Big Data and Decision Management Systems: The impact of Velocity

July 2, 2013

in Analytics, Business Rules, Data Mining, Decision Management

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Table of contents for Big Data and Decision Management Systems

  1. Big Data and Decision Management Systems: The impact of Volume
  2. Big Data and Decision Management Systems: The impact of Variety
  3. Big Data and Decision Management Systems: The impact of Velocity

The third and final post in my series on the impact of Big Data on Decision Management Systems: The impact of Velocity.

As more data arrives more quickly we have to deal with velocity in two ways – we have to decide more quickly and we have to deal with data “in motion” – streaming data – not just data at rest.

The first of these, like the increase in data volumes, simply increases the value of a Decision Management System. As our data arrives more rapidly the value of processing and acting on that data in real-time is likely to grow. This need for real-time responses pushes us inexorably towards Decision Management Systems because people just don’t make decision in real-time. As real-time becomes the right time, we must automate decision-making. This increase in velocity also tends to make decision value decay more quickly. This idea that the value of a decision decays over time (decision latency) is one I have discussed before and the increasingly rapid arrival of new data means that decisions will decay faster as new data will make the old decision less relevant.

This has a side effect of also making predictive analytics more valuable. With less time to decide it becomes more important to have some predictive headroom – the further out I can see the more time I have to respond. With slow moving data it might have been enough to see yesterday’s summary or today’s. As data moves more rapidly we must see in the future, make predictions, if we are to have time to respond.

The second impact is that we must get better at injecting decision-making into streaming data. We have to be able to package up business rules and analytics and inject them into a data stream so that we can enrich the stream with decision answers or so that we can kick of parallel processes as the stream flows by. These require different deployment metaphors with lower latency and more state management capabilities. The growing ability of business rules management systems to integrate with event handling and the deployment of analytic models into streaming data infrastructures are just two of the developments supporting this trend.

So that’s a wrap. The Volume, Variety and Velocity of Big Data are going to drive more demand for Decision Management Systems, put additional pressure on those building analytics for Decision Management Systems and mean we must expect more of the technologies we use to build them. It’s going to be fun.

If you have any questions about how to respond to Big Data in your organization, contact us and we’d be glad to help.

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