way to developing digital...

Analytics Cloud

Tomorrow's CFO Succeeds Today with Analytics

Being a financial planning and analysis professional in today's world always means being one step ahead. Finance professionals know this as the speed of data and day-to-day decision-making requires more than simply accurate financial statements and reports. In today's intensely competitive world, finance leaders partner with every line of business, from marketing and human resources to manufacturing and the supply chain. Their companies demand the analysis and advice needed to boost profitability and drive growth. They need forward-looking, predictive insights from a growing mountain of data. Unfortunately, many finance teams are still doing planning and analysis the same way – with static spreadsheets and rigid, traditional, labor-intensive processes.    To forecast more accurately and make smarter decisions, tomorrow's finance professionals need faster ways to improve quality and access to make data richer and more relevant. This is where Oracle Analytics Cloud can help finance leaders with powerful analytics for data-driven decisions, resulting in better business outcomes and more growth opportunities. Imagine asking questions from your data such as: "What is the status of the close process?"  "What's the outcome if we increase supply chain lead times by one week in our new facility?" "What does our new merger mean for our debt to equity ratio over the next eight quarters?" "What is the impact of an acquisition on finances with a new bid range?" The answers come automatically, secured in the cloud, and powered with artificial intelligence. The video below shows just how a modern finance professional can keep one step ahead of the competition inside and outside her company. Keep an eye out for some new features including natural language processing, autonomous data gathering, and customized visuals that keep the important information at a glance. Jessica Ross is one of those future-minded finance leaders. Ross is vice president and controller at online custom garment service, Stitch Fix, which uses Oracle Analytics Cloud to fulfil its executive decisions with financial power. "It's imperative that we create a crystal-clear vision of how we're going to shift the finance and accounting organization from a transaction capability to a transformation capability," Ross says. "We have this amazing algorithm and data science team and they want to focus on the data. Our opportunity as finance is to translate all of that information into business insights into ways of reporting that feel good to the business and drive new value." Discover how Oracle Analytics Cloud gives finance leaders powerful analytics for better data-driven decisions, driving the business forward through growth opportunities and shaping their future. Visit Oracle Analytics Cloud for more information and to see how we're empowering finance with smarter, faster, and richer data-driven decisions.

Being a financial planning and analysis professional in today's world always means being one step ahead. Finance professionals know this as the speed of data and day-to-day decision-making requires...

Analytics Cloud

Hidden Gems in Your Data: 2018 USGA U.S. Open

This week is the start of the 2018 U.S. Open Championship golf tournament. As with all four majors, I create a data visualization blog and have historically focused on "strokes gained". This time, I am using data found on my personal fantasy sports app as well as scoring numbers found on the PGA Tour website. The goal is to determine which six players I should use on my fantasy golf team this week. It would be easy to pick the highest 'points per game' players as found in my own data but... because you have a salary cap and the higher scoring players cost the most, you cannot do this. You must use other methods, like searching for the players who score the most birdies to create a team that fits under the salary cap. So... I need to find hidden gems. In business, clear answers are not always apparent. This is why data visualization is important. Being able to "see" numbers visually often leads to new insights and directions that may not have been realized otherwise. Let's take a look at the numbers. As I said earlier, the goal is to determine which six players I should use in my fantasy golf team lineup. I can't simply take the highest 'points per game' players because a team with the top six guys would be over the salary cap. However, if I look at other metrics while also looking at the best players, I can find some interesting options. In the table below, I filtered-in my data's highest 'points per game' guys as well as the players that have highest percentage of 'birdie or better' holes as found on the PGATour.com website. I then sorted my list by the 'points per game' and made an interesting discovery. Phil Mickelson scores slightly less points than Jason Day but actually has a higher 'birdie or better' percentage. The best part... Phil costs $8,600 while Jason costs $10,500. That's going to help my salary cap situation! If I keep the same filtering used in the first visualization while looking at different metrics, I can get an even more interesting view. In the filtering section of the screen below, you can see I am still working with the 'points per game' and 'birdie or better' metrics. However, the data I am working with in the visualization is actually 'birdie average' and 'birdie to bogey ratio'. This is a fascinating way to identify potential picks as I am digging deeper into each players statistic. An important point to make here is that I don't want to mix the numbers in the above visualization with the data in the below. Why? Because they are on a different scale. 'Points per game' numbers range from 40-100 and 'birdie or better' data range from 15-30. However, 'birdie average' and 'birdie to bogey ratio' metrics range from 1-5. If I were to mix all four of these data points into one visualization, the value of the small numbers wouldn't be apparent. This is why the filtering functionality is so important. In the visualization below, I can see a few hidden gems. Justin Rose, Dustin Johnson, and Henrik Stenson all have better 'birdie to bogey ratios' than their peers when sorting by 'birdie average'. In order of my fantasy golf team app's salary, Johnson costs $11,700, Rose is $9,900 and Stenson costs $8.800. This helps me pick who I want on my team. Bar charts are tried and true when consuming numbers because they are easy to understand. However, if you want to spice things up a little, try a "chord diagram" like the below. It' the exact same data setup as above, simply pictured differently. Here... you are looking for the biggest, darkest section to identify the key players. Notice how the same three players stand-out? Let's try one more visualization. In the below, I focused only on 'points per game' but used all my birdie stats as filters. Greater than or equal to 20% of the time scoring a 'birdie or better'; greater than or equal to four birdies per round; and greater than or equal to 1.5 'birdie to bogey ratio'. This is called a "word cloud" and is one of my favorite ways to see the data. Like the "chord diagram" above, bigger and darker are the key indicators. Some new names are now surfacing. Justin Thomas, Jon Rahm, and Branden Grace seem to stick out. Justin's salary is $11,000, Jon is $9,500, and Branden is $8,400. I know who will be making my team.  As you can see, visualizing data can lead to better, deeper insights. If you would like to see the spreadsheet I used for these visualizations, you can download here. These findings were obtained in a very easy, quick manner. I simply loaded my data and I was on my way to picking some key player for my fantasy team. I hope you found this blog informative and that you make use of the spreadsheet I attached above. To see how easy it is to use data visualization, here is a link to download the tool. Better yet... sign-up for a trial version of Oracle Autonomous Analytics Cloud. Also, be sure to visit our website and take one of our quick tours or try the data visualization simulator. You can also find more resources on our customized content page. Lastly, for current Oracle Analytics Cloud customers using Data Visualization, be sure to visit the Oracle Analytics Library and download sample files to take your analytics to a new level.  

This week is the start of the 2018 U.S. Open Championship golf tournament. As with all four majors, I create a data visualization blog and have historically focused on "strokes gained". This time, I...

Data Visualization

2018 World Cup: Analyzing Team Win Percentages

It's here again. The largest single-sport spectator event in the world. Every four years, we futbol (soccer) fans get to enjoy an entire month of thrilling matches played at the highest level. It simply does not get any better. If your home-country made it...congratulations! If your nation didn't qualify...just pick your next favorite team. Being American, I am going to have to choose a few other teams to cheer for. Perhaps Mexico. Maybe a powerhouse like Germany or Brazil. Russia would be cool since they are hosting. Egypt, Iceland and Panama have never won a World Cup game...how awesome would that be? We have thirty-two choices. Pick well. To help me decide, I went to the official federal international football association website, found some basic team stats and added them to a spreadsheet. I then manually added each match and their respective dates as well as a formula to calculate winning percentages. Feel free to download and use how you choose. What does this have to do with Oracle? Data. Your business has tons of it. Understanding it is the point. I had a problem. My team isn't in the World Cup. I still want to watch but who do I cheer for? This is where I turn to Oracle Analytics Cloud data visualization to help me make a few choices. Below are some visualizations I created to better understand the situation. The World Cup is all about survival. Step one—get out of the group stage. To identify my favorites for advancing past the first round, I analyzed each team's World Cup winning percentage and average goals scored as well as the winning percentage of the other three teams in their group. Below is the winning percentage and average goals scored for each team. The bigger the text, the higher the winning percentage. The darker the text, the more goals they score on average.   That's a good understanding of the entire landscape. But like I said, step one is getting out to the group stage. Below are the winning percentages of the three opponents for each team, in each group. Group A appears to heavily favor Russia and Uruguay as their opponents haven't won a ton of World Cup matches. Group B favors Spain and Portugal. Group C looks to be harder than A or B. France and Denmark should advance. Argentina and Croatia should make it past in Group D. Another tough set. Brazil is the only clear leader in Group E. Very similar to Group E, Group F only has one clear favorite. Never bet against Germany. In Group G, England and Belgium should easily advance. Probably the most evenly-matched set, there are no clear favorites in Group H. Of course, these metrics are predicated on historical fact. Will that tell the future? Sometimes. In lieu of any real understanding of these teams, their new players, coaches and systems, it's the best guess. Obviously, this is all in good fun and I, like many, can't wait for it to begin. As you can see, a simple question of "who should I root for?" led me to create a spreadsheet and build some visualizations that allow to me identify some of my favorites. These findings were obtained in a very easy, quick manner. I simply loaded my data and I was on my way to filtering by group and "seeing" each situation. I hope you found this blog informative and that you make use of the spreadsheet I attached above. To see how easy it is to use data visualization, here is a link to download the tool. Better yet... sign-up for a trial version of Oracle Autonomous Analytics Cloud! Also, be sure to visit our website and take one of our quick tours or try the data visualization simulator. You can also find more resources on our customized content page. Also, for current Oracle Analytics Cloud customers using Data Visualization, be sure to visit the Oracle Analytics Library and download sample files to take your analytics to a new level.  

It's here again. The largest single-sport spectator event in the world. Every four years, we futbol (soccer) fans get to enjoy an entire month of thrilling matches played at the highest level....

Analytics Cloud

How Cloud-Based Analytics Transforms Finance

(and just maybe the Stanley Cup) By Viktor Sahakian, Hitachi Consulting As an avid hockey fan—and an amateur weekend player—I love to follow NHL teams and players and get immersed in league statistics. Sometimes I even wonder if you had the right analytics program, you might be able to predict the next winner of the Stanley Cup. Regrettably, the performance of analytics when it comes to predicting Stanley Cup winners has been less than stellar, especially when your emotions get involved. This year my "heart-based" analytics program predicted the Los Angeles Kings—my hometown team—would take it all. Suffice to say; I was woefully disappointed. This kind of sports-fan analysis applies to my day job as a technology consultant. I see analytics, especially the cloud-based kind, delivering consistently great results. Cloud analytics is playing a growing role in businesses worldwide. Finance organizations take a leading role in using cloud-based analytics to diagnose and fix performance shortfalls, and proactively avoid pitfalls based on past patterns. Why finance? Because every line of business line of business eventually rolls up to finance. That means they're in the best position to see the entire picture, from manufacturing to supply chain to sales and marketing. From Identification to Prediction Costs, profitability, and compliance are among the top areas of concern for finance organizations these days—and that's where analytics can make a big impact. Analytics solutions can help businesses identify where costs a getting out of hand and highlight the most profitable lines of business. What executive would not want to know that? Simply identifying problems only gets you so far. To make a real difference, you also need to know the causes. With the latest cloud-based analytics platforms, such as Oracle Analytics Cloud, you can quickly get to the bottom of cost overruns and other indicators of poor performance. A leading manufacturer I work with, for example, used analytics to trace the problem to a bottleneck in its supply chain. Now it's using analytics to drill down deeper into its supply chain operations. Of course, every executive would prefer to prevent issues from happening in the first place. By examining data patterns, analytics cab help companies spot emerging problems and then head them off. In the supply chain example, analytics could tell decisionmakers to shift vendors or redeploy employees as soon as early-warning signs appear. It's all about going from reactive to proactive problem solving. And prediction is just the tip of the analytics iceberg. Companies are now beginning to incorporate artificial intelligence to recommend the best way to steer the company to achieve desired outcomes. The Cloud Edge So, I'm not surprised that more and more finance organizations are moving their analytics to the cloud. There's no going back at this point. A few still resort to spreadsheets, but only in the cloud do you have the flexibility to rapidly integrate multiple sources of information in real time that is key to running the business. Large organizations tend to run disparate systems serving lots of different divisions, or they've gone through mergers and acquisitions that left a patchwork of applications and databases. The cloud brings all your data together on a common platform. And it can come from a mix of systems, whether on-premises systems or the cloud and in any format, structured or unstructured. Cloud analytics can't be beaten for this kind of flexibility and ease, not to mention the speed with which you can stand up a solution. Gone are the long waits for complex data center integrations before you start getting insights and predictions.   Cloud analytics is also a great choice for smaller and midsized finance organizations since they are typically stretched for resources and capacity. In recent years, a lot more is being asked of these teams. Instead of just balancing books and paying invoices, they're expected to dig into mountains of data to help the business boost performance and evaluate new opportunities. Cloud analytics can enable a small staff to take on this big new responsibility. Similarly, cloud solutions can make the job of monitoring transactions and secure data a lot easier, as organizations take advantage of security technologies and best practices built into the cloud infrastructure. And don't forget about regulatory compliance, a big burden for finance organizations large and small. Once they move to the cloud, keeping up with constantly changing regulations becomes largely a matter of applying that latest update to their cloud applications. Will cloud analytics ever be powerful enough to pick the next Stanley Cup champion? Probably not. But it may help teams improve performance by making timely adjustments to their personnel and strategy. Hey, LA Kings are you listening?   Want to learn more? Listen to Viktor on the Oracle Analytics Podcast. Author: Viktor Sahakian leads Hitachi Consulting's Oracle technology practice and has over 25 years of consulting experience with applications development, implementations, and systems architecture. He has directed and provided project management and technical leadership on multiple global implementations and transformational projects. His current focus areas are cloud-based SaaS, PaaS and IaaS transformations as well as IoT-based solutions to help companies address business challenges.

(and just maybe the Stanley Cup) By Viktor Sahakian, Hitachi Consulting As an avid hockey fan—and an amateur weekend player—I love to follow NHL teams and players and get immersed in league...

Analytics Cloud

Extreme Finance: Powering Finance with Richer Analytics

Individually, a ceramic tile can have a unique color and shape. But when matched with other tiles in a specific pattern, an artist creates a beautiful mosaic that is pleasing to the eye and whose symbolism is understood by all of those around. The same can be said of financial business data. Part 1 of this blog series covered how FP&A (Financial Planning and Analysis) can work Smarter leveraging machine learning techniques and Part 2 described how a modern autonomous analytics solution powers FP&A to work Faster.  Working smarter and faster doesn't mean much if the analytics produced are of the same caliber as existing business processes and tools produce.  In this piece, I'll describe how the analytics produced are actually Richer providing more color and insight than previously possible.  This allows you to see the business and the problems more clearly with higher resolution that ultimately leads to better decision making. Creating Richer Finance Richer finance? I'm sure every finance department would like to be more affluent, but the term here, if it were a color, would describe it as being more vibrant, deeper or brighter. The meaning here describes the greater depth and substance the analytics and predictions created will provide. This is achieved by ensuring all relevant information is considered in your analytics regardless of which system or store it may reside. Whether that means a data lake, a second GL system from a new acquisition, supplier or customer data, or even personal data held in local XLS files.  Accessing that information should be in the hands of the business professional without reliance on IT, who could eventually provide it, but with an unacceptable wait time that reverses our Faster Finance objective.  For more accurate business decisions greater resolution data is needed.  Fast and complete access to transaction level information and from a single user interface. Not every user can become an expert in the multitude of tools required to access, extract, prepare and report information. Multiple tools mean more licenses and more training and support costs. For a finance department to become richer in both contexts of the word, they need to reduce costs, simplify processes and empower the financial analysts that need the information. This is achieved in a unified autonomous platform that integrates data access, preparation and reporting/visualizations in a single tool. A unified process and tool also reduce points of failure or opportunities to introduce human error thus reducing risk and producing more trustworthy predictions.  Enjoy Tomorrow's Analytics Capabilities Today No one can slow the pace of change, but it is possible to stay in front of it by doing more with the resources an organization already has in place. With Extreme Finance, tomorrow's analytic capabilities are here today.  With machine learning and other emerging technologies, companies can employ a smarter, faster, and richer approach to finance that was unavailable just a few years ago. Just as smartphones transformed mobile communications, Extreme Finance has the potential to make FP&A professionals more agile, self-sufficient, and effective using all the data within the organization. To learn more about how Oracle Analytics Cloud can create new opportunities, visit oracle.com/analytics

Individually, a ceramic tile can have a unique color and shape. But when matched with other tiles in a specific pattern, an artist creates a beautiful mosaic that is pleasing to the eye and...

