All data tells a story. The challenge is to find that story by analyzing business data in dozens of ways, using spreadsheets, applications, desktop tools, long-standing data warehouses, and business intelligence software. That means you are probably managing a variety of different dimensions—specific requirements for accessing various data sources, complexities around understanding visualizations, and of course, the associated costs. At the same time, everyone wants more innovation faster—all without sacrificing correct and consistent results.
Companies just like yours seek solutions that offer easy access to any data, enabling you to make the best use of your entire data story, no matter where it is. What’s even more important is having a variety of options for intelligent analysis that isn't overwhelming to deploy and manage. The result creates an effective way to engage more people in analysis and extend your organization's expertise. After all, you can have all the bells and whistles a robust platform should offer—but if only a select few can figure out how to use them, what are you really gaining?
So, the question you face is how to find and tell that story. How will you get the flexibility you need together with the structure to drive accuracy, and the speed to disrupt your competitors? Here are four values that can apply to your business analytics strategy.
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Every company has a story to tell with its data. There are customer transactions, marketing metrics, collaboration with partners and suppliers, innovations in business processes, and the enablement of growth and personnel efficiencies. And while the data is abundant, useful insights are sometimes difficult to develop.
Data visualization software makes these complex ideas engaging, meaningful, and easy to understand. With just a few clicks, business managers can add, analyze, and share new insights. Savvy executives use this technology to improve their strategies with facts and get action from their ideas. The important thing is that data visualization can help us see information differently—and by doing so, we make better decisions in ways that spreadsheets or traditional static business intelligence cannot deliver.
One of the benefits of modern business analytics software is that more people can gain a deeper understanding of the problems they are trying to solve without having to be data scientists.
Imagine if workers at a manufacturing plant could know when and why a machine would fail before it happened. How much would that impact production? Data modeling and predictive analytics programs can help answer these types of questions. Previously, this type of insight could only be delivered by data scientists working with sophisticated algorithms. Now, business managers with no data science experience can easily look at data, ask "what if" questions, identify hidden relationships, and convey these insights to the rest of the business.
Simplifying these forecasting and modeling scenarios enables business users to quickly model complex business questions. Organizations can then define a dimensional view of their business—and provide business users with new levels of self-service to access, navigate, and gain actionable insight into critical business issues. All of this is enabled through cloud-based analytics platforms.
Analytics implementations have matured in uneven ways, making it clear that there's no single formula for success, especially with hybrid-cloud and on-premises deployments. In some cases, a turnkey cloud service is the right answer; in other cases, you may need a cloud environment where you can turn all the knobs and cranks like you did on-premises. Either allows you to lift and shift back and forth between on-premises and the cloud. The result is a sense of balance, with organizations working smarter—not harder—to bring these elements together.
The growing tension between governance, risk, and compliance is also relaxed by this flexibility to choose. Financial, operational, and regulatory policies and mandates overwhelm your ability to manage the associated risks. Challenges are compounded by a lack of enterprise-wide visibility. What could go wrong? Have you ever asked yourself what it would take to spend less time worrying about risks so you could focus on pursuing growth opportunities?
What if businesses gained the power to identify your needs and fulfill them long before you even thought of them?
Sure, websites already track our browsing patterns and make suggestions, but what we're talking about here is something much deeper. Business intelligence is evolving into augmented analytics.
Augmented analytics, along with the power of artificial intelligence (AI) and machine learning (ML), address how we interact with information and transform how we work and live. For example, businesses are preventing and detecting credit card fraud, automating supply chains through checks and balances, optimizing supply chain finance based on market swings, and boosting data center efficiency. With the help of artificial intelligence and machine learning, these abilities are now at the intersection of people's judgment, and machine automation and augmented analytics makes them possible.
What could be possible in your organization if insights came to you when you needed them most? What might happen if half the content for your next operations review were generated by machines? How prepared would you be for your next meeting if you were notified of changes based on your location?
Augmented analytics applications are a new category of continuously adapting, self-learning applications powered by enterprise data from a variety of data sources, including transactional business apps (such as customer experience, enterprise resource planning, supply chain management, and human resources). These are solutions that operate without human bias and deliver a very high degree of confidence—on a very large scale.
Take a moment to think about all the data generated during a single business day. New customer profiles. Sales logs. Marketing touches. Payroll allotments. Service records. Inventory rolls. No doubt that in a single 24-hour period, a business retains specific details of a massive number of transactions. But what does it all mean?
Simpler big data discovery tools will let business analysts shop for datasets in enterprise Hadoop clusters, reshape them into new mashup combinations, and even analyze them with exploratory machine-learning techniques. Extending this kind of exploration to a broader audience will improve self-service access to large data repositories and provide richer hypotheses and experiments that drive the next level of innovation.
Many successful companies have already adopted a data-driven approach to innovation. They connect their data, automate their processes, understand the business drivers, align corporate finance planning with operations planning, and build their team's analytics expertise.