Friday Dec 20, 2013

Year in Review – Oracle Business Analytics in 2013

2013 was a busy year for Oracle Business Analytics and as it comes to end, we wanted take a moment to thank all of our customers and partners for another great year together. At Oracle, we enjoy a good year-end recap so here is a look at Oracle Business Analytics top 10 moments in 2013 (in no particular order).  Relax and take a stroll down memory lane with us!

10. The Release of Oracle Endeca Information Discovery 3.1 – The latest release of Oracle Endeca Information Discovery 3.1 incorporates new enterprise self-service discovery capabilities for business users, allowing them to easily make information-based business decisions with greater success, safety and confidence. Learn more about Oracle Endeca

9. Big Data at Work Webcast Series – 5 webcasts, 1000’s of attendees with featured guest speakers from Dell, Passoker, Cloudera, Delphi, as well as MIT’s Andrew McAfee. View on-demand

8. Oracle Exalytics T5-8 Scales Up to Deliver Customers with Analytic Insights Oracle Exalytics In-Memory MachineT5-8, the new engineered system with 4TB of memory per machine, delivers extreme performance for business intelligence (BI) and enterprise performance management (EPM) applications, helping organizations drive better efficiency by speeding answers to complex business scenarios. Learn more about Oracle Exalytics

7. Mark Hurd - Oracle OpenWorld 2013 Keynote – Oracle President, Mark Hurd, gives his keynote on transforming businesses with Big Data and Analytics at Oracle OpenWorld 2013. Watch the Video

6. Oracle Exalytics Strong Customer Adoption– Sodexo, SoftBank, Cablemas, WorleyParsons, Santos, Zagrebacka banka , Cablevisón, Avago Technologies, United Supermarkets, Immonet GmbH, Nilson Group AB, Siemens Healthcare, Pinellas County, Ministero del Lavoro were among the many organizations recognized at Oracle OpenWorld 2013 for leveraging Oracle Exalytics to deliver extreme performance for their mission-critical BI and enterprise performance management (EPM) applications. Learn more about Oracle Exalytics

5. The Release of Oracle BI Mobile App Designer A new design tool with which business users can easily create stunning and interactive analytical applications for use on any major mobile device. With this release, Oracle adds major innovations to Oracle Business Intelligence, extending the capabilities of the Oracle BI Mobile solution, and reinforcing Oracle’s commitment to empowering organizations to stay connected to their businesses with real-time insights while on the go. Learn more about Oracle BI Mobile App Designer

4. Oracle Exalytics X3-4 Powers Real-Time Analytical Insights – The new system features significant software enhancements and hardware updates, dramatically expanding the capabilities of the industry’s first high-speed engineered system for business analytics. Learn more about Oracle Exalytics

3. The release of Oracle Business Intelligence Applications 11.1.1.7.1 Completely redesigned to increase implementation productivity, the new release incorporates significant enhancements across the entire BI Applications product line and introduces new in-memory analytic applications. Learn more about Oracle BI Applications

2. The Oracle Business Intelligence Foundation Suite Release 11.1.1.7 Delivers significant enhancements to usability, mobility, user experience and Big Data integration, enabling organizations to analyze critical information and get the intelligence they need to optimize their business. Learn more about Oracle BI Foundation Suite

1. Oracle Positioned in Leaders Quadrant for BI and Analytics Platforms by Gartner – Oracle has been named Leader in Business Intelligence for the seventh consecutive year. Read the report. Plus, Oracle customer, Land O Lakes, wins Gartner BI and Analytics Excellence award for their innovative use of Oracle Endeca Information Discovery. Read the story

Historic Moment - Oracle Team USA puts Big Data and Analytics to work and fuels the most dramatic victory in the history of the America's Cup. Watch the Video

Friday May 18, 2012

The Art of the Possible with Business Analytics

It has been established beyond doubt that data and its analysis can have a huge impact on an organization’s top line and bottom line. Business Analytics helps organizations deliver better business performance in two ways – by optimizing business processes and by helping to innovate. Optimization helps organizations be efficient and effective by taking inefficiencies out of the business processes and focusing on the high impact opportunities. Innovation on the other hand helps organizations by uncovering new customer segments, new product categories, new markets, new business models etc.

The styles of analyzing data are many fold from answering questions like “what is going on?” to “why are the things the way they are?” to “what will happen if I do X or Y?” to “what does the future look like?” Broadly speaking the styles of analytics can be classified into three categories:

·         Exploratory Analysis: The objective of exploratory or investigative analysis is exploration and analysis of complex and varied data – whether structured or unstructured for information discovery.  This style of analysis is particularly useful when the questions aren’t well formed or the value and shape of the data isn’t well understood.

·         Descriptive Analytics: The objective of this style of analysis is to answer historical or current questions like what is going on. why are the things the way they are?. This is the most common style of analysis and here the questions as well as the value and shape of data are well understood.

·         Predictive Analysis: Predictive analysis aims at painting a picture of the future with some reasonable certainty.

So, what’s art of possible with business analytics? It’s the application of the above three styles of analytics to a business scenario for better insights, decisions and results. Let’s try and explain this with an example. Consider this scenario:

You are a Financial Services firm e.g. a large bank and are trying to improve profitability. You read Larry Seldon’s book titled “Angel Customers and Demon Customers” and agree with the findings that 20% of your top customers bring in 80% of the profits and would like to manage you business as a portfolio of customers as opposed to portfolio of products. So, how do you do that? The answer is business analytics.