Analytics Cloud

Extreme Finance: Powering Finance to Work Faster

In Part 1 of this blog series, I described how FP&A could work smarter by leveraging modern analytics.  In this piece I'll describe how FP&A can work faster with the same human resources and skills already in place, and still reduce costs.  Tomorrow's analytics for finance is here today. We call it, Extreme Finance.  Finance Working Faster Why work faster?  Because fast is cool. Just ask the people behind the Bloodhound Project (pictured above), which is working with Oracle Academy to make a supersonic car it aims to smash the 1,000 mph world land speed record. In the same ways that cars have gotten faster over the years, it's common knowledge that data is growing and consequently any processing, preparation, and analytics can be sped up.  Often the increase in processing time required has out grown the limited windows available.  After all, everything may be growing, but the one thing that remains unyielding is the number of hours in the day.   Cloud provides elastic scaling meaning that at busy times, and finance departments experience these regularly throughout the year, dynamically adds more CPUs and resources to ensure no loss in query performance.  Software-based robots (robotic process automation or RPA) are processes that take on the tedious and repetitive low value tasks humans should no longer be doing in this day and age.   New autonomous analytic methods provide the capability to write textual narratives for management reports that ensure all salient points are accurately detailed.  Gone are the days of arriving at the numbers, to then tediously copy and paste them into a word processor and spend the next few days writing the narrative.  A process that if not caffeine powered could easily result in a report that, by human error missed some key points in the data.  And, what happens if you find you started with incomplete numbers and had to run them.  Do you start your narrative again?  Autonomous analytics would update that narrative real-time.  Additionally, leaps forward in analytics querying methods now means interrogating the financial systems is as easy as using your voice.  Think Siri or Alexa and using that type of approach to be asking sophisticated queries about your past or future business performance.  Executives are spending much less time at their desks and consequently are not consuming information via dashboards or reports.  But, they have their phones, and they can ask voice queries very easily.  This example of technology that places the information back into the hands of the personal who needs it, when they need it, speeds up business operations because decisions can happen in place without delay.   Everyone loves their weekends and getting home at a reasonable hour during the week.  Why should periodic busy periods change that?  Cloud and autonomous analytics mean that you never need to wait for slow queries or data processing.  It is no longer an accepted norm that finance is dependent on IT for information, or slave to slow hardware.  Upgrade to an analytic platform that works Faster, and enjoy your weekends. In Part 3 of this blog, I will discuss more about how machine learning and autonomous analytics can create "richer" analytics built using all relevant information from anywhere. To learn more about how Oracle Analytics Cloud can create new opportunities, visit oracle.com/analytics

In Part 1 of this blog series, I described how FP&A could work smarter by leveraging modern analytics.  In this piece I'll describe how FP&A can work faster with the same human resources and skills...

Analytics Cloud

Extreme Finance: Powering Finance to Work Smarter

Finance Planning and Analysis (FP&A) is always looking for greater insight and do more with fewer resources.  But, how can this be achieved with limited bandwidth?  Cloud infrastructure and software together with machine learning make it possible to bring innovation to all finance professionals.  Existing financial business processes may appear to be working for the average company, but is the stress associated with obtaining the data and building meaningful results actually a 'working' process?  Technology has come a long way.  While ordinary cars are still able to get you from A to B, the modern world has provided self-driving autonomous cars that give you back your time in transit.  Why would finance processes be any different?  This innovative approach to business would enable finance to work 'Smarter' by leveraging embedded machine learning to create trustworthy predictions. They can work 'Faster' and do more analysis and scenarios in the same amount of time. And they achieve 'Richer' insight by having all information accessible without help from IT.  Tomorrow's analytics for finance is here today. We call it, Extreme Finance.  I'm going to dive into each of those three areas, Smarter, Faster, and Richer in a 3-part blog, starting with this one. Finance Working Smarter What is smarter?  Smarter is transforming from a knowing culture to a learning culture.  Finance traditionally is a knowing culture, keeping records, transactions, and information for years for a variety of mandated reasons.  Taking note of lessons learnt throughout your history ultimately help you prepare for future challenges.  In business, using that wealth of historical information blended with current economic factors in predictive models will provide much better insight into your future than any 'experienced' gut feel ever could.  Trustworthiness of predictions is one of CFO's top concerns.  Disconnected processes, multiple tools for data extraction and analytics introduces human error that contributes heavily to that lack of confidence.  Machine learning is the key to finance working smarter.  Let the machine consider all the information available, a process no humans can possible undertake, and plot out a view of your future performance.  Machine learning will highlight the truths in the data and not be influenced by analyst background, experience or bias.   Adopting emerging technologies is on the Top 5 priorities for the CFO in 2018, according to Oracle Group vice president, Steve Cox, because new technologies like machine learning enable finance departments to process more information, yet produce more accurate predictions with the same or less human resources.  Saved human energy can instead be directed to more strategic tasks rather than data processing. Using new machine learning techniques are not as complex as they were a few years ago.  Data scientists and specialist tools are not required to gain benefits from machine learning.  Modern analytic tools incorporate machine learning either as autonomous capabilities or in such a way that any user can apply them.  In Part 2 of this blog, learn more about how machine learning and autonomous analytics can help FP&A work FASTER with the same skills and resources already in place, and still reduce costs. To learn more about how Oracle Analytics Cloud can create new opportunities, visit oracle.com/analytics

Finance Planning and Analysis (FP&A) is always looking for greater insight and do more with fewer resources.  But, how can this be achieved with limited bandwidth?  Cloud infrastructure and software...

Analytics Cloud

Get Faster Insight With Autonomous Analytics

Running a small business is hard. In fact, most small companies never make it past year five. It takes a scrappy group of individuals to make it last. Long hours. Hard work. Every dollar is earned. Typically, most small organizations require employees to wear several hats. The accounting person might also handle operations. The CEO could be the sales lead. This multi-responsibility environment can be exhausting. There simply is too much work and not enough hours in the day. This kind of situation demands efficiency. Working smarter, not harder, is where technology comes in. Using Oracle Autonomous Analytics Cloud, businesses are decreasing the amount of time spent on routine tasks and making room for the larger, more important strategic activities. For example, Oracle customers are automating sales reports that provide insights and bring clarity to every-day situations, quickly. Is Jacob a better sales rep than Aaron? How many sales did each have last month? Last quarter? Did one sell more high-priced items than the other? Is Carlos a better customer than Steven? Which store do each of them prefer? What kinds of products are they buying most recently? These questions are important to answer before making high-priority decisions such as potential hiring, opening new stores, and which products to order from suppliers. More to the point, getting these insights quickly and easily is critical to moving on to bigger, better things. Oracle Autonomous Analytics Cloud allows customers to capture their data easily and dynamically pivot through the numbers to gain deeper understandings quickly. Let's look at a fictional chain of fly fishing retail stores. Perhaps the owners of the stores want to understand who their best sales people are. But how they define "best" is important. Is it the number of sales that matter? Is it the amount of revenue? Does the store location influence the numbers? How about the month or type of product? A general question of "who is the best" can be quite complex and could lead to other findings such as training needs, movement of sales people, and predict which months specific stores would expect to see an increase in sales. For the visualizations below I will be using this spreadsheet. After uploading any spreadsheet or creating a new data connection, and starting a new project, you see this: On the left-hand side of the screen, you see the columns of data from the spreadsheet. Click and drag the data you want to look at into the main section of the project. This is where the fun starts. Now that I have an unsorted, generic data set, I can quickly manipulate the output by filtering, formatting, adding data to consider, etc. First off, simply clicking "save" in the top right allows me to name my project. Clicking the drop-down box to change from a pivot table allows me to create a bar chart. Right clicking on the graph allows me to sort high-to-low. Dragging "item" into the graph creates this: That's a good looking sorted chart and answers the general question of "who's the best." In a few short actions, I created, named, sorted, and saved a detailed visualization. When I add filtering to the mix, I can see more useful information. Dragging "item" into the filter section allows me to click on only the items I want to focus on. Let's say I only want to only see the big-ticket items such as rods and reels. I simply click on those products in the filter, and the graph gets updated instantly. Notice there are some changes in the order of "who's the best." Not too different as focusing on the larger products is similar to looking at the whole picture. The point is...it was a few clicks to dig a bit deeper. This is a very fast way to see things from multiple perspectives. And that's the goal—saving time. For instance, if I decide to focus on only flies, tippet, and leader, I see this: That really changes things. I also added a background graphic that spiced up the visualization a bit. Who says you can't be creative and have fun while saving time too? As you can see, a simple question of "who's the best salesperson" can be answered a few different ways. These findings were obtained in a very easy, quick manner. And this is the point. Small businesses fight for their existence every day. Spending long hours sorting through spreadsheets doesn't bring in revenue. Oracle Autonomous Analytics Cloud is helping customers get back to the real work. I hope you found this blog informative and that you make use of the spreadsheet I attached above. To see how easy it is to use data visualization, here is a link to download the tool. Better yet... sign-up for a trial version of Oracle Autonomous Analytics Cloud! Also... be sure to visit our website and take one of our quick tours or try the data visualization simulator. You can also find more resources on our customized content page. Also, for current Oracle Analytics Cloud customers using Data Visualization, be sure to visit the Oracle Analytics Library and download sample files to take your analytics to a new level. And finally...to see the bigger picture, and learn more about Oracle autonomous cloud services please visit the Oracle Autonomous Cloud website.    

Running a small business is hard. In fact, most small companies never make it past year five. It takes a scrappy group of individuals to make it last. Long hours. Hard work. Every dollar is earned....

Analytics Cloud

Experience Oracle Autonomous Analytics Cloud

Autonomous systems are here creating opportunities for business transformation, but they are also adding analytics to become more personal and proactive—more human if you will.   Unlike first-generation semantic BI models and newfangled self-service analytics on the desktop, we want people to be focused on using their expertise to solve real business problems, not having to figure out how to ask the right question to get a decent answer out of the data.  It's in this spirit of transformation that we announce the availability of Oracle Autonomous Analytics Cloud. With built-in advanced AI (artificial intelligence) and machine learning algorithms, the platform as a service (PaaS) automates and eliminates key tasks that would previously have been carried out by IT managers or data scientists. The software is designed to lower cost, reduce risk, accelerate innovation, and generate predictive insights. Oracle Autonomous Analytics Cloud breaks down barriers between people, places, data and systems, fundamentally changing the way people analyze, understand, and act on information. With Oracle Autonomous Analytics Cloud, you can: •        Empower more people to uncover more insights more quickly via proactive, personalized self-service analytics on any data •        Reveal hidden patterns and performance drivers through predictive insights and automatic natural-language explanations powered by machine learning •        Accelerate time to value by providing a comprehensive, intelligent cloud analytics platform that automates operations and reduces overhead, lowering the total cost of ownership "This is the latest in a series of steps from Oracle to incorporate industry-first autonomous capabilities into our PaaS portfolio to help customers take advantage of automation to build now and innovate for the future," says Amit Zavery, executive vice president of development, Oracle Cloud Platform. The announcement also includes the availability of Oracle Autonomous Data Integration Cloud Platform, Oracle Autonomous Integration Cloud, and Oracle Autonomous Visual Builder Cloud. More Human Overall, Oracle's autonomous vision boasts "no human labor," "no human error," and "no manual performance tuning," but even without people involved, there is a great deal of humanity woven into it. Self-driving cars and the chatty home assistants aside, autonomous systems are now being used to find efficiencies to reduce waste, monitor conditions to improve worker safety, and eliminate defects to ensure quality. Oracle Autonomous Analytics Cloud can tackle these issues as well, but it also allows for interactions both personally and professionally, which is changing how people work—for example, using voice as the interface for analytics.  Why type when you can just ask a question the way you'd ask another person in conversation?  Phones and other mobile devices also make it easier to make analytics proactive, so as you get to your customer meeting, for example, you get insights about how your customer is doing, what might be affecting their business, and have that all available to you, no pre-planning required.  Additionally, the growth in cloud increases expectations for simplicity and ease-of-use, or really ease of ownership across the whole experience, from buying subscriptions to rolling out new capabilities to expanding deployments and embedding analytics – you name it, customers expect more and more of a push-button approach. Three Steps to OAAC Oracle Autonomous Analytics Cloud builds on the previous Oracle Autonomous Data Warehouse release in three areas: Automated insights, visualization, and narration Automated data discovery and data preparation Proactive and personalized insights Automated insights mean that you start with auto-created visuals, rather than a blank canvas. These insights reveal patterns in the data that may otherwise never have been found, while embedded natural language generation helps people to understand insights using text and or audio. Automated data discovery means you can search to find data sources, and ask data questions just like a data scientist, with recommendations to guide your way. There are instant recommendations for data preparation, cleansing, standardization. Proactive and personalized insights bring back decisions based on context: time, location, caller, and other identifiers. Voice to query based on natural language processing brings back multiple possible answers ranked based on self-learning. The software understands the business and company specific vocabulary that you can share with one click. Interested in automating your data analytics? Try Oracle Autonomous Cloud Platform services, including Oracle Autonomous Data Warehouse Cloud by signing up for a Free Oracle Cloud Trial via cloud.oracle.com/tryit.

Autonomous systems are here creating opportunities for business transformation, but they are also adding analytics to become more personal and proactive—more human if you will.   Unlike...

Analytics Cloud

How Analytics Shapes a New Role for Finance

Did you ever stand in a store check-out line, thinking that it would move quickly, only to find that it’s moving slower than the line you chose not to stand in?  How much time would you have saved if you had more information or analysis about the people in the lines, the items in the carts, the cashiers, the equipment and how they all fit together? Those are the kind of questions we must answer when faced with making a multitude of decisions throughout our day, from the mundane to the complex, in our personal lives and our business lives. Recently I drove into a parking lot, to find that at every turn and every floor, there were digital displays letting me know exactly how many parking spaces were available. It eliminated the guessing game, helped me save time, and it made me smile. Making finance execs smile is another matter, altogether.  Mitch Campbell, Senior Director of Product Marketing for Oracle Analytics Cloud recently discussed this topic, and the answer is analytics!  In the podcast: Management Reporting and Analytics in Finance he emphasized the importance finance executives have in an organization and the shift in focus from simply supplying financial reports and forecasts to now making more informative recommendations that affect the future financial health of an organization. Finance executives, CEO’s and other lines of business increasingly need more holistic financial information and analysis to make informed decisions to grow their business. CEO’s are increasingly looking at their finance executives to not just look at cost cutting but to identify new growth opportunities and oversee investment strategies that tie into growth. They are looking for them to go beyond the traditional finance function of “how are we doing” and instead provide real world “where are we going” and “what if we changed x?” comparisons. Essentially finance execs need to pull analysis from financial reporting, operational reporting, real-time reporting and wrap that all together, into a management reporting analysis. Management Reporting has been around a long time, but the management reporting analytics of today impacts decisions across multiple functional areas, various data sources, and provides the forward-looking scenarios that businesses need for growth. It delves into various levels of detail and involves forecasts often done outside of planning and budgeting apps. Would I pick a “stand in line” scenario A, scenario B, or scenario C if I had access to an analytics feed into my own daily life management reporting app?  Maybe, but it sure helps finance executives smile when they can offer scenarios that provide a holistic view of the business and offer recommendations that position their organization at the front of the line for their customers. Click on the photo below and take a listen to our latest Podcast Episode: Management Reporting and Analytics in Finance

Did you ever stand in a store check-out line, thinking that it would move quickly, only to find that it’s moving slower than the line you chose not to stand in?  How much time would you have saved if...

Analytics Cloud

Test Drive Oracle Analytics Cloud Near You

Recently, I attended an Oracle 'Test Drive' workshop session that offered customers a hands-on experience using Oracle Analytics Cloud. This event was held in Houston and allowed more than 45 customers to gain a better understanding of the power of Oracle Analytics Cloud and the business benefits it offers. A key Oracle partner, Perficient, led several labs which covered several modules including predictive analytics, machine learning, and visual analytics. These events are free of charge and include an overview of dashboards and pre-built reports; data visualization, machine learning and data modeling using Oracle Essbase. In Houston, it was interesting to hear the partner's perspective on how they are seeing analytics change their customer's businesses. Capturing more data, speeding up analytical efforts, seeing trends, finding more opportunities, driving a deeper understanding of the business, and predicting what the future might hold were some key benefits the partner listed. It was also intriguing to watch customers become more comfortable with the tools and see the 'light bulb' turn on. Several customers from numerous industries including oil and gas, higher education, aerospace, energy, retail, and healthcare commented on specific areas of their business that could use these kinds of analytics tools to gain a more complete picture. This is the value of Oracle Analytics Cloud. A comprehensive enterprise-wide solution that can handle data from anywhere in your organization, in any format, and put it to work by helping you find new perspectives, relationships and dependencies that impact the bottom line. These discoveries are no longer 'nice-to-haves'. They are required in this age of cloud analytics, machine learning and adaptive intelligence. Companies that wish to remain significant in their respective markets must use analytics to thrive and grow. That's also why Oracle topped "The Forrester Wave: Enterprise BI Platforms with Majority Cloud Deployments," where it outperformed Microsoft, Amazon Web Services (AWS), and Salesforce. The remaining sessions include Toronto (5/1), NYC (5/2), Philadelphia (5/4), Minneapolis (5/8), Cleveland (5/10), and Atlanta (5/11). Click on the city and date for more information. If you are planning to go, there will be Wi-Fi but you must bring your own laptop. This will be an extremely interactive event, with lunch provided and potentially a social hour to follow. You’ll also have the opportunity to network with other companies, users throughout the day. Below are a few pictures of the event. We hope you get the chance to attend one of these sessions.  

