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Operational Intelligence – Is This the End Game for Big Data?

− Operational Intelligence – Is This the End Game for Big Data?

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.

Robin Bloor

About Robin Bloor

Robin is co-founder and Chief Analyst of The Bloor Group. He has more than 30 years of experience in the world of data and information management. He is the creator of the Information-Oriented Architecture, which is to data what the SOA is to services. He is the author of several books including, The Electronic B@zaar, From the Silk Road to the eRoad; a book on e-commerce and three IT books in the Dummies series on SOA, Service Management and The Cloud. He is an international speaker on information management topics. As an analyst for Bloor Research and The Bloor Group, Robin has written scores of white papers, research reports and columns on a wide range of topics from database evaluation to networking options and comparisons to the enterprise in transition.

Robin Bloor

About Robin Bloor

Robin is co-founder and Chief Analyst of The Bloor Group. He has more than 30 years of experience in the world of data and information management. He is the creator of the Information-Oriented Architecture, which is to data what the SOA is to services. He is the author of several books including, The Electronic B@zaar, From the Silk Road to the eRoad; a book on e-commerce and three IT books in the Dummies series on SOA, Service Management and The Cloud. He is an international speaker on information management topics. As an analyst for Bloor Research and The Bloor Group, Robin has written scores of white papers, research reports and columns on a wide range of topics from database evaluation to networking options and comparisons to the enterprise in transition.

12 Responses to "Operational Intelligence – Is This the End Game for Big Data?"

  • Dave Duggal
    February 4, 2013 - 12:43 pm Reply

    Great post Robin. Question for you, how do you distinguish Big Data, which seems to have a bias towards streams of public web data and website activity, from internal operational event data (state of records, logs, business entities, rules, process, people, org structure, etc.)? Is that Broad Data??? I just think there is tremendous value in being able to reflect on your internals as well as your externals.

  • Dan Linstedt
    February 4, 2013 - 1:24 pm Reply

    Hi Robin,

    This is an interesting post and certainly points out part of what’s missing from the current landscape. I am in agreement with you, that we will and should hear more about this, this year. In retrospect, there are some companies whom have been engaged at these levels for years.

    Teradata has been doing this (especially for fraud detection) with a Real-time / right time data warehousing strategy (as you know). I’ve written quite a few blog entries on this subject on B-eye-network.com, and on danLinstedt.com – mainly around Operational Data Warehousing.

    It’s good to see the market finally catching up with the need. My two cents on the topic is this: Big Data & Non-Big Data are both very useful in the following sets of ideas:
    1) As raw data sets to find and discover anti-patterns, outliers, and to be used as gap analysis between the data collection systems and the source systems, along with gap analysis between the “belief” (read: business requirements) and the actuality (read: raw data historical storage).
    2) Probably the more useful case: as “rolled up” or analytical / mined information stores. The analytical mined information is what is pure gold (IF the right question has been asked up front). These stats produce landscapes that show general patterns in a small set of results. These are the “common statistics” that should be applied to a neural network learning engine at run time to bounce the current transaction against, and to determine “where does it fit?” if at all.

    In my mind, it’s not just Big Data that plays a role, but traditional data and transactional data as well. Although, we all know that the deeper the data, the better the statistics (usually if there is a common trend to be spotted), and hopefully, the better the analysis.

    Operational Data Warehousing is a convergence step, making the Data Warehouse 24×7 real-time “plugged in” to raw data feeds, and linked up with NoSQL (not only SQL) feeds for maximum analysis capabilities. Couple that with an ESB and a real-time mining engine that accepts transactions on the fly, and I think you have (possibly) a gold mine to work with.

    Cheers,
    Dan Linstedt
    http://LearnDataVault.com

  • Neil Raden
    Neil Raden
    February 4, 2013 - 4:07 pm Reply

    Robin,

    I’m a little confused. It seems to me that what you’re describing as operational intelligence is pretty much the same thing that James Taylor and I wrote about in “Smart (Enough) Systems” and called Decision Management. One constructs business rules using a business rules management system, with rules informed typically by various types of descriptive, predictive and optimization analytics. We found in our research that implementation of these systems (and this research was 5-6 years ago) was widespread in financial services, call center optimization, supply chain and a few other verticals.

    Granted, we weren’t dealing with what is now considered big data, but it was heavily populated with customer data, transactions, public data such as credit bureau, housing starts,…a whole slew of things.

    These systems were designed to handle real-time decisions of high volume transactions where 100% accuracy was not the idea. Consistency and speed, as well as ease of modification were. But they weren’t applicable to all kinds of decisions. Those decisions that require human input, or at least human review, feel more deserving of the term “operational intelligence.”

    My $.02

    • Eric Kavanagh
      Eric Kavanagh
      February 4, 2013 - 4:35 pm Reply

      Neil, You? Confused? That’s not possible! #LOL

  • Mike Ferguson
    Mike Ferguson
    February 4, 2013 - 10:59 pm Reply

    Robin,
    I have been writing about operational BI for a decade now and have a whole 1 day workshop on it. It staggers me why it has taken so long. There are many companies doing this already today. On-demand operational BI in the call centre and event-driven operational BI for continuous optimisation. I wrote a paper on this for Teradata back in 2011 you can find here http://www.google.com/url?sa=t&rct=j&q=active%20intelligence%20for%20smart%20business&source=web&cd=1&ved=0CDsQFjAA&url=http%3A%2F%2Fwww.teradata.com%2Fwhite-papers%2FActive-Intelligence-for-Smart-Business%2F&ei=-Y8QUaTEE-Or0AWy54HQCQ&usg=AFQjCNGXfKMtcDp79SM-u_sdHAXCmzbcKw&bvm=bv.41867550,d.d2k. There are lots of companies doing this. For me, operational intelligence is what transforms companies from having business intelligence to being an intelligent business. It is ‘always on’ intelligence designed to keep the company operations optimised. I have a survey on this. It’s mind blowing what some companies are doing…particularly in media.

  • Shalin Shah
    February 5, 2013 - 1:23 pm Reply

    Many people, incorrectly, think that OI is synonymous with BI. If you are looking to make decisions that will take effect next quarter or next fiscal year, then BI is your solution. If, on the other hand, you are looking to make decisions that will take effect immediately (while the process or event is still in play), then OI is your solution.

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