“Making predictions is hard, especially about the future” is a well known witticism. When it comes to making predictions about how companies will make predictions, it can be even harder to know what to say. Nevertheless, the folks over at KDnuggets recently asked some of the leading experts in Data Science and Predictive Analytics for some thoughts on developments in 2016 and trends for 2017. I was one of those that participated and you can see the article here – Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017. Several key themes emerged from the various expert responses:
As I discussed in my earlier post, analytics or data science teams know that two key challenges for analytics projects are making sure you solve the real business problem (framing the problem) and making sure you can operationalize the result (deployment). In this second post I am going to talk about deployment.
Analytic teams are using decision modeling to frame the problem with their business partners, assess the predictive model’s value in business terms and clarify the people, process and technologies needed for successful deployment.
Learn more about Decision Management Systems and Analytics at INBADD 2016 with speaker Gagan Saxena, VP & Principal Consultant, Decision Management Solutions.
Decision modeling and the graphical decision requirements diagram (DRD)helps analytics teams assess the predictive model’s value in business terms, select the right delivery option and know how to make the analytic actionable.
A decision requirements diagram (DRD), developed with with the Decision Model and Notation (DMN) standard, provides analytics projects with focus and direction, delivering value quickly without huge upfront costs or time investment.
Here we see the classic challenge of analytics – 70% think it is very or extremely important but only 2% say their analytics efforts have a broad, positive impact. And like every other assessment of this problem, including ones we have conducted here at Decision Management Solutions, the problem is not in the data or in the analytics themselves! The problems are in framing the problem and operationalizing the results. Everyone seems to have the analytic technology they need but they just aren’t getting it to work for them.
Learn how to achieve a standards-based, efficient approach to analytics deployment from open source like R and commercial analytics products to a wide range of platforms including mainframes like IBM zSystems and new data infrastructure like Hadoop. Thursday, March 3, 2016 11:00:00 AM PST – 12:00:00 PM PST Register here. The big challenge for analytics-driven […]
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