Recently, I attended an Oracle 'Test Drive' workshop session that offered customers a hands-on experience using Oracle Analytics Cloud. This event was held in Houston and allowed more than...

Analytics Cloud

Top 10 Reasons to Move to Oracle Analytics Cloud

By Lucie Trepanier and Mitch Campbell Are you a list type of person? If you're reading this, I bet you probably are.  Perhaps you remember The Dave Letterman Show and his Top 10 lists?  They certainly were memorable!  And so, we thought it might be a fun idea to use that concept with a current topic... moving to the cloud.  It's how smart companies are making the transition to modernize and improve and save money all at once. Oracle Analytics Cloud is a comprehensive portfolio of analytics offerings built for the cloud; deployed in the cloud; and enabling data analysis for the cloud or on-premises. From self-service visualization and data preparation to enterprise reporting and advanced analytics to dynamic user-driven what-if modeling to self-learning mobile analytics that provides proactive insights, Oracle Analytics Cloud provides everything you need to ask any question of any data, on any device so you can get the value you expect from analytics. At the end of this blog, you will find a link to a cool article on why our brains love lists.  It boils down to how we like to see information spatially organized.  This gives us a sweet tie-in to the Oracle Analytics Cloud because it's all about organizing information.  So, without further ado, we give you the Top Ten Reasons to move to Oracle Analytics Cloud. 10.  Your future is autonomous If you look at most analytic platforms today, they are almost exclusively "human to machine," meaning discovery and data preparation as well as analysis and prediction is done by human interaction to the machine to produce results and insights.  Autonomous systems will improve analytics in the cloud by flipping that script and providing "machine to human" improvements Machine learning helps find the correct data for us, auto refines and enriches the data, explain and contextually improve the quality of the insights, and provide self-learning insights that improve predictions the more data they use.  Check out our easy, fast, and elastic autonomous data warehouse cloud: 9.  Tell your best story with Natural Language Insights Oracle Analytics Cloud provides stunning visualizations and interactive presentations, sourced from any relevant data.  We blend automatic chart creation based on intelligent data services with singe-click trending and forecasting, drag-and-drop clustering, and outliers.  End users can create interactive stories directly from their analysis.  It's easy to build a story around the rich palette of hundreds of visualizations, along with natural language creation to automate and contextually describe the visuals. 8.   Connect to ALL of your data Oracle has its eye on the future with its cloud analytics by allowing you to connect to all of your data sources.  Relational?  Check.  Multi-dimensional?  Check.  data warehouse?  Big data sources with Hadoop?  ERP?  CRM?  Check, check, check, check.   No need for separate tools for different types of data any longer.  The Oracle Analytics Cloud has the connections you need to bring together all of your relevant data and deliver the most comprehensive Analytics offering on the market. 7.   Model any scenario Only Oracle brings Oracle Essbase, the number-one multi-dimensional database used by thousands of loyal customers, into the Cloud.  Essbase allows you to quickly model scenarios, leverage strong "what if" iterations to determine the best possible outcome for your company.  Now with the cloud, we have improved many facets of Essbase to make it easy and fast to create and pilot new ideas. Build cubes in minutes from data flows or directly from Excel with the new cube designer, available in the enhanced Smart View Excel interface.  We should also mention that only Cloud Essbase has the new hybrid Essbase engine, combining the "best of both worlds" for handling huge sparse data sets (Aggregate storage) and strong calculations across different dimensions of data (block storage) into a single Essbase cube.  Click here for detail: 6.  Talk to your data What if we lived in a world where you could ask a device questions and it would respond with the answers?  Wait... we do!  As a matter of fact, voice to text also works with Oracle Analytics Cloud.  Simply speak your question and relevant visualizations appear.  That's right, no coding required.  Want to have that resulting visual card appear refreshed every morning?  We do that.  Want it to appear each time you land in a city with one of your offices or products?  We do that too!  Oracle Day by Day is exactly the mobile experience you are looking for. 5.   Enrich any data to fuel your stories Perhaps one of the most important features needed in the cloud is allowing for "self-service".  The ability to create sharable and traceable business data transformations by yourself... easily.  This avoids Excel clutter and empowers analysts to enhance data with no coding skills required Common needs include grouping values, joining data sets, sub-select rows/columns, aggregations, calculated fields, etc.  Oracle Analytics Cloud Intuitively and in-context embeds this power into the Analyst and Business User experience, yet allows it to be customizable with customer reference knowledge. 4.  You keep your ability to do pixel perfect reports and printing With Oracle Analytics Cloud Enterprise Edition, now you can create pixel perfect report and deliver to a variety of destinations such as email, printer, fax, file server, Oracle WebCenter content, and Oracle Content & Experience Cloud. "Pixel perfect" means highly formatted business documents such as Invoices, Purchase orders, Dunning letters, Marketing collateral, Financial statements, Government forms, Operational reports, Management reports, retail reports, shipping Labels with barcodes, and more.  You can connect to a variety of data sources, schedule your report to run once or as a recurring job; and even burst documents to render in multiple formats and be delivered to multiple destinations. 3.  Machine Learning and Artificial Intelligence make things better So far, we have discussed how you can quickly create your own visualizations, use data preparation to auto refine and enrich your data, and use search or natural voice controls to ASK and automatically create your visuals.  But what if you don't know what's in the data?  What to ask or where to start?  That's where machine learning can bring true value in relieving "Blank Canvas syndrome".  Oracle Analytics cloud introduces the ability to "Explain" an attribute in context of the other attributes and metrics in the dataset!  Uncover what drives your results, be transparent with your findings, and analyze key segments of customer behavior.  The difficult task of determining where to start... is now over: 2.  Easier than ever to buy and get started Oracle offers Universal Credits, the industry's most flexible buying and consumption model for cloud services. With Universal Credits, customers have one simple contract that provides unlimited access to all current and future Oracle PaaS and IaaS services, spanning Oracle Cloud and Oracle Cloud at Customer.  Customers gain on-demand access to all services plus the benefit of the lower cost of pre-paid services. Additionally, they have the flexibility to upgrade, expand or move services across datacenters based on their requirements. With Universal Credits, customers gain the ability to switch the PaaS or IaaS services they are using without having to notify Oracle. Customers also benefit from using new services with their existing set of cloud credits when made available. Try it now 1.  And... the number one reason:  Do it all in the best Cloud, the Oracle Analytics Cloud. To cap it off, Oracle topped "The Forrester Wave: Enterprise BI Platforms with Majority Cloud Deployments, Q3 2017" where it outperformed Microsoft, Amazon Web Services (AWS), and Salesforce.  You're not going to go wrong, and regardless of where you start and where you want to go, you'll never have to make that decision ever again. Thanks for playing along with us on our serious list, and if you can't wait any longer, here is the fun list on why we love lists.  

By Lucie Trepanier and Mitch Campbell Are you a list type of person? If you're reading this, I bet you probably are.  Perhaps you remember The Dave Letterman Show and his Top 10 lists?  They...

Data Visualization

2018 NBA Playoffs as Seen Through Oracle Analytics Cloud

The 2018 NBA playoffs have arrived. Did your favorite team make the cut? If not, maybe you can still enjoy the action by picking a couple of other teams to root for! I have spent part of this workday searching for NBA team stats so that I could create a few data visualizations I will share below. Before we dive into those, I would like to for you to think about how data visualization could positively impact your day-to-day work. Do you create presentations that explain detailed metrics? Do you need to share analytical insights with peers across your organization? Are you capturing all of the business data from every corner of your enterprise? These are just a few considerations to ponder when dealing with data visualizations. What does this have to do with the NBA playoffs? Data. Sports, like many other aspects of life rely on data. Did you see the score of the game last night? How many turnovers did that guy have? What’s their record on the road? All of these questions are answered by comparing the data. So…let’s check out the first round match-ups! In the Eastern Conference, Toronto (1) takes on Washington (8), Boston (2) plays Milwaukee (7),  Philadelphia (3) will challenge Miami (6), and Cleveland (4) matches-up with Indiana (5). Let’s look at these contests. As you can clearly see, Toronto has considerably more wins in every category except in the head-to-head match-up where they are tied with Washington. Boston also has more wins in every category than their opponent, Milwaukee. However, the home records are very similar and they split the direct match-ups during the season. Philadelphia versus Miami is following a familiar pattern with the 76ers besting the Heat. And then we get to the Cleveland and Indiana series where both teams are very similar except in direct competition where the Pacers have won three out of four. Now let’s look at the Western Conference where Houston (1) faces Minnesota (8), Golden State (2) will play San Antonio (7), Portland (3) matches-up with New Orleans (6), and Oklahoma City (4) takes-on Utah (5). The Houston and Minnesota match-up shouldn’t be much of one as the Timberwolves appear to be very large underdogs. The Golden State versus San Antonio series is interesting because San Antonio has such a good record at home but the Warriors have outplayed the Spurs in the head-to-head games. Portland versus New Orleans should be a good match-up as both teams appear to be similar with regard to wins. The same can be said for the Oklahoma City and Utah series but the Thunder lead three to one when playing the Jazz. One of the most useful aspects of the data visualization feature in Oracle Analytics Cloud is the dynamic capabilities of the tool and the flexible ways you can view data. With just a few clicks, I can dramatically change the way I am seeing my data. This often leans to new, better, deeper undertakings.  Below are a few examples. As I alluded to earlier, I used the data visualization functionality of Oracle Analytics Cloud to create all of these visualizations. It was a simple import from this spreadsheet and I was on my way! I enjoy working with numbers and feel they are best understood when represented visually. This is especially true when you want to look at your situation from numerous dynamic viewpoints and uncover hidden knowledge. I hope you found this blog informative and that you make use of the spreadsheet I attached above for your own NBA Playoff research. Better yet...sign-up for a trial version of Oracle Analytics Cloud and create your own visualizations! Also... be sure to visit our website and take one of our quick tours or try the data visualization simulator. You can also find more resources on our customized content page. Also, for current Oracle Analytics Cloud customers using Data Visualization, be sure to visit the Oracle Analytics Library and download sample files to take your analytics to a new level.

The 2018 NBA playoffs have arrived. Did your favorite team make the cut? If not, maybe you can still enjoy the action by picking a couple of other teams to root for! I have spent part of this workday...

Essbase Cloud

What Is Agile Scenario Modeling and Why Does It Matter?

What does the future world look like? How will our value change over time? What's the risk to our organization? If you're a finance leader, these are a small sample of the questions that you wrestle with. Many finance professionals would say that they confront these questions through budgeting, planning, and forecasting. Yet, Rich Clayton, vice president of product strategy at Oracle, argues that these processes aren't enough to inform a company about what may happen in the future. Scenario modeling, on the other hand, helps companies map out multiple views of the future, over different time periods, with different assumptions. Clayton explained the difference between these two processes and gave some helpful tips about how to get started with scenario modeling in a recent webcast with FEI (Financial Executives International), called Scenarios Made Simple, which you can review on demand. Budgeting, planning, and forecasting are processes designed to articulate how a particular strategy will unfold in the future and communicate key assumptions to business leaders throughout the organization, Clayton explained.  It's the plan of record against which the company makes decisions. The problem with these processes is they look into the future based on a single set of assumptions. It's not that these processes don't serve a purpose. It's that they're not enough. Scenarios, on the other hand, Clayton said, map out multiple views of the future, over different time periods, with different assumptions. But more than that, the process of building different scenarios is a tool for confronting the flaws in how an organization thinks about the future and encourages discussion and debate on any number of situations—from large, black-swan events to volatility in the market to major disruptions and changes in the competition. Scenarios Done Well If you want to see an example of scenario modeling done well, Clayton points to Royal Dutch Shell, the global oil and gas company that has been doing scenario modeling since the early 1970s. Back then it was facing a changing regulatory environment and needed to imagine a future where they could successfully navigate those changes. Today, Shell's scenario-development practice is an institutional competency that they've used to investigate everything from improving the response to AIDS in Africa to how natural gas could become a mainstream energy source in China. As you can imagine, Clayton noted, these aren't easy problems to confront. But then again, filling the gap between what's happening now and what will have happened in the future is never an easy task—not for emerging energy markets and certainly not for your business. That kind of thinking requires decoupling the past from the future, which seems deceptively straightforward. But as Shell's resources on scenario modeling point out, the same area of the brain is responsible for both picturing the future and remembering the past. Conflating the past with the future is part of our biology. "That's where the scenario modeling development process becomes useful," Clayton said. The Journey Is the Reward During his presentation, Clayton pointed to this quote from well-known strategic management, decision-making, and leadership expert Paul J.H. Schoemaker: "Scenario planning is as much art as science, and prone to a variety of traps (both in process and content)." Clayton explained that these traps can be everything from looking at the wrong drivers to making a scenario so detailed (read complicated) that decision makers don't know what to glean from the findings. That why, Clayton advised, it's important not just to do scenario modeling, but to go through the process for doing agile scenario modeling. An agile process is something that's fast to produce and adjust, takes into account all the dimensions of financial integrity (whether that's cost to capital or foreign exchange rates), uses carefully selected drivers, goes through a clear feedback loop, and makes use of visuals so the findings are easy to grasp. Ultimately, scenario modeling can be difficult, but the process can change your company from an organization that's often surprised by the future to one that's prepared for it. And, as Clayton covers in detail in his webcast, it can also be made easier with the right competencies and tools. Register to review the on-demand version of Clayton's FEI webcast Scenarios Made Simple to go into much more detail about how to create an effective scenario-modeling competency within your company. And click here to see more on how Oracle can help your company outperform your competitors by providing a strategic modeling platform called Essbase Cloud Service for all your scenario analysis and performance measurement needs.

What does the future world look like? How will our value change over time? What's the risk to our organization? If you're a finance leader, these are a small sample of the questions that you...

Data Visualization

Masters Perspectives: More Angles Equals Better Analysis

Last year, I had the pleasure of attending a Masters Tournament practice round at Augusta National Golf Club. As soon as I stepped on those sacred grounds, I thought for sure the grass was fake. It wasn't. The grass was perfect. Birds were singing. The breeze was light. The sun was hidden by the rain clouds. It was perfection. Sergio Garcia ended up winning last year—his first major. Those who follow golf knew he would win a major someday. But having the Masters be your first... that's special. Will another first time major winner be the champ again this year? Will it be a seasoned veteran? A fresh face? No one knows for sure but it sure is fun to speculate. Being a numbers guy, I tend to look at stats when anticipating the next big sporting event. For this year's Masters, I've decided to look at this from a few perspectives. My one-stop-shop for PGA Tour metrics is the statistics page on PGA.com. It's a fantastically complete and detailed resource. Let's start with some background. Several years ago, Professor Mark Broadie from Columbia Business School developed a concept called "strokes gained" which is the number of strokes a player was better or worse than all of the competitors on the same course and event. This metric is expressed as an average and is applied to several aspects of the game. With the numbers from the PGA Tour website (through the Dell World Golf Championship Match Play event), I put together this spreadsheet. Next, I simply uploaded the spreadsheet into Oracle's Data Visualization Desktop and began creating visualizations. If you download the spreadsheet, you will see I added some of my own calculations. In particular, I added a "weighted" column that adds all of the various strokes gained numbers with the "approach the green", "around the green", and "putting" numbers counting more than the others. Below are the visualizations I put together for each of the strokes gained metrics. Tee to Green – the per round average of the number of strokes the player was better or worse than his competitor's average on the same course and event minus the players strokes gained putting value. Top ten players (1.30 strokes minimum) Off the Tee – the number of strokes a player takes from a specific distance off the tee on Par 4 and par 5's is measured against a statistical baseline to determine the player's strokes gained or lost off the tee on a hole. Top ten players (.695 strokes minimum) Approach the Green – the number of approach the green strokes a player takes from specific locations and distances are measured against a statistical baseline to determine the player's strokes gained or lost on a hole. Top ten players (.675 strokes minimum) Around the Green – the number of around the green strokes a player takes from specific locations and distances are measured against a statistical baseline to determine the player's strokes gained or lost on a hole. Top ten players (.375 strokes minimum) Putting – the number of putts a player takes from a specific distance is measured against a statistical baseline to determine the player's strokes gained or lost on a hole. Top ten players (.667 strokes minimum) Total – the per round average of the number of strokes the player was better or worse than his competitor's average on the same course and event. Top ten players (1.65 strokes minimum) Total – the per round average of the number of strokes the player was better or worse than his competitor's average on the same course and event. Top nine players (1.65 strokes minimum) As a reminder, the numbers I used are through the Dell World Golf Championship Match Play event. Looking for answers from multiple perspectives gives you a more comprehensive understanding of the situation. More angles allow for better analysis. For this particular event, I am obviously leaning on metrics that relate to play on and around the greens. At Augusta National, this is key. For any course it is very important but at the Masters, there is extra focus on the strokes taken in this area. I enjoy working with numbers and feel they are best understood when represented visually. This is especially true when you want to look at your situation from numerous perspectives. I hope you found this blog informative and that you make use of the spreadsheet I attached above for your own Masters research. Better yet... sign-up for a trial version of Oracle DV and create your own visualizations! Also... be sure to visit our website and take one our Quick Tours and/or Simulator. You can also find more resources on our Customized Content page. Also, for current Oracle Data Visualization customers, be sure to visit the Oracle Analytics Library and download sample files to take your analytics to a new level.

