You could say that for the past 20 years we talked the data analytics talk but didn’t really walk the data analytics walk. It isn’t that companies didn’t do data analytics; they did. Many people were employed as data analysts or used data analyst tools as part of their job. You could find them in pharmaceutical companies, banks, insurance, big retail and, once the web took off, they were attacking web log data with their mathematical tools.
In truth though, it wasn’t until Google that we had a company run by data analytics. And let’s not kid ourselves on this: intelligent business innovation drove much of what Google did, but data analytics drove a good deal of their activity and innovation.
Boiling Down Big Data
Data didn’t suddenly become “big,” it just hadn’t been analyzed before or, in some instances, it hadn’t been analyzed in depth. So the advent of easily deployed public cloud resources or easily manageable private cloud resources, plus the inexpensive Hadoop stack, created an opportunity for data analysts to work on data sets that they hadn’t examined before. As was likely, some of that data unearthed valuable knowledge.
In part, this involved large volumes of data, high velocity data or awkwardly structured data, but the general point was that analytics increased in business importance. While not every piece of knowledge needed to be exploited immediately, some did indeed require quick action.
Thinking of the broad business intelligence (BI) market, we can look at this in terms of the four categorizations of BI that we use here at The Bloor Group: Hindsight, Oversight, Insight and Foresight. The first two, hindsight and oversight, are fairly well exploited by many companies via regular reports, dashboards, OLAP capabilities and varieties of data visualization. The new data sources that companies are now exploiting can be fed into established hindsight and oversight capabilities quite easily.
Most of the big data action lies in the areas of insight and foresight (deep analytics and predictive analytics), and some of the knowledge that is being discovered needs to actioned swiftly. Speed is a prime factor.
To point to the obvious: the cost of fraudulent activity diminishes the sooner the activity is detected and stopped. The same is true of a network security breach or some risk factor in the financial market. Further, a characteristic of valuable information (intelligence) is that its value tends to degrade over time, either because the information is shared or because competitors also discover the information. So the trick is not just to unearth such information, but to act on it as fast as possible.
The Rising Tide of Operational Intelligence
Operational intelligence is, we believe, beginning to take off. For one thing, we see more and more vendors using this term to describe their technology. Such vendors all have one thing in common, irrespective of what their other capabilities are: they seek to transform business intelligence into business action at real-time or near real-time latencies.
The business intelligence we are talking about here is coming mainly from data analytics or predictive analytics. What we mean by business action is that the intelligence is presented either to a user for immediate action or delivered as a trigger to software which takes action automatically.
Arguably, such operational intelligence applications have existed for quite a time. Banks have been automating trades based on smart algorithms for years. But a general set of software capabilities that can feed intelligence directly to the point of business action is fairly new.
In our view, there is a rising trend here that’s very likely to take off this year.
Does your company provide Operational Intelligence software or services? If so, please request a briefing so that we can provide a detailed overview of your offering in our OI Market Roundup, to be published this March.