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Some perspectives on prescriptive analytics

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I recently participated in an International Institute for Analytics webinar,  Prescribing The Right Decision With Prescriptive Analytics (I am a faculty member of IIA). In the webinar we did some surveys that had some interesting results.

First off we asked the audience what they were using analytics for. This was interesting to me as it overlapped with a question I asked as part of the Analytics Capability Landscape research we did last year.  I took a subset of the answers and corrected for it being a multi-select answer this time to come up with the graph below that shows the degree to which analytics are focused on:

  • Reporting on data
  • Monitoring business performance
  • Improving decision-making

I ended up with three sets of answers – one from the Analytic Capability Landscape (ACL) survey focused on what folks were doing then, one from the same survey focused on where they expected their focus to be in 12-24 months and the IIA results from this week.

IIA Why Use Analytics

You can see that when we surveyed before we got a strong focus on analytics being about reporting and monitoring, with a lot less on decision-making. The IIA results, on the other hand, showed a bigger focus on decision-making while the survey that asked what people expected to be their focus in the future showed a clear trend – away from reporting, away from monitoring and increasingly focused on decision-making. This is why we like to say “Begin with the decision in mind” – stay focused on the decision to maximize the value of analytics.

IIAPrescribingActionsThe second survey was asking about prescription – to what extent were companies using analytics to prescribe action not simply as a way to present insight. Here you see that over half the respondents are doing analytics but not driving to prescribed actions while another 40% are only prescribing action sometimes. This is a missed opportunity – using Decision Management to drive actions from predictive analytics is key to getting value from them.

IIA Deployment ResultsThe final survey asked about deployment – time to deploy analytics and see results. As usual well under half the respondents said they were able to get their analytics into deployment in weeks or less – most took months or never really managed it. Focusing on how the analytic can drive action is one way to improve this – it focuses deployment efforts – but the other is to ensure that deploying and integrating the analytic is part of the same project as developing the analytic. As one client likes to say “minimize the white space between analytic success and business success”.

We strongly recommend decision modeling for analytic clients to address all these issues. Using decision modeling:

  • Focuses everyone on the decision-making to be improved
  • Makes sure that the actions that are being guided or prescribed by the analytic are clear
  • Puts the analytic into a deployment and usage context right from the start

If you want to see the webinar, check out the recording here. If you want to learn more about decision modeling, check out this white paper on framing analytic requirements with decision modeling.

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