Last year, I had the pleasure of attending a Masters Tournament practice round at Augusta National Golf Club. As soon as I stepped on those sacred grounds, I thought for sure the grass was fake....

Analytics Cloud

Analytics' Role in Autonomous Data Warehouse Cloud

Oracle Autonomous Data Warehouse Cloud is now available. This is the first official service launched by Oracle under the "Autonomous" category that we announced earlier this year. Oracle Executive Chairman and CTO, Larry Ellison took to the stage at Oracle headquarters to make the announcement. The video below outlines the many compelling features and benefits for businesses looking to take advantage of automation. Built on Oracle Database 18c, Autonomous Data Warehouse Cloud includes more than 100 features that require zero IT administration on the customer’s part. While Oracle will be releasing Autonomous Analytics later this year, what makes Autonomous Data Warehouse Cloud significant is that it already has so many data analytics features included in this initial release. The software uses machine learning to deliver a self-managing, self-securing, self-repairing database over the Oracle Cloud. The video below includes a full demonstration of those features including ones related to analytics. Of those hundreds of features, the most significant is a free (as in "free beer") version of Data Visualization Desktop. Data visualization allows you to spot patterns, trends, and correlations that otherwise might go unnoticed in traditional reports, tables, or spreadsheets. Oracle Data Visualization lets you analyze data from multiple sources and easily create intelligent integrations including broadcasting to mobile devices. One feature that is sure to gain fans is the ability to process these analytic queries as fast as your business needs them. The Autonomous Data Warehouse Cloud allows you to scale your analytics computing speeds according to the number of CPUs you assign to it. As seen below, a slider taps into a REST instance that winds up more compute power as needed and then scales back either after the task is complete or during specific hours. This saves on both time and money since you only pay for the CPUs you use. "In a data-driven organization, the best tools for measuring the performance are business intelligence (BI) and analytics engines, which require data," says Alan Zeichick, principal analyst at Camden Associates. "And that explains why data warehouses continue to play such a crucial role in business. Data warehouses often provide the source of that data, by rolling up and summarizing key information from a variety of sources." If you want to see the Autonomous Data Warehouse in action, feel free to download the free trial. For those who are interested in the costs involved, Oracle has provided a total cost of ownership (TCO) calculator. We're also taking Oracle Autonomous Database Cloud on a World Tour where you can learn more and test drive the new service at any of events taking place in more than 30 cities worldwide.

Oracle Autonomous Data Warehouse Cloud is now available. This is the first official service launched by Oracle under the "Autonomous" category that we announced earlier this year. Oracle Executive...

Analytics Cloud

Ireland's Postal Service Delivers with Oracle Analytics

Mail delivery has come a long way since the couriers of ancient Persia, the riders of the Pony Express, or even FedEx drones. The romantic ideal that "neither snow nor rain nor heat nor gloom of night stays these couriers from the swift completion of their appointed rounds," is getting a boost these days from the insights gleaned from data analytics. Whether it's the marketing department determining the return on investment from a campaign, to the HR department capturing data on employee talent, to the IT department looking for efficiencies or compliance, every organization has data they need to gather, analyze, and interpret. Such was the case for An Post, Ireland’s main postal provider and largest retail network. The national carrier serves 2.1 million homes through 4,000 delivery routes where it transports 2.8 million pieces of mail per day. That system also includes overnight data load processes, continuous monitoring, and data quality controls. With the help of Oracle Analytics Cloud, An Post takes in data from various sources including finance, manpower costs, mails operations and now retail. The analysis is provided to customers through interactive self-service dashboards as well as to An Post staff in mails processing plants nationwide. In the following video, Christine Curley, a Business Intelligence Solution Architect with An Post describes how the company uses Oracle Analytics Cloud to help drive new innovations in just over a month. An Post's backend systems are deployed to almost 600 interactive users and can handle 87 million events tracked for barcoded products. By using Oracle Analytics Cloud, the carrier can process up to 623,000 mail pieces per hour. In the video below, Curley explains how An Post's hybrid analytics enabled a seamless transition to cloud and accelerated customer access to data and insights. Finally, with Oracle Analytics Cloud helping power their processes for governance and change management, customers can do broader, faster analysis to help create the best experience. Curley adds that adding cloud-based analytics, An Post now can quickly respond to new business opportunities and is well positioned for future opportunities and challenges.  An Post is just one of the Oracle's partners using Oracle Analytics to support their customers' digital transformations and cloud-based endeavors. If you like what you see, take one of our Quick Tours or Simulations to learn more about Oracle Analytics. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Oracle Analytics website.

Mail delivery has come a long way since the couriers of ancient Persia, the riders of the Pony Express, or even FedEx drones. The romantic ideal that "neither snow nor rain nor heat nor gloom of night...

Analytics Cloud

Finding Human Insights with Autonomous Analytics

What if you wanted to transform data into actionable insights that impact people's lives in new and extraordinary ways? Would you do this with a business intelligence suite on premises or with a non-scalable self-service software? Probably not. Likely, you would be looking for a cloud-based platform that increases your agility while creating a high business value. You would, therefore, be looking for services that tap into a broad transformation of data analytics to reach real people. That's the message coming from Waqar Hasan, senior vice president, Oracle Big Data Analytics. In front of a packed crowd at Oracle headquarters, Hasan put a very real face on Oracle Cloud Platform Autonomous Services by explaining how Oracle is using its own tools to help government agencies fight against the current opioid epidemic. "This is a crisis that affects the lives of people in cities and towns, across all income levels and backgrounds," Hasan said. Photo courtesy of Rich Clayton The opioid epidemic has been wreaking havoc in the United States alone with 116 people dying every day in the last few years from opioid-related drug overdoses, according to the US Department of Health and Human Services. The crisis has also resulted in 2.1 million people reporting they have an opioid use disorder and accrued $504 billion in economic costs in the same time frame. To combat the epidemic, government agencies are analyzing the data and using new tools to spot trends. Hasan noted that Oracle researchers helped identify one important factor in the research: the role of the prescribing doctor's gender. "Our data found that women doctors prescribed opioids far less often than their male counterparts," Hasan said. "We combed through billions of lines of data and hundreds of different data markers in two days and came up with these results." By accelerating that time to insight, the hope is that leaders can derive insights quickly and prevent more tragedies. Autonomous Analytics The realization of faster time to insight and the path towards autonomous cloud platforms is the result of a progression of analytics acumen that has developed in three stages as seen in the graphic below. First, the semantic models helped with data analysis and alignment across functions, and was used on premises. Next, the evolution of self-service, accelerated use became widespread over the past few years particularly in the cloud. Thirdly and most recently is the promise of autonomy, taking insight and decision making to a new level. Today, humans are doing most of the work. Data from existing sources is combined. The consumer works by executing queries, then gets insight by interacting with visual representations of the data, and builds models to predict future trends or outcomes. These are all managed and controlled by people. "We believe that the future of Business Analytics is Adaptive and Autonomous," Hasan said. "This means using machine learning to power the Business Analytics value chain. The value chain for data starts with discovery, moves to preparing and augmenting data, then to analysis, modeling and finally to prediction. it is data-driven and is a powerful platform for innovation." Context is also important, Hasan noted. It must understand "Who I am, Where I am, What I am doing. What do I need to know right now?" The system must serve up the best information possible in this very specific situation. "This automation is helped by the power of the cloud, of course, but also technologies such as natural language processing, geolocation, voice-enabled queries, anticipation of questions through machine learning, and data visualization." As part of his speech to business analytics and data experts, Hasan also announced Oracle Data Visualization Desktop to be offered for free with the Oracle Autonomous Data Warehouse Cloud. Visit our sites to find out more about Oracle Cloud Platform Autonomous Services and Oracle Analytics Cloud.    

What if you wanted to transform data into actionable insights that impact people's lives in new and extraordinary ways? Would you do this with a business intelligence suite on premises or with...

Analytics Cloud

Oracle Cloud Partner Vertice Helps Others Master Analytics

The journey to Oracle Cloud often begins with a single step, to paraphrase a common saying. And while Oracle has many avenues for businesses to deliver a cloud-based success, often we rely on the expertise of our partners to guide customers along the path. Such is the case of Vertice Cloud, an Oracle Platinum partner and recipients of the Oracle Excellence Award for the last two years. The Ireland-based company has spent a considerable amount of time helping other companies in the UK and Europe realize their business goals by using the cloud as the backbone of their infrastructure. Times are good for partners that enable companies to add more cloud infrastructure as a service. The global cloud migration services market size is expected to reach $9.6 billion by 2023, rising at a market growth of 22 percent according to researchers with Markets and Markets. Recently, Oracle sat down with CEO Tony Cassidy to discuss his approach to helping customers. While many organizations continue to run operations on premises, Cassidy touts Oracle's ability to help customers transition to more cloud-based direction. In the following video, Cassidy shares his confidence in the direction of Oracle Analytics Cloud and reveals his plans to relaunch Vertice as the Oracle Cloud pure-play partner. Because of its collaboration with Oracle, Vertice touts two marquee customers that have made a transition to cloud: An Post (Ireland's state-run postal system) and iCabbi (software that taxi fleet managers use to connect drivers and passengers. Both companies The video below Cassidy describes how Oracle Analytics Cloud’s hybrid architecture enabled the delivery of customer analytics for An Post in just six weeks. In the case of iCabbi, Vertice helped the startup software provider for dispatch companies connect passengers and drivers more efficiently. Cassidy describe delivering a pure-play cloud analytics solution that offers new insight across all his customers' data from around the globe. Of course, Vertice is just one of the Oracle's partners using Oracle Analytics to support their customers' digital transformations and cloud-based endeavors. If you like what you see, take one of our Quick Tours or Simulations to learn more about Oracle Analytics. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Oracle Analytics website.

The journey to Oracle Cloud often begins with a single step, to paraphrase a common saying. And while Oracle has many avenues for businesses to deliver a cloud-based success, often we rely on...

Analytics Cloud

When Management Reporting is Your Navigation

Everyone has a different definition of what "reporting" means to their organization.  What content matters is determined by the audience and their expectations.  Some want operational reporting, others financial, others "real time" reporting, and still others management reporting.  Well, it's all just reporting, right?  No, not at all.  Management Reporting is all about bringing together multiple data sources and extending the analysis with richer detail to determine the best possible outcomes while navigating your business.  By navigation we mean that a company can change course and know what the most likely possible outcome will be because of Management Reporting. The best system for this type of reporting is multi-dimensional, like Oracle Essbase.  And knowing that... is what differentiates successful financial organizations from their peers.  So what do I mean?  Let me break it down a bit: Management Reporting: Is a process for analyzing performance measures and variances Combines financial and operational data: multiple sources with different levels of detail Combines budgeting systems and operational systems Extends to additional levels of detail, and provides regular hierarchy dimensions and alternate hierarchies for different reporting needs Allows users to run "what-if" scenarios modeling for forecasts and trend analysis You can see that it is much more than canned or real-time reports from a single system.  Most analytic platforms fail to provide the ability to do true management reporting given all of these requirements.  Planning systems clearly do "what if" for iterations of budgeting versions, but are at a summarized level of detail, they aren't rich enough.  Operational analysis does a great detail for "what happened." By contrast, "what if" financial reporting is very static and formatted, without the ability to do ad-hoc analysis.  Real time reporting can be very detailed but can't take into account trends, projections, year over year comparisons.   Check out this diagram: As I said earlier, each reporting style meets a different need, and each in and of themselves is not a complete solution.  Which brings us back to the value of Management Reporting.  Variance analysis comparisons are truly "how are we doing?" comparisons.  What do we need to change or improve?  They are essential to run the business. Navigating changes and understanding immediate effects of those changes can separate success from failure.  A successful analytics platform is critical as we see the role of Finance changing.  People in Finance are doing more, and they are looking to work smarter, faster, and need to analyze richer data sets than ever before.  Management Reporting is one of the key processes that truly brings value to them and their organization as they strive to modernize and improve into the future, navigating and charting a course for success. Like what you're reading? Subscribe to the Analytics Drilldown, and get updates sent to you.

Everyone has a different definition of what "reporting" means to their organization.  What content matters is determined by the audience and their expectations.  Some want operational reporting,...

Using Data Visualization to Handle the Madness

Much has been written about data visualization (DV) and how it aids in the understanding of numbers. It’s true that we can gain deeper insights when data is in visual form. But are there any other benefits of DV? How about the dynamic manipulation of data? Yes. I believe there is value in being able to quickly “move and shift” data and discover new relationships, dependencies and meaning. To illustrate this, I have decided to create a new project in Oracle Data Visualization that looks at the upcoming NCAA Men's Basketball Tournament. I found my data on www.teamrankings.com. This website hosts NBA and NCAA basketball data and even provides their own rankings. I figured it's worth giving them a try during the madness that is about to ensue. Here is the spreadsheet I created with their data. After I uploaded the spreadsheet into DV, and started my project, I see screen below. From here, I can take any number of directions to try and predict the unpredictable. Let’s say, I decide to look at some specific matchups. So…in the South bracket, Virginia is set to play UMBC. I want to dig deeper into this matchup and use the filter function in Oracle DV to see this screen: Obviously, Virginia is the heavy favorite. With outstanding records versus top 25 and top 26-50 teams and a perfect record against teams ranked 51-100, Virginia should have no issues getting past the first round. Now let’s look at a different matchup with some visualizations. Although this game appears to be a close matchup between a 7th seed and a 10th seed with nearly identical records, if you look at the respective winning percentages against better teams, Arkansas might have an edge. Notice I say "might". This is the beauty of the Men’s NCAA Tourney. The unpredictability. Let’s say Butler upsets Arkansas. They would go on to play either Purdue or California State Fullerton. I know who Butler would prefer to play. Because the nature of March Madness is…madness, it's useful to be able to quickly manipulate the data you are looking for and better understand the entire landscape. You can preview "what-if" scenarios all day long. This is just one useful example of how to use DV. Let's say I want to filter out the best teams in the entire tournament. I simply change my filters to focus on the winning percentages and not specific teams. For the graphic below, I focused on teams with a 25 percent or better winning percentage against top 25 teams, a 50 percent or better record against top 26-50 teams, and a 75 percent or better statistic against top 51-100 teams. Here are my favorite teams to win it all: I enjoy working with numbers and feel they are best understood when represented visually. This is especially true when you can change directions quickly and discover new perspectives. I hope you found this blog informative and that you make use of the spreadsheet I attached above for your own March Madness research. Better yet…sign-up for a trial version of Oracle DV and create your own visualizations! Also…be sure to visit our website and take one our Quick Tours and/or Simulator. You can also find more resources on our Customized Content page. Also, for current Oracle Data Visualization customers, be sure to visit the Oracle Analytics Library and download sample files to take your analytics to a new level. Thanks for your time.  

Much has been written about data visualization (DV) and how it aids in the understanding of numbers. It’s true that we can gain deeper insights when data is in visual form. But are there any other...

Analytics Cloud

Why Cloud is a Disruptive Force for Innovation and Change with Analytics

Oracle's Point of View on Gartner Magic Quadrant for Business Intelligence and Analytics Platforms 2018 Every day, I talk with customers and prospects about their plans for analytics and business intelligence. Every one prominently mentions "Cloud" as their go-forward computing environment. Regardless of what path a customer takes to Cloud, it's abundantly clear that this decision is not just a deployment option—it's one that unleashes innovation throughout the enterprise. It frees analysts and business consumers to explore data sources within and outside the enterprise at will, to combine data in ways not possible before without IT efforts, and to share that data wherever/whenever they need to, all in the Cloud. It frees IT from procuring, managing and deploying servers behind the enterprise firewall; solely managing any data for analytics; and being the choke point for any data and analytics activity. Need more resources? Instantly scale up cloud compute nodes and you’re done. Scale down when resources are no longer needed. Enable the business consumer, not control them. Enabling self-service everywhere changes the game for business intelligence and analytics, and is a major force for innovation. Add in integrated PaaS services, including the market-leading Autonomous Data Warehouse, Data Integration, Security and Identity Management, as well as SaaS applications such as HCM, ERP, Customer Experience and Supply Chain, and organizations can now easily embrace the transformative power of cloud to not just improve, but also fundamentally change the way they work. Oracle’s Core Beliefs about Business Analytics We believe Cloud is the disruptive technology that fosters change for business analytics. Our investments in Oracle Analytics Cloud (OAC) are optimized so that new and existing customer can experience a simple, self-service set of capabilities in a Cloud or hybrid world. Self-service analysis, self-service mash-ups, self-service prediction—empowering professionals on the front-line of the organization. We believe that to serve the enterprise, a rich set of capabilities are necessary, including self-service data visualization, mobile, OLAP, autonomous discovery, as well as reporting and dashboards, all running in a robust cloud environment to meet the wide range of use cases and needs of any enterprise. Self-service data visualization is not the only market requirement, but one of many essential capabilities to serve buyer requirements. We believe that autonomous analytics are the future, and are here NOW in OAC.  Conversational interfaces, machine learning (ML) embedded in the analytics experience, proactive, personalized content based on "in the moment" context, augmented and virtual reality—all part of today's service offering. Couple that with ML-fueled capability across the platform and SaaS applications, Oracle is ready to serve its customers now on all fronts. Autonomous Analytics platforms leverage the power of cloud and artificial intelligence to automate BI & Analytics, not simply automate discrete tasks. The focus of evaluation for the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms is largely self-service data visualization. Oracle recognizes this as a critical component of any BI and Analytics regimen, but only part of a broader Cloud-based data and analytics strategy that every organization needs to consider moving forward. The MQ evaluation framework did not fully factor Oracle’s considerable competitive advantages in cloud computing and the benefits this confers to analytics consumers. It's important to note that Gartner's definition of "Ability to Execute" and "Completeness of Vision" are not just current/future ability and product roadmap.  Both evaluate many facets (see here for more information about what goes into this assessment). Oracle believes its business analytics product roadmap and future ability are both top tier. See What Other Analysts Think Prominent industry analysts have assessed Oracle Analytics products in the past six months and have come to different conclusions than Gartner. Both Forrester Research and BARC named Oracle as a  Market Leader. Ovum indicates the Cloud is a model for better business. Take a look at this research to see how breadth and depth of business intelligence and analytics capabilities along with Cloud really make a big difference. I welcome your feedback or questions about this blog. You can reach me at john.hagerty@oracle.com.  