You can start by using descriptive analytics techniques like operational reports, ad-hoc query, dashboards etc. on data collected from different sources like sales, customer service etc. to determine the profitability of each customer. You can then use predictive analysis techniques like data mining, statistical analysis to further enrich your customer data into profitability segments like high, medium, low and loss making customers. Finally, you can choose different customer service channels like personal banker, phone or ATM to cost effectively serve you customers e.g. a high profitability customer can be served by a personal banker free of charge but if the loss making customer wants a personal banker there will be a charge. Once you have implemented such programs you can use exploratory analysis to gauge the sentiment across social media channels like Facebook and Twitter to see if the programs are working as desired. Better yet you may come up with new innovative business models like mobile banking or online only banking to improve profitability.

That’s the art of possible powered by business analytics. Stay tuned, I intend to publish more examples from different industries to show the art of possible with business analytics.

Tuesday Feb 07, 2012

Big Data Analytics – The Journey from Transactions to Interactions

Big Data Defined

Enterprise systems have long been designed around capturing, managing and analyzing business transactions e.g. marketing, sales, support activities etc. However, lately with the evolution of automation and Web 2.0 technologies like blogs, status updates, tweets etc. there has been an explosive growth in the arena of machine and consumer generated data. Defined as “Big Data”, this data is characterized by attributes like volume, variety, velocity and complexity and essentially represents machine and consumer interactions.

Case for Big Data Analysis

Machine and consumer interaction data is forward looking in nature. This data available from sensors, web logs, chats, status updates, tweets etc. is a leading indicator of system and consumer behavior. Therefore this data is the best indicator of consumer’s decision process, intent, sentiments and system performance. Transactions on the other hand are lagging indicators of system or consumer behavior. By definition leading indicators are more speculative and less reliable compared to lagging indicators; however, to predict the future with any confidence a combination of both leading and lagging indicators is required. That’s where the value of big data analysis comes in, by combining system and consumer interactions and transactions, organizations can better predict the consumer decision process, intent sentiments and future system performance leading to revenue growth, lower costs, better profitability and better designed systems.

So, which business areas will benefit via big data analysis? Think of areas where decision-making under uncertainty is required. Areas like new product introduction, risk assessment, fraud detection, advertising and promotional campaigns, demand forecasting, inventory management and capital investments will particularly benefit by having a better read on the future.

 Big Data Analytics Lifecycle

The big data analytics lifecycle includes steps like acquire, organize and analyze. Big data or machine/consumer interaction data is characterized by attributes like volume, velocity and variety and common sources of such data include sensors, web logs, status updates and tweets etc. The analytics process starts with data acquisition. The structure and content of big data can’t be known upfront and is subject to change in-flight so the data acquisition systems have to be designed for flexibility and variability; no predefined data structures, dynamic structures are a norm. The organization step entails moving the data in well defined structures so relationships can be established and the data across sources can be combined to get a complete picture. Finally the analysis step completes the lifecycle by providing rich business insights for revenue growth, lower costs and better profitability. Flexibility being the norm, the analysis systems should be discovery-oriented and explorative as opposed to prescriptive.

Getting Started

Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data sources and analyzes them alongside your existing data to find new insights and capitalize on hidden relationships. Learn how Oracle helps you acquire, organize, and analyze your big data by clicking here.

Thursday Nov 17, 2011

Introducing the Industry's First Analytics Machine, Oracle Exalytics

Analytics is all about gaining insights from the data for better decision making. The business press is abuzz with examples of leading organizations across the world using data-driven insights for strategic, financial and operational excellence. A recent study on “data-driven decision making” conducted by researchers at MIT and Wharton provides empirical evidence that “firms that adopt data-driven decision making have output and productivity that is 5-6% higher than the competition”. The potential payoff for firms can range from higher shareholder value to a market leadership position.

However, the vision of delivering fast, interactive, insightful analytics has remained elusive for most organizations. Most enterprise IT organizations continue to struggle to deliver actionable analytics due to time-sensitive, sprawling requirements and ever tightening budgets. The issue is further exasperated by the fact that most enterprise analytics solutions require dealing with a number of hardware, software, storage and networking vendors and precious resources are wasted integrating the hardware and software components to deliver a complete analytical solution.

Oracle Exalytics In-Memory Machine is the world’s first engineered system specifically designed to deliver high performance analysis, modeling and planning. Built using industry-standard hardware, market-leading business intelligence software and in-memory database technology, Oracle Exalytics is an optimized system that delivers answers to all your business questions with unmatched speed, intelligence, simplicity and manageability.

Oracle Exalytics’s unmatched speed, visualizations and scalability delivers extreme performance for existing analytical and enterprise performance management applications and enables a new class of intelligent applications like Yield Management, Revenue Management, Demand Forecasting, Inventory Management, Pricing Optimization, Profitability Management, Rolling Forecast and Virtual Close etc.

Requiring no application redesign, Oracle Exalytics can be deployed in existing IT environments by itself or in conjunction with Oracle Exadata and/or Oracle Exalogic to enable extreme performance and best in class user experience. Based on proven hardware, software and in-memory technology, Oracle Exalytics lowers the total cost of ownership, reduces operational risk and provides unprecedented analytical capability for workgroup, departmental and enterprise wide deployments.


Click here to learn more about Oracle Exalytics.

 

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