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Beyond Predictive BI

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Syndicated from ebizQ

David Linthicum‘s recent blog post Approaching Predictive BI made me want to reiterate some of my thinking around BI (on which I have a whole category) on my blog. I often talk about going “beyond BI” with decision management. I distinguish between BI and Decision Management because BI helps you understand your business while Decision Management helps you execute business. If BI is the bridge between your data and your strategies, DM is the bridge between your strategies and your operations. Other differences include:

  • BI provides insight on customers as groups, DM uses customer-level insight to identify the
    ideal action to take
  • BI tends to be a back-room, offline operation for knowledge workers while DM is embedded in operational systems and processes.
  • BI traditionally synthesizes past performance to aid in understanding while DM relies on predictive analytics to predict future behavior

I often write about this – check out Beyond BI to EDM (again) and If dashboards are the end game, kill me now… for example.

David makes a good point that our data is now ready for  us to move to a more predictive mindset. As he says you

can model some future events, using historical data as the foundation
Predictive BI is all about determining what will happen versus what happened.

My favorite phrase in this context is that

Predictive analytics turn uncertainty about the future into usable probability

And this leads back to my point about Decision Management. If a predictive analytic model turns uncertainty about how a particular customer (or supplier or partner) will behave in the future into a usable probability then you can act based in part on that probability. In other words you can specify some rules that use the probability in deciding what action to take next. This kind of “intelligent” decision-making by systems is, I believe, the future.

I think that many folks over-estimate the value of making more information available – even if that information is predictive. No matter how easy you make it to consume the information you still assume that the person is able to put it in context and use it. I call this the “so what” problem:

  • My call center reps know how profitable each customer is when they call.
    So what? Does this change the cross-sell offer, the rules about letting them off charges or what?
  • My doctor has an electronic medical record of my entire history
    So what? Is she going to make a different treatment decision because of it? Will she have the time to read it all or be able to spot the crucial piece?
  • My insurance agent knows what natural disasters my house it at risk of
    So what? Is he going to know how to change my risk-based premium as a result?

Making information more readily available can be important but making better decisions based on it is what pays the bills. So go beyond predictive BI and start thinking about the decisions based on predictive BI.

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