Oracle's Point of View on Gartner Magic Quadrant for Business Intelligence and Analytics Platforms 2018 Every day, I talk with customers and prospects about their plans for analytics and business...

Success Stories

Anthem Prescribes Oracle Analytics for Talent Lifecycle

Analyzing patient data seems so normal these days that it would be hard to imagine a doctor that would ignore a person's vital signs and make life decisions without consulting the information. Anthem Inc. is taking that layer of analysis one step further and developed a portal to better improve career decisions of its own employees. The health care insurance provider's human resource department is analyzing the lifecycle of its employees with the help of Oracle Analytics. The Indianapolis, Indiana-based company says the goal of its health plan division is to use the information gathered by its analytics tools to improve its HR department's recruiting, hiring, promotion, and development programs. The HR analytics team at Anthem created what it calls a People Data Central (PDC) portal that serves its more than 56,000 associates. Oracle Analytics' scenario modeling and data visualization tools allow Anthem's HR teams to answer significant questions, such as: What does our turnover look like in a specific region for a specific job function? What is the cost of that turnover? Are we differentiating rewards for our top performers? How many of our nurses might retire within the next couple of years? According to an article in The Wall Street Journal, Anthem's human resource team gathers information each month and breaks down various indicators such as company performance, employee participation in wellness programs, reductions in absenteeism, or salary benchmarks. The data analysis helps the company increase workforce productivity, reduce turnover, and foster diversity. "A high-level 'executive scorecard' feature explores the relationship between HR metrics and business outcomes to support short and long-term planning," author and Oracle Content Central editorial director, Rob Preston wrote. "The team's latest report described the relationship between customer growth, Anthem’s net hire ratio and total costs associated with internal and external labor. PDC also provides links to additional analytics and relevant external reports and reporting tools." During the following interview, Joe Knytych, Staff VP of Talent Insights at Anthem, describes how Oracle's self-service analytics in the cloud helps Anthem achieve its goal of driving innovation by enabling deep analytical expertise together with broad operational insights. Of course, Anthem is just one company using Oracle Analytics to drive its digital transformation and cloud-based endeavors. If you like what you see, take one of our Quick Tours or Simulations to learn more about Oracle Analytics. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Oracle Analytics website.

Analyzing patient data seems so normal these days that it would be hard to imagine a doctor that would ignore a person's vital signs and make life decisions without consulting the information. Anthem...

Machine Learning

Machine Learning Alleviates 'Blank Canvas' Syndrome

For an artist or a writer, a completely blank canvas might inspire the next Rembrandt masterpiece or Pulitzer Prize winner. For a business manager performing data analytics, a blank canvas might cause extreme terror. Every data visualization tool on the market today looks and operates in a similar fashion.  They have a pick-box down the left of dimensional attributes and numeric metrics and a canvas onto which you would drag and drop those attributes and metrics thus constructing your visualizations.  See examples from various vendors below, including Oracle. However, there is an issue with all of these tools that is being referred to as a "blank canvas" syndrome. In this blog, we will discuss what it is and why is it something you would consider alleviating. The Blank Canvas Syndrome What is the issue with this blank canvas?  Seems like a natural and clean position to begin any analytics.  Let's take two different but equally capable analysts using the same data visualization tool.  Each analyst has their unique background, life experience, and professional experience.  If each analyst was tasked with finding the answer to the same business question, using the same tool and same data, would each analyst actually execute exactly the same steps and reach the exact same conclusion?  No, that's unlikely.  How the analyst tackles the problem comes down to their experience, bias, and even their imagination.  When faced with a blank canvas, the combination of attributes and metrics selected and visualizations created becomes very different.  The end results are usually quite distinctly different.  When analysing an HR data set to understand attrition rates, our first analyst might think that salary is a key factor and create an answer around that while our second analyst thinks the issue is connected to with sub-standard offices that are not comfortable for employees.  Another issue that arises is that our analysts may become fixated on a spike or trend they spotted and consequently miss other critical factors that contribute to the overall problem.  Our analysts essentially can't see the forest for the trees. Are our two analysts bad at their jobs?  No, it's human nature to fall back on your experience when faced with new challenges.  However, differing answers to the same question is a significant business problem. Today, the trend is to become more productive and more efficient. Cutting costs by optimizing business processes is top of mind.  This rather slow, iterative, and wasteful approach for analysts to start their analyses to then render conclusions that might answer only part of the problem.      Machine Learning That Explains Attributes in Context Modern analytic platforms have machine learning embedded to alleviate the issues I described earlier.  Machine Learning removes human bias and imagination, considers vastly more information, is incapable of missing key signals and does everything in a fraction of the time.  Sure, the machine might not provide a 100 percent complete picture, and some human interpretation may be required, however, if our objective is to become more efficient with better accuracy, then machine learning saves tons of time—and time is money.  The key is that machine learning is placed into the hands of the business professionals, the end users.  In our case, this means our two HR analysts.  There is no requirement to present the problem to a data scientist (and their team) who use extremely complex and expensive tools to power machine learning to generate results days later.  What if you could instead, ask the visualization tool to explain an attribute in context of the other attributes and metrics in the dataset?  Instead of starting with my blank canvas I'm immediately presented with two key drivers of attrition, Over Time and Job Role.  It turns out that salary and premises were both only minor factors – both our analysts missed the key business problems. I'm also presented automatically generated insights like below.  With 93 percent confidence, attrition was high due with people that had a Job Level less than 2, have been with the company for less than four years and were putting in overtime.  Immediatley, I can see a tangible problem that I can address – without even starting to drag and drop stuff onto my blank canvas. Finally I'm also presented with anomalies that are usually missed or too time consuming to figure out via manual analysis.  Here I can see that the machine discovered that our Research Scientists who are putting in overtime are leaving at a much higher rate than normal. Alleviate Blank Canvas Syndrome Today, blank canvas syndrome is accepted as normal.  However, starting analyses this way is inefficient, introduces human bias.  It also may lead to conflicting answers, or entirely miss the root cause of the problem.  Machine Learning is now in the hands of business users, empowering them to filter noise, quickly see the signals and share their findings.  Have you started using machine learning with your analytics, or are you still just coping with how it's always been done? Take a look at a complete video demonstration showing how to alleviate blank canvas syndrome using machine learning to explain attrition in an HR dataset.

For an artist or a writer, a completely blank canvas might inspire the next Rembrandt masterpiece or Pulitzer Prize winner. For a business manager performing data analytics, a blank canvas might cause...

Success Stories

Western Digital Drives its Future with Oracle Analytics

Data storage and the cloud are two technologies that complement each other. How else can it be so simple and appealing to upload data and then access it anywhere? So, it should come as no surprise that a cloud leader like Oracle would help a data storage powerhouse like Western Digital to help it meet customer demands and expand its business. From its early days as a storage drive leader, Western Digital has matured its capabilities and helped fuel the hot growth in data centers and data storage for cloud providers. How hot? Total spending on IT infrastructure products such as servers and enterprise storage for deployment in cloud environments hit $46.5 billion in 2017 with a year-over-year growth trajectory of 20 percent, according to researchers with IDC. Running data analytics in those cloud environments also plays a major part, according to the latest forecasts by Gartner research as companies look to make sense of their business data. Bill Roy, Senior Director of Enterprise Performance Management at Western Digital recently sat down with Oracle to discuss the company's use of analytics in its operations, how it translates those efficiencies to serve its customers, and its outlook on using analytics in the cloud. Suffice to say, WDC has transitioned to a "cloud first" company that is embracing next-generation data analysis to bolster its capabilities. Here's Roy's perspective about transitioning to a Cloud First company. The demand for cloud storage solutions is rising among a diverse set of vertical markets. Analysts have recorded spikes in cloud adoption among banking, financial services and insurance, healthcare, and media and entertainment companies. Other factors contributing to the growth of the data storage market are increasing adoption of cloud storage gateways, burgeoning investments in latest Information Technology solutions and more awareness and deployment of cloud-based solutions. Here's what Roy had to say about why Western Digital was eager to work with Oracle Analytics. Another factor in choosing Oracle Analytics was that Western Digital had also gone through a few acquisitions and needed a platform to help its managers perform analytics services without putting a strain on IT. Here's what Roy had to say about avoiding downtime and increasing productivity. Of course, Western Digital is just one of the companies using Oracle Analytics to drive its digital transformation and cloud-based endeavors. If you like what you see, take one of our Quick Tours or Simulations to learn more about Oracle Analytics. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Oracle Analytics website.

Data storage and the cloud are two technologies that complement each other. How else can it be so simple and appealing to upload data and then access it anywhere? So, it should come as no...

Analytics Cloud

How Autonomous Analytics Accelerates Business Insight

Even with the best intentions, people can't possibly analyze all of the data at their disposal. Those that have a plan, however, will see a great reward over their competitors; a $430 billion advantage, according to projections by analyst firm IDC. What’s needed is a way for enterprises to generate business insight at breakneck speed that applies next-generation thinking across its entire portfolio to help lower cost, reduce risk, accelerate innovation, and get predictive insights. And by these standards, we suggest Oracle Autonomous Analytics. Oracle President of Product Development Thomas Kurian recently demonstrated the latest advances in the Oracle Cloud Platform. Among the many self-driving, self-securing and self-repairing Cloud Platform services, Kurian demonstrated how Oracle Analytics will include automated data discovery and preparation as well as automated analysis for key findings along with visualization and narration delivering quicker insights. The multiple autonomous database services, each tuned to a specific workload, are expected to be available in upcoming versions, including Oracle Autonomous Data Warehouse Cloud Service for analytics. What do we mean by Autonomous Analytics? Not artificial intelligence and not simply automated, it is the application of machine learning to human judgment. This new collaboration creates many opportunities to improve your decision-making and predictive abilities as the data landscape becomes more and more complex. Machine learning is key to enabling Autonomous Analytics. Machine learning is not, however, about writing more rules. A systemic problem today is that most predictive systems are built on millions of lines of code with nested business rules that are complicated and expensive to maintain.  Never mind launching an audit. Instead, Autonomous Analytics refers to self-learning algorithms that thrive with the growing volume of data. Unlike prior generations, these algorithms modify themselves as more data and more actions are evaluated. Adaptive and Autonomous The future of business analytics is both adaptive and autonomous. This means using machine learning to power the business analytics value chain which starts with discovery, moves to preparing and augmenting data, then to analysis, modeling and finally to prediction. It is data-driven and is a powerful Platform for innovation. Personal context is all-important: the system must understand who I am, where I am and what I need to know now. The objective of data discovery and preparation stages is to help you easily find useful datasets across any combination of sources. It means understanding which datasets you can access and what condition they are in with regards to quality and completeness. Then to receive automated recommendations for data standardization, cleansing, and enrichment. Rather than starting your visual analysis with a blank canvas, you can now start with insights that are automatically generated, based on correlations and patterns in the data that the system identifies. This autonomous capability speeds up analysis provides automatically generated narration and delivers quick real-time insight. Pictured above: Oracle Day By Day   Today, on your mobile device, you can verbally ask business questions (natural language processing) using your own business- and company-specific vocabulary, have the system understand your requests and anticipate your questions based on self-learning. It will reply to your voice queries with multiple possible answers, then learn and refine based on your guidance. Currently, data-savvy executives can create visual business models without any special training, collaborate with others on the models, then test assumptions without impacting anyone else, in order to see answers to "what happened" and "what if". But now we can create a new value when these models automatically learn from transactions and update predictive results in real time. Not only does this reduce administration overhead, but it also eliminates human bias. This allows real-time learning to be immediately available for the next prediction, for example, to drive adaptive, high-value interactions with customers, to determine the best profile for employee candidates, or to recommend when to service critical machinery and capital assets. For business professionals, Autonomous Analytics can provide insight to inform every decision, when and where it matters, and in context. For IT, automated insights result in lower cost and enable business users to be more self-sufficient, lowering the support burden. Autonomous capability, driven by machine learning, is a promising enabler of greater productivity and innovation. How would you feel about an autonomous approach to your data? Feel free to leave a comment below.

Even with the best intentions, people can't possibly analyze all of the data at their disposal. Those that have a plan, however, will see a great reward over their competitors; a $430...

Success Stories

Exelon Powers Up with Oracle Analytics

For an energy company, Exelon's data output outpaces the tens of thousands of megawatts of nuclear, gas, wind, solar and hydroelectric it can generate at capacity. Servicing more than 10 million Exelon customers means managers need to understand and act on the data the company generates. The company's business intelligence and data analytics solutions previously sat in pocketed silos without any centralization or strategic vision around it; complicating issues. A series of mergers and acquisitions over the years made integrating data and analytics systems from six different utilities a critical issue as well.  Headquartered in Chicago, Illinois, Exelon operates in 47 states, the District of Columbia, and Canada. The company employs upwards of 34,000 people. And while Exelon is the largest electric holding company in the US by revenue (around $34.5 billion annually), managing employee, partner, and customer data can be a daunting task.    Exelon IT Director, Michelle Ferrara recently talked to Oracle about the process the company took to better analyze its data using Oracle Analytics. The company also uses a combination of Oracle products for data ingestion such as Oracle Analytics Data Lake Edition along with other Oracle appliances to integrate, aggregate, and expose data internally or to its partners. The video below spotlights the high marks that Ferrara gave Oracle Analytics for helping Exelon integrate and manage the data from various sources: Ferrara mentions Exelon's use of Oracle Opower products. The software allows the company to offer multi-channel personalized experiences for its residential, business, commercial, and industrial customers. The platform includes a full suite of business analytics tools that give Exelon self-service access to data and insights to improve program management and decision making. Funnel analysis, web metrics, and other data analytics applications give Exelon a comprehensive view of its customer engagement across all touchpoints at all utilities. The video below explains how Exelon combined the data from its six different companies with the help of Oracle Analytics. Find out more about Oracle Analytics at https://www.oracle.com/solutions/business-analytics/analytics-cloud.html [Editor's note: A previous version of the story has been corrected to reflect Oracle Analytics as part of Exelon's solution set]

For an energy company, Exelon's data output outpaces the tens of thousands of megawatts of nuclear, gas, wind, solar and hydroelectric it can generate at capacity. Servicing more than 10 million Exelon...

Success Stories

iCabbi Connects its Many Customers Using Oracle Analytics

Even if you don't live in New York City, chances are you know the proper way to hail a taxicab these days does not involve whistling through your fingers. Smartphone apps make it easy for people to schedule, track and pay for a traditional cab and/or ride-sharing services. Part of that industry transformation involves a constant wave of information for dispatchers and drivers; one that simple spreadsheets can't process. Irish startup iCabbi was faced with this transformation following its launch in 2009. The company produces passenger apps that integrate into an online booking engine that dispatchers can use to maintain customer experience and driver quality. The Dublin-based firm currently now operates in four markets – Ireland, the UK, US, and Canada – with more than 500 companies signing up in the last two years. However, collecting constant data from 65,000 taxis and more than 100,000 drivers is much more than the company could handle.  Instead, the company sought out a partner to help manage data for analysis, which would be used later to review speed of the apps, ease of use, and customer experience. The apps also needed to be cloud-based to provide scale and support for the customer and the driver.  So, some time ago, iCabbi chose to work with Oracle Analytics with an emphasis on the cloud to help it keep up with the data and to provide growth opportunities. Following the deployment of OAC (Oracle Analytics Cloud) iCabbi received an Oracle Excellence Award for Innovation with Enterprise Analytics. In this first video, Ian McDonald, Senior Business Analyst with iCabbi explains how serving customers requires understanding their needs based on potentially millions of data points.  McDonald also notes that the company was cloud-based from the start, which made Oracle Analytics Cloud a natural fit for their current and future needs. If you want to see Oracle Analytics Cloud for yourself, take one of our Quick Tours or Simulations. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Business Analytics website.  

