Ventana Research has been researching and advocating operational intelligence for the past 10 years, but not always with that name. From the use of events and analytics in business process management and the need for hourly and daily operational business intelligence, but its alignment with traditional BI architecture didn’t allow for a seamless system, so a few years later the discussion started to focus around business process management and the ability of companies to monitor and analyze BPM on top of their enterprise applications. Business activity monitoring became the vogue term, but that term did not denote the action orientation necessary to accurately describe this emerging area. Ventana Research had already defined a category of technology and approaches that allow both monitoring and management of operational activities and systems along with taking action on critical events. Today, Ventana Research defines Operational Intelligence as a set of event-centered information and analytics processes operating across the organization that enable people to take effective actions and make better decisions.
The challenge in defining a category in today’s enterprise software market is that prolific innovation is driving a fundamental reassessment of category taxonomies. It’s nearly impossible to define a mutually exclusive and combinatorially exhaustive set of categories, and without that, there will necessarily be overlapping categories and definitions. Take the category of big data; when we ask our community for the definition, we get many perspectives and ideas of what big data represents.
Operational intelligence overlaps in many ways with big data. In technological terms, both deal with a diversity of data sources and data structures, both need to provide data in a timely manner, and both must deal with the exponential growth of data.
Also, business users and technologists often see both from different perspectives. Much like the wise men touching the elephant, each group feels that OI has a specific purpose based on their perspective. The technologist looks at operational intelligence from a systems and network management perspective, while business users look at things from a business performance perspective. This is apparent when we look into the data sources used for operational intelligence: IT places more importance on IT systems management (79% vs. 40% for business), while business places more importance on financial data (54% vs. 39% for IT) and customer data (40% vs. 27% for IT). Business is also more likely to use business intelligence tools for operational intelligence (50% vs. 43%), while IT is more likely to use specialized operational intelligence tools (17% vs. 9% for business).
The last and perhaps biggest parallel is that in both cases, the terms are general, but their implementations and business benefits are specific. The top use cases in our study for operational intelligence were managing performance (59%), fraud and security (59%), compliance (58%) and risk management (58%). Overall we see relative parity in the top four, but when we drill down by industry, in areas such as financial services, government, healthcare and manufacturing, we see many differences. We conclude that each industry has unique requirements for operational intelligence, and this is very similar to what we see with big data.
It is not surprising that our definition of operational intelligence is still evolving. As we move from the century of designed data to the century of organic data (terminology coined by Census Director Robert Groves), many of our traditional labels are evolving. Business intelligence is beginning to overlap with categories such as big data, advanced analytics and operational intelligence. As I discussed in a recent blog post, The Brave New World of Business Intelligence, the business intelligence category was mature and was showing incremental growth only a few years ago, but it is difficult to call the BI category mature any longer.
Based on the results of our latest operational intelligence benchmark research, we feel confident that our current definition encompasses the evolving state of the market. As operational intelligence advances, we will continue to help put a frame around it. For now, it acts very much like what might be called “right-time big data.”