Even if you don't live in New York City, chances are you know the proper way to hail a taxicab these days does not involve whistling through your fingers. Smartphone apps make it easy for people to...

Essbase Cloud

Ignore Robots - Or Better Yet, Count Them Separately

It is quite common to have web sessions that are undesirable from the point of view of analytics. For example, when there are either internal or external robots that check the site's health, index it, or just extract information from it. These robotic sessions do not behave like humans and if their volume is high enough they can sway the statistics and models. One easy way to deal with these sessions is to define a partitioning variable for all the models that are a flag indicating whether the session is "Normal" or "Robot". Then all the reports and the predictions can use the "Normal" partition, while the counts and statistics for Robots are still available. In order for this to work, though, it is necessary to have two conditions: It is possible to identify the Robotic sessions. No learning happens before the identification of the session as a robot. The first point is obvious, but the second may require some explanation. While the default in Oracle Real-Time Decisions is to learn at the end of the session, it is possible to learn in any entry point. This is a setting for each model. There are various reasons to learn in a specific entry point, for example, if there is a desire to capture exactly and precisely the data in the session at the time the event happened as opposed to including changes to the end of the session. In any case, if Oracle Real-Time Decisions has already learned on the session before the identification of a robot was done there is no way to retract this learning. Identifying the robotic sessions can be done through the use of rules and heuristics. Consider some of the following: Maintain a list of known robotic IPs or domains Detect very long sessions, lasting more than a few hours or visiting more than 500 pages Detect "robotic" behaviors like a methodic click on all the link of every page Detect a session with 10 pages clicked at exactly 20-second intervals Detect extensive non-linear navigation Now, an interesting experiment would be to use the flag above as an output of a model to see if there are more subtle characteristics of robots such that a model can be used to detect robots, even if they fall through the cracks of rules and heuristics. In any case, the basic and simple technique of partitioning the models by the type of session is simple to implement and provides a lot of advantages. If you want to see more signals in your data, Oracle Essbase Cloud Service transforms complex financial modeling into easy-to-understand visualizations. Use this highly advanced calculation engine to perform what-if scenario modeling on any data set. Quickly build custom analytic applications with a simple workflow for merging datasets. (Editor's note: This article was originally published on Oracle's Real-Time Decision blog)

It is quite common to have web sessions that are undesirable from the point of view of analytics. For example, when there are either internal or external robots that check the site's health, index it,...

Analytics Cloud

When Boardrooms Meet Natural Language Processing

We've all been in meetings where a strategy question is asked and the data geek pulls out their smartphone and does some 'back of the envelope' calculations.  The answer? 42. What were the assumptions?  How did they get there so fast?  I'll admit, sometimes that data geek is me.   As a great mentor of mine once said—never trust averages because if your feet are on fire and your hands are frozen, on average, you're not OK.  Being a data geek, I'm usually first to recognize that there is no substitute for a well-formed analysis which assesses a wide range of possible scenarios.   When strategic questions are well framed, they are almost always multi-dimensional.  Rarely, if ever, does a critical analysis just have two dimensions. Yet, we still try to force fit the scenarios into our favorite envelope—albeit a technical one—Excel.  Don't get me wrong, Excel spreadsheets have their place, but not for evaluating the feasibility of strategic business scenarios.   Let's face it, pivot tables are used by so few consumers and though well intended don't fit the bill for scenario modeling.  Why? For starters, the data is stored in two dimensions.  I could go on forever on this topic but I'll spare you the time (for now). What's missing from far too many management and board meetings is agile scenarios.  Scenarios that factor in the entire enterprise data.  Scenarios that understand the intricacies of the profit model and associated drivers.  Scenarios that account for fluctuations in currency and a whole host of other pertinent factors in making the analysis sustainable and defensible.  From what I see in the labs at Oracle, I foresee a very different boardroom.  I foresee one which is powered by emerging technologies like digital assistants, cloud computing, and natural language processing.  I foresee one where the HiPPO's (Highest Paid Person's Opinion) view is augmented with facts.  The API's built-into Oracle Analytics Cloud allows developers to quickly connect a variety of conversational interfaces to the platform.  Digital assistants, chatbots, and voice-enabled smartphone access is just the beginning.  With Oracle Essbase Cloud, executives and board members will not only be able to have a conversation about the past but will be able to assess the plausibility of different scenarios of the future.  Deloitte, for example, has already built an innovative solution which integrates Alexa with the Oracle Planning and Budgeting Cloud Service to help non-finance professionals engage more deeply in the planning and forecasting process.  These conversational interfaces are sure to change the tone and tempo of every management meeting in the future.  I for one can't wait! If you want to see Oracle Analytics Cloud or Oracle Essbase Cloud for yourself, take one of our Quick Tours or Simulations. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Business Analytics website.

We've all been in meetings where a strategy question is asked and the data geek pulls out their smartphone and does some 'back of the envelope' calculations.  The answer? 42. What were...

Data Visualization

Predict Heart Disease with Oracle Data Visualization

How strong are our hearts? Can we use data to help prevent heart disease that could lead to heart attacks or strokes? It turns out a simple tool like Oracle Data Visualization combined with our Machine Learning plugin might be the keys to the solution. Heart Disease (including Coronary Heart Disease, Hypertension, and Stroke) accounts for about 1 of every 3 deaths in the US, or nearly 801,000 deaths in one year, according to the American Heart Association, Cardiovascular disease is the leading global cause of death, accounting for more than 17.3 million deaths per year in 2013, a number that is expected to grow to more than 23.6 million by 2030. A healthy lifestyle—including good nutrition, exercise, and avoiding smoking—can decrease the chances of developing heart disease. But what if we could see data based on specific markers that could spot trouble ahead and the likelihood of cardiovascular disease?  Let's crunch the numbers and find the story. In honor of Valentine's Day and American Heart Month, we offer this demonstration video showing how Oracle Data Visualization and machine learning algorithms are applied on patient health data to predict the prospect of heart disease. Multi-classification Machine Learning technique is used in this demonstration. The process shown in the video below can be summarized as follows: Get data of patients known to have heart disease. This dataset contains information related to heart diseases like blood sugar, cholesterol and other medical information about the individual Create a multi-classification neural net model using that data  Use that model to predict the heart disease likelihood in other individuals for whom we know their medical history or medical information   The machine learning plugin example seen in the video can be downloaded from the Oracle Analytics Store. The name of the project is Example DV project: Heart Disease Prediction:   Why Use This Method? More than often, most individuals and businesses have access to historical data which contains information on whether a particular event has happened or not; under what conditions has it happened and what are the values of other factors involved in this event. Wouldn't you want to use this historical data to predict whether that event is likely to happen or not? (Is it Likely? Less Likely? More Likely? Definitely?). The method of training a model using actual known values of a column, to predict the column value for unknown cases, comes under the domain of Supervised Machine Learning. Oracle Data Visualization comes equipped with inbuilt algorithms to perform such supervised multi-classification and others. Users can choose any one of these algorithms based on the need. Here is a snapshot showing list of inbuilt algorithms in Oracle Data Visualization that can perform this multi-classification, as seen in the graphic below:   By using these methods, even a simple-to-use data visualization tool can help answer complex questions and get to the heart of the matter. Of course, you can't test out these features for yourself unless you get your hands on it. To learn more about the machine learning feature download Oracle Data Visualization Desktop and tell your boss how much you love it.

How strong are our hearts? Can we use data to help prevent heart disease that could lead to heart attacks or strokes? It turns out a simple tool like Oracle Data Visualization combined with...

Data Visualization

Make the World Your Data Visualization

As the saying goes, the world is your oyster. We at Oracle suggest an addendum to this phrase. We think the world can also be the source of your successful data visualizations. Take for example the concept of a world map. The various hemispheres, territories, continents, and countries are all ripe for identifying and quantifying so that people can easily understand data concepts. Do you have a geographic map layer data sitting in a shapefile format and would you like to visualize it? In this blog, we will discuss how to use Oracle tools to convert a shapefile into GeoJSON format for use in Oracle Data Visualization. In a previous blog, we discussed how to convert an image to a GeoJSON file format so that you can derive value from a location like a floor plan. We also outlined how to create a GeoJSON map layer from an existing Oracle Database map theme to something a bit larger. GeoJSON is a format for encoding a variety of geographic data structures using JavaScript Object Notation (JSON). Oracle Data Visualization supports custom map layers defined in GeoJSON formats. A shapefile format is a digital vector storage format for storing geometric location and associated attribute information. The shapefile format can spatially describe vector features like points (address or location), line strings (streets or boundaries), a region of space (counties or states), or other polygons representing a different kind of geographical features. For our example, here is a generic map of the globe. It looks blank, but just plug in a few data sources and you will be surprised at the insights you can extract. The file name extension of shapefiles is .shp. More information on shapefiles can be found here. Using Oracle Map Builder you can convert shapefiles to GeoJSON files. GeoJSON can be directly uploaded into OracleDV as a custom map layer and the data can be visualized directly on top of the Map layer. To understand this in a bit more detail, download the detailed instructions. In simpler terms, you will: 1) Install Oracle Map Builder 2) Use Export to JSON feature in Map Builder and use ShapefileSDP as source type and use the shapefile to convert. 3) Choose appropriate key columns and SRID to do the conversion. Within a few minutes, you will be able to use Oracle Data Visualization tool to create regional sales territories, identify diseases in specific countries, or just spot marketing trends across continents. If you like what you see, take one of our Quick Tours or Simulations to learn more about Oracle Data Visualization. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Business Analytics website.

As the saying goes, the world is your oyster. We at Oracle suggest an addendum to this phrase. We think the world can also be the source of your successful data visualizations. Take for example the...

Data Lab

7 Best Practices for a Successful Data Lake

Or, how to avoid building a Data Swamp So, you’re thinking about a data lake for your organization. You might even have the green light to start the planning stages. We’re big believers in the power of the data lake. It can often be a significantly cheaper way to store your data, but that’s not the most attractive part. No, it’s the fact that it can hold your structured and unstructured data, internal and external data, and enable teams across the business to discover new insights. But here’s the thing – the hype has run away (a little) with the data lake. A data lake is not something you can implement with a snap of your fingers. The rewards are enormous, but it still takes work and strategy, and that’s why we want to help you avoid some mistakes. Let's create an easier path to data lake nirvana.  [Try Building Your Own Data Lake With a Free Trial] We’ve gathered insights from experts Larry Fumagalli and David Bayard of Oracle’s Cloud Platform Team for best practices and what not to do. First, of course, make sure you think about whether your data lake is going to be located in the cloud or on premises. Do you have to create your data lake on premises because of regulatory or business requirements? Or can you locate your data lake in the cloud and take advantage of the new data lake architecture, which we’ll describe in more detail below. Perhaps you can talk the exec team into trying cloud if you have your own private cloud. There are pros and cons to each of these methods, but that’s a topic for an entirely different article.  Today, we’ll focus on data lake best practices overall. 1. Start With a Business Problem or Use Case for Your Data Lake Over and over, we’ve found that customers who start with an actual business problem for their data lake are often more effective. They are more likely to have results to point to, and more likely to have information that will please the higher-ups. They often also get the data job done and do it more quickly and more easily, because they remain focused. This may seem like a basic piece of information, but we include it here because there still exists a tendency for IT to turn their data lake into a science project; they want to play with it and experiment and build a dream data repository. And they tend to assume that once that dream is a reality, it will solve all use cases and business teams will simply come to them with their data questions and issues. But the actual reality is that this rarely happens, and it’s better if you start with a business problem in mind, stay focused, and solve it. Read the other seven best practices here. Guest author, Sherry Tiao is a Product Marketing Senior Manager for Oracle Big Data Cloud 

Or, how to avoid building a Data Swamp So, you’re thinking about a data lake for your organization. You might even have the green light to start the planning stages. We’re big believers in the power of...

Data Visualization

Map Your Data Visualization Using a GeoJSON Oracle Database Theme

Using maps in data visualizations is extremely effective. You can instantly communicate location and spatial information in colorful and easy to understand ways. Whether for a regional manager looking to arrange sales territories or an insurance underwriter trying to compare city data with customer demographics, data visualization and maps go hand in hand. In this blog, we will discuss how to create a GeoJSON map layer from an existing Oracle Database map theme. This helps Oracle customers who have their maps/spatial data in Oracle Database and who now want to leverage that investment using Oracle Analytics Data Visualization.  In a previous blog, we discussed how to convert an image to a GeoJSON file format so that you can derive value from a space. Using a combination of Oracle tools, users can convert an image of a layout into GeoJSON. GeoJSON is a format for encoding a variety of geographic data structures using JavaScript Object Notation (JSON).  Oracle Map Themes are also referred to as a Geometry Theme. A theme is a visual representation of a particular data layer. Using Oracle Map Builder, you can extract a GeoJSON from this Geometry theme. This GeoJSON can be directly uploaded into Oracle Data Visualization as a custom map layer. Let's take a look at how this might work. In the example below, we have an Oracle Database Map Theme of California. While the congressional districts are clearly defined, we want to assign values to each region in the state. In that way, we can dig deeper into the data and see a visual representation to help us uncover its value: Now, let's see how that extracted map layer will look when it is rendered in Oracle Data Visualization Map:   Already, we can derive value from the information based on the color hues of the districts. In this case, the larger the size of the district, the darker the blue color. But now, we can now dig deeper into the data for each area while keeping the map shape and properties intact. Detailed steps on how to do this conversion can be found in this document. On a higher level, you want to take these steps: 1) Install Oracle Map Builder (If it's not installed already).  2) Connect to the Database Schema where the maps/Spatial data is present 3) Select the table that contains Geometry Theme data 4) Use Map Builder tools to extract this geometry table into GeoJSON If you like what you see, take one of our Quick Tours or Simulations to learn more about Oracle Data Visualization. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Business Analytics website.

Using maps in data visualizations is extremely effective. You can instantly communicate location and spatial information in colorful and easy to understand ways. Whether for a regional manager looking...

Analytics Cloud

A Recipe for Tasty Marketing Analytics

In my first couple of years out of college, while safely past my starving-student, ramen noodle-eating days, I wasn't making much money. I must admit, I didn't much fancy learning to cook, but needs must when the devil drives. Boy was I terrible at it. Then my dad gave me a cookbook (Fearless Cooking for One.  Thanks Dad for pointing out I was single!) and I started to learn. Over the years, I’ve trained myself to be a pretty good cook. I bake bread and pasta from scratch and love to cook for my friends and family, especially Italian food. Happy to trade recipes with anyone, by the way.  While I can now look at a bunch of ingredients in my refrigerator and pantry and instinctively know what to make with them, with or without a recipe, that took a lot of trial and error, practice, and burnt pork chops. "What the heck does this have to do with marketing?" you might ask. Actually, it has more to do with data.  How good am I – how good are you – at looking at a pantry (spreadsheet) full of ingredients (attributes) and knowing what to cook for dinner (how to analyze the data)? It would be awesome if Julia Child, or your favorite chef, was there whispering advice. Ditto the awesomeness of having your analytics explain to you which attributes are important, and then give you the tools to quickly whip up dinner – I mean build a dashboard. What if you were given a couple of datasets with the results of recently run marketing campaigns, and asked to put together a story showing which campaign types were successful and why. You probably would start by poking around the data sets until you found an attribute called “Campaign Type”.  Good start.  Now what?  Randomly drag and drop combinations of Campaign Type and other attributes onto a blank canvas? What a waste of time! Instead, how about Analytics that can explain the Campaign Type attribute and start building your dashboard for you. That’s precisely what Oracle Data Visualization gives you, as you can see in the video below.   If you want to run your own data through this process, we're offering a free trial of Oracle Analytics Cloud. You can also read more in this white paper entitled, Go All in With Analytics for Marketing Finally, and most importantly, who’s got the best Sunday Sauce with Meatballs recipe?

In my first couple of years out of college, while safely past my starving-student, ramen noodle-eating days, I wasn't making much money. I must admit, I didn't much fancy learning to cook, but needs...

Data Visualization

Visualize Key Economic Indicators

It's 2018! Time to get back to the gym, eat better, review your budget, analyze your investments, and all the other "better you" tasks that we neglect most of the year. And why not? It's a good opportunity to set goals and take stock in our respective situations. Since I work on the Oracle Business Analytics team, I find it useful to leverage the tools at my disposal to help me evaluate where I stand with many of life's measuring sticks. This is especially true when it comes to money. Should I be less aggressive with my investment choices? Should I continue to ride the bull market? Stocks or bonds? Mutual funds or ETFs? I am not a financial adviser so if you are looking for any hot tips, you won't find them here. What I can offer is an easy-to-use tool that will help you see the economy better and hopefully make you feel more comfortable with your understanding of the current situation. I will be using some information I found on Investopedia.com. I simply searched for "Best Economic Indicators" and a link with the title ‘Top Ten US Economic Indicators' appeared. I read the article and decided to create several visualizations that highlight some of the indicators listed there. Once again... I am not a financial advisor and Oracle is not suggesting that you use these visualizations to make any investment decisions. This is a simple snapshot of the US Economy that I put together to help you view the current environment. To start, I created a spreadsheet that I then imported into Oracle's Data Visualization tool. Below is a brief description of the indicators I included in the spreadsheet. Employment Situation The United States Department of Labor and more specifically, the Bureau of Labor Statistics releases a monthly report that details the unemployment rate. For this data point, I calculated the monthly percent change in an inverted fashion because as the unemployment rate drops, the metric becomes more positive. Industrial Production and Capacity Utilization The United States Federal Reserve delivers a report each month that measures the output of manufacturing-based industries. For this analysis, I used the monthly percent change of industrial production that is provided by the Federal Reserve so I did not have to calculate any data. Personal Income and Outlays The United States Department of Commerce's Bureau of Economic Analysis offers a monthly report that highlights consumer spending and personal income. For this metric, I calculated the monthly delta between personal income percent change and price index percent change. New Residential Construction The United States Department of Commerce and more specifically, the Census Bureau along with the Department of Housing and Development releases a report each month that indicates real estate developer's confidence in the economy. For this data, I calculated the monthly percent change in total new construction permits issued. Construction Spending The United States Department of Commerce's Census Bureau delivers a monthly report that details changes in construction spending. I used the seasonally adjusted total (private and public) construction numbers to calculate the monthly percent change. Manufacturers' Shipments, Inventories, and Orders The United States Department of Commerce and more specifically, the Census Bureau offers a report each month that measures information on manufacturer's activity with regard to shipments, inventories, and orders. The monthly percent change is offered in the report so no calculations were needed in my spreadsheet. Monthly and Annual Retail Trade The United States Department of Commerce's Census Bureau releases a monthly report that highlights retail and food service spending and is an indication of consumer health. The report is very detailed and provides gross numbers so I had to calculate the monthly percent change. New Residential Sales The United States Department of Commerce and more specifically, the Census Bureau along with the Department of Housing and Urban Development delivers a report each month that indicates how strong consumer confidence is in relation to housing. For this metric, I only considered ‘sold' houses and not ‘for sale' as that is a better indicator of consumer sentiment. Here is a look at the visualizations I created with the above data points:     The list on the Investopedia website continues but I decided to focus on the eight indicators listed above. Depending on where you like to find your economic information, there are different opinions on which indicators are best to consider. Here are a few more choices: https://www.moneycrashers.com/leading-lagging-economic-indicators/ https://dailyreckoning.com/top-10-market-indicators-of-economic-development/ http://www.aaii.com/investing-basics/article/the-top-10-economic-indicators-what-to-watch-and-why In any case, I hope you found this blog informative and that you make use of the spreadsheet I attached above for your own economic research. As I stated a couple of times earlier... Oracle is not suggesting any specific investment advice. Instead, we offer these items as tools for your own use. I enjoy working with numbers and feel strongly that they are best understood when represented visually. If you like what you see, take one of our Quick Tours or Simulations to learn more about Oracle Data Visualization. You can also find more resources on our Customized Content page. When you are ready to start a free trial, visit the Business Analytics website. Also, for current Oracle Data Visualization customers, be sure to visit the new Oracle Analytics Library and download sample files to take your analytics to a new level. Thanks for your time.

It's 2018! Time to get back to the gym, eat better, review your budget, analyze your investments, and all the other "better you" tasks that we neglect most of the year. And why not? It's a good...

Data Visualization

A Room with a Data Visualization

What does your dream home look like on the inside? Does it have a large kitchen? How about an open floor plan? How would you analyze a space to understand its optimal conditions? Many times, we encounter images of a geographic layout (like a floor plan of a shopping mall, museum, airport, or a demo hall) and wonder how great it would be to visualize all the associated data in that space. Where are the high-traffic areas? Which rooms hog the HVAC? With more and more sensors being installed indoors, it's no wonder companies are looking for answers based on a building's layout. The great news is that this is possible as a map layer in Oracle Data Visualization. In this blog, we will discuss about how to convert an image to a GeoJSON file format so that you can derive value from a space. Using a combination of Oracle tools, users can convert image of a layout into GeoJSON.  GeoJSON is a format for encoding a variety of geographic data structures using JavaScript Object Notation (JSON).  A GeoJSON object may represent a point (address or location), line strings (streets or boundaries), a region of space (counties or states), or other geographical features.  Features in GeoJSON contain a Geometry object and additional properties, and a Feature Collection contains a list of Features. Let's start with a snapshot that shows how a floor plan map layer extracted from an image looks like: Here's that floor plan's Custom Map Layer extracted from the previous image: As you can see, the GeoJSON layer allows us to see the space as a visualization that we can immediately identify for any sort of metrics. For example, we might derive that this home does not use its dining room as much as you might think if the darker hues indicate foot traffic. The GeoJSON layer also works will larger venues with exponentially more data to sift through such as a stadium, like Oracle Arena, and a few metrics associated with viewers seating at an event. One way to create these layers is using Oracle Map Builder and Oracle Map Editor tools. Here are step by step instructions on how to convert image to a map layer: Image to geoJSON Map Layer Here is a high-level view of steps involved in this process:  Create a Base Map using the Image file received, using Oracle Map Builder tool.  Create a GeoRaster theme using the Base Map using Map Builder tool Create a Base Map based on the GeoRaster Theme using Map builder tool Create a Geometry layer to show different regions on the map using Map Editor tool Create a Theme (a database table) based on the Geometry Layer using Map Builder tool Export the Theme to GeoJSON using Oracle Map builder In future blogs, we will cover how to achieve similar insights using Oracle Data Visualization and GeoJSON for a large-scale map and a custom shape file. As always, you can't uncover the best visualizations without trying it for yourself, visit www.oracle.com/goto/datavisualization to learn more about Oracle Data Visualization and get your free trial.

What does your dream home look like on the inside? Does it have a large kitchen? How about an open floor plan? How would you analyze a space to understand its optimal conditions? Many times, we...

Analytics Cloud

Pixel Perfect Reporting Comes to Oracle Analytics Cloud

For all you BI Publisher fans, here is the good news - BI Publisher is now available with Oracle Analytics Cloud. Oracle Analytics Cloud (OAC) is a scalable and secure public cloud service that provides a full set of capabilities to explore and perform collaborative analytics for your enterprise. You can take data from any source, explore with Data Visualization and collaborate with real-time data. It is available in three flavors - Standard Edition, Data Lake Edition, and Enterprise Edition, with Standard Edition giving the base ability to explore data, Data Lake Edition allowing insights into big data, and Enterprise Edition offering the full platter of data exploration, big data analytics, dashboard, enterprise reporting, Essbase etc. Refer to this documentation for additional details on different editions. With OAC 17.4.5 Enterprise Edition, now you can create pixel-perfect reports and deliver to a variety of destinations such as email, printer, fax, file server using FTP or WebDAV, Webcenter Content and Content & Experience Cloud. The version of BI Publisher here is 12.2.4.0. If you have used BI Publisher on premises, the experience will be very similar feature wise and look-and-feel wise, and therefore you will find it easy to get on-board. If you are new to BI Publisher, you will now be able to create pixel perfect and highly formatted business documents in OAC such as Invoices, Purchase Orders, Dunning Letters, Marketing Collateral, EFT & EDI documents, Financial Statements, Government Forms, Operational Reports, Management Reports, Retail Reports, Shipping Labels with barcodes, Airline boarding passes with PDF417 barcode, Market to Mobile content using QR code, Contracts with fine-print on alternate page, and Cross-tab reports. You can connect to a variety of data sources including BI Subject Areas, BI Analysis, and RPD; Schedule your report to run once or as a recurring job; and even burst documents to render in multiple formats and be delivered to multiple destinations. Can we move from BI Publisher on premises to BI Publisher on OAC? Well yes, you can. You will have to understand your on premises deployment and plan accordingly. If your data can be migrated to OAC, that will be the best otherwise you can plan to extend your network to Oracle Cloud allowing OAC to access your on premises data. The repository can be migrated by archiving and un-archiving mechanism. User data management will be another task where application roles from on premises will need to be added to OAC application roles. Details on this will be coming soon. Benefits of BI Publisher on OAC First of all, OAC comes with many great features around data exploration and visualization with advanced analytics capabilities. BI Publisher compliments this environment for pixel perfect reporting. So now you have an environment that is packed with Industry leading BI products providing an end-to-end solution for an enterprise.  Managing Server instances will be a cakewalk now, with just a few clicks you will be able to scale up/down to a different compute shape or scale out/in to manage nodes in the cluster, saving you both time and money. Many self-service features to manage reports and server related resources. What's new in BI Publisher 12.2.4.0? BI Publisher in OAC includes all features of 12.2.1.3 and has the following new features in this release: Accessible PDF Support (Tagged PDF & PDF/UA-1) New Barcodes - QR Code and PDF417 Ability to purge Job History Ability to view diagnostic log for online report Widow-orphan support for RTF template   So why wait? You can quickly check this out by creating a free trial account here. Once you log in, you are in OAC home page. To get to BI Publisher you need to click on the Page Menu on right side top of the page and then select the option "Open Classic Home". BI Publisher options are available under the Published Reporting section in the classic homepage. For further details on pixel-perfect reporting, check the latest Oracle Analytics Cloud Documentation. Guest author, Pradeep Sharma is a Senior Principal Product Manager based in India.

For all you BI Publisher fans, here is the good news - BI Publisher is now available with Oracle Analytics Cloud. Oracle Analytics Cloud (OAC) is a scalable and secure public cloud service that...

4 Machine Learning Techniques You Should Know

Previously, we discussed what machine learning is and how it can be used. But within machine learning, there are several techniques you can use to analyze your data. Today I’m going to walk you through some common ones so you have a good foundation for understanding what’s going on in that much-hyped machine learning world. If you are a data scientist, remember that this series is for the non-expert. But first, let’s talk about terminology. I’ll use three different terms which I’ve seen used interchangeably (and sometimes not accurately): techniques, algorithms, and models. Let me explain each one. A technique is a way of solving a problem. For example, classification (which we’ll see later on) is a technique for grouping things that are similar. To actually do classification on some data, a data scientist would have to employ a specific algorithm like decision trees (though there are many other classification algorithms to choose from). Build a data lake for free and test out machine learning techniques Finally, having applied an algorithm to some data, the end result would be a trained model which you can use on new data or situations with some expectation of accuracy. It should all be clearer after these examples, so read on. Machine Learning Technique #1: Regression If you’re looking for a great conversation starter at the next party you go to, you could always start with “You know, machine learning is not so new; why the concept of regression was first described by Francis Galton, Charles Darwin’s half-cousin, all the way back in 1875”. Of course, it will probably be the last party you get an invite to for a while. But the concept is simple enough. Francis Galton was looking at the sizes of sweet peas over many generations. We know that if you selectively breed peas for size, you can get larger ones. But if you let nature take its course, you see a variety of sizes. Eventually, even bigger peas will produce smaller offspring and “regress to the mean”. Basically, there’s a typical size for a pea and although things vary, they don’t “stay varied” (as long as you don’t selectively breed). The same principle applies to monkeys picking stocks. On more than one occasion there have been stock-picking competitions (WSJ has done them, for example) where a monkey will beat the pros. Great headline. But what happens next year or the year after that? Chances are that monkey, which is just an entertaining way of illustrating “random,” will not do so well. Put another way, its performance will eventually regress to the mean. What this means is that in this simple situation, you can predict what the next result will be (with some kind of error). The next generation of the pea will be the average size, with some variability or uncertainty (accommodating smaller and larger peas). Of course, in the real world, things are a little more complicated than that. Find out what the other machine learning techniques are by visiting Oracle's Big Data blog. Guest author, Peter Jeffcock is an Oracle Senior Principal Product Marketing Director focused on Big Data and Cloud Tech Database  

Previously, we discussed what machine learning is and how it can be used. But within machine learning, there are several techniques you can use to analyze your data. Today I’m going to walk you...

Data Visualization

Which Machine Learning Model Is the Right One for Me?

Machine learning has taken the data analytics world by storm. From empowering supply chain automation to presenting customized offers to detecting fraud, ML has certainly enhanced predictive analytics and is now becoming a critical element in demand planning. In the world of machine learning, quite often we want to create multiple prediction models, compare them and choose the one that is more likely to give results that satisfy our criteria and requirements. But all machine learning is not designed to accomplish the same objectives. One question we often get asked is, "How can I tell which machine learning model is right for my enterprise?" These criteria can vary, sometimes models which have better overall accuracy are chosen. Sometimes models that have at least Type I and Type II errors (False Positive and False Negative Rates) are chosen, and in some cases models that return results faster with an acceptable level of accuracy are chosen (even if not ideal), and there are more such criteria. Oracle Data Visualization has multiple machine learning algorithms implemented out of the box for each kind of prediction and classification required. Because of this, users have the luxury to create more than one model using these algorithms, or using different fine-tuned parameters to those algorithms or using different input training datasets and then, choose the best model out of them. But to choose the best model, we need to compare two models and weigh them against our own criteria. So how to compare these models? Where can we find the data in Oracle Data Visualization to do this comparison?  In our previous blog, we talked about related datasets and model quality details they contain. Here is an example of how to use these related datasets to compare two models based on a criterion: Choose a model with least Type II (False Negative Rate) errors. The video below explains the process of using these related datasets to compare two models:   Of course, you can't test out these features for yourself unless you get your hands on it. To learn more about these and other machine learning features, download Oracle Data Visualization Desktop and tell your boss how much you like it.

Machine learning has taken the data analytics world by storm. From empowering supply chain automation to presenting customized offers to detecting fraud, ML has certainly enhanced predictive analytics...

Data Visualization

Add Machine Learning For an Effective Marketing Campaign

What do most effective marketing campaigns have in common? A clear purpose? A unique angle? What about machine learning? Let us suppose that a company wants to perform a direct marketing campaign to get a response (like a subscription or a purchase) from users. It wants to run a marketing campaign for around 10,000 users out of which only 1,000 users are expected to respond. But the company doesn't have a budget to reach out to all the 10,000 customers. To minimize the cost, the company wants to reach out to the smallest number of customers as possible but at the same time reach out to most (user defined) of the customers who are likely to respond. The company can create machine learning models to predict which users are likely to respond and with what probability. Then the question comes which model should I choose? Which machine learning model is likely to give me the optimal number of respondents with the fewest number of original respondents as possible? A cumulative Gains and Lift chart can answer these questions. In this technical blog, we will talk about a Cumulative Gains chart and Lift chart created in Oracle Data Visualization for Binary Classification machine learning models and how these charts are useful in evaluating the performance of a classification model. A Cumulative Gains and Lift chart is a measure of the effectiveness of a binary classification predictive model calculated as the ratio between the results obtained with and without the predictive model. Gains and Lift charts are popular techniques in direct marketing. They are visual aids for measuring model performance and contain a lift curve and baseline. The effectiveness of a model is measured by the area between the lift curve and baseline. The greater the area between lift curve and baseline, the better the model. One academic reference on how to construct these charts can be found here. Sample Project for Cumulative Gains and Lift Chart Computation The Oracle Analytics Library has an example project for this that was build using Marketing Campaign data of a bank. The charts below demonstrate this.   Scenario: This marketing campaign aims to identify users who are likely to subscribe to one of their financial services. They are planning to run this campaign for close to 50,000 individuals out of which only close to 5,000 people (about 10 percent) are likely to subscribe to the service. The marketing campaign data is split into training and testing data. Using training data, we created Binary classification machine learning model using a Naive Bayes classifier to identify the likely subscribers along with prediction confidence. The actual values (i.e., whether a customer actually subscribed or not) is also available in the dataset. Now they want to find out how good the model is in identifying most number of likely subscribers by selecting a relatively small number of campaign base (i.e., 50,000). Machine learning models are applied to the test data and receive the Predicted Value and Prediction Confidence for each prediction. This prediction data and Actual outcome data is used in a data flow to compute cumulative gain and lift values. How to Interpret These Charts and How to Measure Effectiveness of a Model: A Cumulative Gains chart depicts cumulative of the percentage of Actual subscribers (Cumulative Actuals) on the Y-Axis and the Total population (50,000) on the X-Axis in comparison with random prediction (Gains Chart Baseline) and Ideal prediction (Gains Chart Ideal Model Line). This depicts all the 5,000 likely subscribers are identified by selecting first 5,000 customers sorted based on Prediction Confidence for Yes. What the cumulative Actuals chart says is that by the time we covered 40 percent of the population we already identified 80 percent of the subscribers and by reaching close to 70 percent of the population we have 90 percent of the subscribers. If we are to compare one model with another using cumulative gains chart model with a greater area between the Cumulative Actuals line and Baseline is more effective in identifying a larger portion of subscribers by selecting a relatively smaller portion of the total population. The Lift Chart depicts how much more likely we are to receive respondents than if we contact a random sample of customers. For example, by contacting only 10 percent of customers based on the predictive models we will reach 3.20 times as many respondents as if we use no model. The Max Gain shows at which point the difference between cumulative gains and baseline is maximum. For a Naive Bayes classifier model, this occurs when population percentage is 41 percent and maximum gain is 83.88 percent. How to Compare Two Models Using Cumulative Gain and Lift Chart in Oracle Data Visualization: To compare how well two machine learning models have performed we can use Lift Calculation dataflow (included in the .dva project) as a template and plug in the output of Apply Model data flow as a data source/input to the flow. Add the output dataset of Lift Calculation to the same project and add columns to the same charts as shown above to compare. Please note that the data flow expects dataset to contain these columns (ID, ActualValue, Predicted Value, Prediction Confidence). This is how it will look like when we compare two models using same visualizations: Of course, you can't test out these features for yourself unless you get your hands on it. To learn more about the machine learning feature download Oracle Data Visualization Desktop and tell your boss how much you like it.

What do most effective marketing campaigns have in common? A clear purpose? A unique angle? What about machine learning? Let us suppose that a company wants to perform a direct marketing campaign to...

Analytics Cloud

Master Your QBR Using Oracle Analytics Cloud

How would you like to master your next quarterly business review? Salespeople all have to go through this innocuous-sounding thing called a quarterly business review—QBR for short. It sounds reasonable: every quarter, you review your business. No problem.  Turns out however, even when you have a fabulous quarter and crush your numbers, they still come at you with pointed questions (e.g. forecast accuracy? sandbagging?). When you have a not-so-great quarter, then the questions really start popping. The sessions can be intense, so she who best masters her data, wins. What would you need to have to do that?  I have insight into this whole QBR thing. For some reason, I've spent a big chunk of my adult life around sales professionals.  I didn't see that coming. When I graduated with an electrical engineering degree, I thought I'd spend my time building power plants and substations. And I don't mean just for work, although that's a large part of it.  I also have very dear friends of many years who happen to be successful, charismatic sales leaders. So, you might say I've got a decent handle on the species. Now, imagine now that you walk into that next QBR, and everyone is there with their slides, and their spreadsheets, and their reports.  And instead of dragging through an interrogation, the questions and discussions surge up to a new, interesting, potentially very profitable, direction.  Wouldn't you like to be able to surf that wave? We advise using a cloud-based analytics platform for success. Let's review how this is done. There are five areas to cover if you want to successfully master your data—such as the data you review in a QBR—and create a data-driven sales organization Integrate any data: sales leaders can gain full visibility by blending their territory, conversion, and sales rep data (for example) with performance data, and with data from yet other applications. Visualize any data: as a customer recently put it—you want to add the color and the life to your data, and be able to tell stories that other people can get excited about. You probably aren't doing that with rows and columns in Excel. Access any data from anywhere: mobile access is key.  How many times did you look at your phone today?  Nuff said. Model any outcome: count the number of what-if's you here in your next QBR.  Just count 'em. Manage pipeline: aka the Holy Grail.  Really understanding pipeline coverage can illuminate the most important trends and prioritize changes In the video below, we show Oracle Analytics in action with a simple QBR dashboard.   If you want to run your own data through this process, we're offering a free trial of Oracle Data Visualization Cloud Service. If you need more convincing, or need to persuade your boss, we have published a data sheet that identifies the features and benefits of using this product.   And do feel free to re-share and add your own interesting, funny, memorable QBRs sessions that you've experienced or witnessed over the years.

How would you like to master your next quarterly business review? Salespeople all have to go through this innocuous-sounding thing called a quarterly business review—QBR for short. It...

Data Visualization

Predict Bike-Sharing Needs Using Oracle Data Visualization

Bicycles are quickly becoming popular for commuters in some metro areas for those who want to ditch their cars or support a green lifestyle. More than 50 cities offer some type of casual bike rental or bike-sharing program that's easy to use with the swipe of a credit card or click on an app. While it's quite the convenience for city dwellers, the companies supplying the bikes have a more complex problem: How to predict the needs of the customers and make sure there are enough bikes in any given location. In the video below, we demonstrate how to use machine learning algorithms in Oracle Data Visualization to predict expected bike rentals for a bike renting company which wants to prepare itself for the upcoming demand.    The latest version of Oracle Data Visualization (v4.0) introduces a machine learning feature that lets users train or build their own machine learning models. These models can perform various prediction and classification operations like Numeric Prediction, Classification, and Clustering. The plugin example seen in the video can be downloaded from Oracle Analytics Store. The name of the project is Example DV Project: Bike Rental Prediction: To predict the demand, we use one of the most commonly used machine learning techniques: Numeric Prediction. Numeric Prediction is a common requirement in the business world. Classic examples of this prediction include a sales forecast, a demand prediction, and a stock price prediction. Oracle DV comes loaded with multiple Numeric prediction algorithms and users can choose any one of these algorithms based on the need. List of algorithms include: Linear Regression, Elastic Net Linear Regression and Classification and Regression Tree(CART) for Numeric prediction. Here is a snapshot showing list of algorithms in Oracle DV: Users can develop their own custom Python/R scripts that can perform Numeric prediction and upload it to Oracle Data Visualization. Uploaded scripts can be invoked from data flows in Oracle DV. In case you are interested there is a short video showing how to upload format and upload custom Python scripts. To learn more about the machine learning feature download Oracle Data Visualization Desktop and feel free to play around with it.

Bicycles are quickly becoming popular for commuters in some metro areas for those who want to ditch their cars or support a green lifestyle. More than 50 cities offer some type of casual bike rental...

Data Visualization

Predict Attrition Using Machine Learning

Look around the office. Chances are half of your coworkers are looking for a new job, according to the latest Gallup report. High attrition numbers could be frightening for a human resource manager. However, it is possible to predict which employees have their eye on the door long before they hand in their two-week notice. Besides, knowing who might be at risk of leaving may even help HR retain the best talent. In this blog, we are going to focus on how to predict attrition using binary classification algorithms and show how to use those inbuilt algorithms for addressing a real-life, common question for any organization. In this case: how can we identify which employees are likely to quit? Our latest release of Oracle Data Visualization has built-in machine learning features. This means users can now build their own models from training data and use these trained models for prediction and classification. The platform comes equipped with a host of machine learning algorithms that can perform numeric prediction, multi and binary classification, and clustering. In addition, you can build your own custom model scripts for training and scoring. Before we venture any further let us try to understand briefly what we mean by binary classification. Binary classification is a technique of classifying records/elements of a given dataset into two groups on the basis of classification rules for ex: Employee Attrition Prediction whether the employee is expected to Leave or Not Leave. Leave and Not Leave are the two different groups. These classification rules are generated when we train a model using training dataset which contains information about the employees and whether the employee has left the company or not. Oracle Data Visualization ships with multiple algorithms that can perform Binary classification. Here is a snapshot showing list of inbuilt algorithms in Oracle Data Visualization that can perform binary classification: Users can also upload their own Python/R scripts (with appropriate tags) which can perform Binary classification and these custom algorithms will show up in the list and can be used for prediction. Now let us see how one of these inbuilt algorithms can be used to predict Employee Attrition prediction. Will this set of employees leave or not? Yes or No? The recording below explains the process of model creation as well as prediction process (i.e. scoring using created a model). Of course, seeing is believing. If you like what you see and you want to try it for yourself, visit www.oracle.com/goto/datavisualization to learn more about Oracle Data Visualization and get your free trial.

Look around the office. Chances are half of your coworkers are looking for a new job, according to the latest Gallup report. High attrition numbers could be frightening for a human resource manager....

Data Lab

Data and a Dog Called Molly

Molly is a golden retriever belonging to Paul Sonderegger, Oracle's Big Data Strategist. In a series of short 3-5 minute webcasts, Paul uses Molly as a neat way of introducing the topic and explaining in simple terms the three principals of Data Capital, and how companies can exploit the value of data capital to drive new business opportunities and transformation. 1. Data comes from Activity. Pet suppliers all want Molly’s data because it isn’t just a record of what she did today. Or didn’t. It’s raw material for creating new digital products and services. And if you’ve got the data, your rival doesn’t. This same battle surrounds every plane, train, automobile and anything else in the Internet of things. Plus, every kind of financial trade, insurable risk, and kind of medical care.    2. Data tends to make more data. Take a company that collects Molly’s – and all of her friends’- data through GPS trackers and has a mobile app that alerts when a dog wanders out of its safe zone. They can see which features get used. Are there differences between owners of different breeds? In the city versus the country? These data-fuelled feedback loops make it faster and easier for the company to learn what their customers like and don’t like, intensifying competition. But even this isn’t the real action. Algorithms are where it’s at – data-fueled learning for people and machine-learning beyond human scale plays out in e-commerce recommendations and online ads, as well as fraud detection in payment systems, inventory allocation in robotic warehouses, and dynamic pricing in parking meters. Companies must develop both a data lab and the discipline of rapid experimentation with it.    3. Platform tends to win. Molly is sweet. And a battleground. Companies are fighting over her data in order to be the platform for digital pet services. But what does that mean, exactly? All the chew toy, dog vest, and grooming supply makers. Plus the vets, animal hospitals, dog boarders, groomers and more. A platform is the thing that sits in between them and makes it easier, cheaper, or just more convenient for the two sides to do business. It reduces transaction costs in a two-sided market. With the capture and use of more data in more daily activities, platform competition is coming for industries that haven’t seen it before. This has already happened in book retail, urban transportation, and job hunting. Industries like insurance, healthcare, and industrial manufacturing have to get ready.  These videos are recommended viewing for you to start a high-level conversation about the value of exploring and exploiting Big Data, and connecting Big Data Analytics to strategic digital transformation projects. And once you've started that conversation, check out Oracle Analytics Cloud to find out how you can tell your own business story—to the power of data.

Molly is a golden retriever belonging to Paul Sonderegger, Oracle's Big Data Strategist. In a series of short 3-5 minute webcasts, Paul uses Molly as a neat way of introducing the topic and explaining...

Machine Learning

3 Ways Machine Learning Will Transform Finance

Machine learning is everywhere. At home, it helps power personalized shopping apps, suggests personalized entertainment experiences, manages and monitors self-driving cars, supports virtual assistants, and improves navigation. At the office, it helps businesses develop the next best offer, recruit top-notch candidates, detect fraud, automate supply chains, and boost data center efficiency. Yet, corporate finance leaders are asking deeper questions which require more advanced analytics systems. "Why can you ask your mobile phone for directions to find the nearest restaurant but you can’t ask your system how revenues are trending in Italy?" Oracle Vice President of Product Strategy for Big Data Analytics, Rich Clayton said during a recent Financial Executives International (FEI) webcast. "Why is that your systems don't understand your processes? Why do you spend so much time explaining simple variances when much can be automated?" Clayton discussed how machine learning is transforming businesses and specifically the finance departments. Clayton collected tried-and-true use cases for machine learning in Finance over the year and knows from his experience how best to prepare your organization for big changes ahead in a way that seems very simple on the surface. Clayton says machine learning helps financial service leaders tackle three areas at once: Finance Productivity -- so users can do their job more efficiently  Enterprise Protection -- to support employee safety, and security issues  Process Transformation -- to best optimize operations and coordinate with partners and suppliers "Rather than hiring expensive programmers to write ETL or data loading programs, machines will recommend how to combine data," Clayton said. So how does this work in the real world? Clayton suggested a couple examples: A large bank used machine learning to analyze its collection activities and learned it could eliminate more than 40 percent of customer calls with better outcomes. A global retailer used advanced machine learning to forecast customer demand cutting forecast error in half. A telecom company found that its machine learning yielded a 75x reduction in "false alarms" for churn and instead focused its resources on those truly at risk of leaving. Because machine learning techniques are designed to learn as they go, Clayton suggests that business analytics designers look at Oracle's suite of analytics products to help with autonomous data discovery to help guide users toward areas of interest they may have passed over. Self-learning contextual insights anticipate questions and infuse data-based insights into daily activities. And what machine learning discussion would be completed without mentioning voice-activated analytics. If words can be expressed as values, interpreting a semantic layer of data through natural language processing can enable on-the-fly queries and auto-complete expressions on any device with a microphone. The on-demand webcast is now available as well as more information about Oracle Analytics for Finance.

Machine learning is everywhere. At home, it helps power personalized shopping apps, suggests personalized entertainment experiences, manages and monitors self-driving cars, supports virtual...

Analytics Cloud

You Might Be a Finance Leader If... Analytics Edition

US-based comedian, Jeff Foxworthy, has this whole shtick where he lists a bunch of humorous "If" statements based on a certain demographic followed by the hilarious catchphrase, "You might be a redneck." Personally, I stink at stand-up, but it occurred to me that finance leaders have some humorous cliches. And while some will let you crack a smile, most could be simply solved with data-driven analytics. So, I'm putting my comedy hat on and going to go with the following: If you find a well-constructed spreadsheet a thing of beauty (and immediately try to improve it) ... You might be a finance leader If the words "scenario modeling" make you smile… You might be a finance leader If you know that Monte Carlo simulation is not a fake city in Monaco... You might be a finance leader If every time you volunteer in your community, they nominate you to manage the budget... You might be a finance leader If you are happiest when you can bring financial data to heel, and make it work for you and your organization... You might be a finance leader If the above pegged you as a finance leader, let's talk about using those mad skills to transform your organization into a data-driven powerhouse. Your first question might be, "Why?"  Why is being data-driven so important to the finance organization? Very simply, because data is quickly becoming the world's most valuable resource, and is a driver of growth and change. The good news is that when you succeed, you can see a great advantage as measured by revenue growth and EBITDA performance, reduced expenses, and productivity benefits. Even better, is that Accenture predicts that by 2020, the best of the best (that's you) will have figured out how to spend more time on analytics to support decision making, predictive analytics and performance management Making sense of your business data is critical to your organization's success. Using visual analytics to provide a deeper understanding of this data can be a game changer. In the example below, we're going to look at how you would find the root cause of an unexpected drop in net income, and how you would easily explain the results of your analytics.   Allow me to be blunt: if you try to do this just with spreadsheets and a bunch of tools, you're going to find it hard going. So, don't do that.  Instead, to help you get there, we've developed a multi-step process, with a checklist to guide you on your way. Find out more in this business brief or try Oracle Analytics Cloud for yourself Oh, and you totally get to keep your spreadsheets.

US-based comedian, Jeff Foxworthy, has this whole shtick where he lists a bunch of humorous "If" statements based on a certain demographic followed by the hilarious catchphrase, "You might be a...

Analytics Cloud

Oracle Analytics Cloud Tops Forrester Wave

An essential part of any business is to be able to easily access its data analytics in the cloud so that it can gather business intelligence quickly. Whether it is part of an overall cloud strategy or to quickly getting BI projects off the ground, features like querying, reporting, and data visualization are the foundation to any successful analytics strategy. However, it's the new types of human-to-machine interactions, insights-to-execution capabilities, and other innovative analytics cloud capabilities that dictate which providers lead the pack. That's why Oracle topped "The Forrester Wave: Enterprise BI Platforms with Majority Cloud Deployments, Q3 2017" where it outperformed Microsoft, Amazon Web Services (AWS), and Salesforce. In its review, Forrester researchers reported that Oracle has the strongest current offering coupled with its large market presence. The software analyst firm surveyed 10 different companies that offer cloud-based analytics products; some of whom also offer complete or partial on-premises options. Cloud delivery has become essential to deploying business intelligence tools. They are becoming a critical capability in some enterprise's cloud-first or cloud-only strategy. Other companies use their cloud-based BI apps to provide the most up-to-date features for customers as well as having the flexibility to scale quickly. Forrester researchers recognized this trend and point out that Oracle continues to be the analytics platform choice for any scale of deployment, from workgroup to enterprise, cloud, or hybrid. From the report: "With Oracle Analytics Cloud, Oracle supports the complete spectrum of BI needs at enterprise scale, ranging from self-service analytics to complex requirements leveraging Essbase's powerful MOLAP [multidimensional online analytical processing] engine. The vendor can also support hybrid scenarios. Comprehensive advanced analytics capabilities and natural language functions round out the offering. Customers of Oracle's other applications (e.g., CRM or ERP) can gain additional benefits from the Common Enterprise Information Model, as the semantic layer extends across products. Existing Oracle customers will also welcome the increased licensing flexibility." Additionally, Forrester's researchers acknowledged the importance of Oracle Day By Day—our mobile application for analytics—calling it, "a fresh start on mobile, focusing on users' information needs rather than on simple BI delivery." You can download the whole report from the Forrester Reports website. And of course, you can see what the fuss is all about for yourself. Visit Oracle Analytics Cloud and get your free trial.

An essential part of any business is to be able to easily access its data analytics in the cloud so that it can gather business intelligence quickly. Whether it is part of an overall cloud strategy or...