The new analytic stack is all about management, transparency and users by George Mathew.
On transparency:
Predictive analytics are essential for data-driven leaders to craft their next best decision. There are a variety of techniques across the predictive and statistical spectrums that help businesses better understand the not too distant future. Today’s biggest challenge for predictive analytics is that it is delivered in a very black-box fashion. As business leaders rely more on predictive techniques to make great data-driven decisions, there needs to be much more of a clear-box approach.
Analytics need to be packaged with self-description of data lineage, derivation of how calculations were made and an explanation of the underlying math behind any embedded algorithms. This is where I think analytics need to shift in the coming years; quickly moving away from black-box capabilities, while deliberately putting decision makers back in the driver’s seat. That’s not just about analytic output, but how it was designed, its underlying fidelity and its inherent lineage — so that trusting in analytics isn’t an act of faith.
Now there’s an opportunity for topic maps.
Data lineage, derivations, math, etc. all have their own “logics” and the “logic” of how they are assembled for a particular use.
Could debate how to formalize those logics and might eventually reach agreement years after the need has passed.
Or, you could use a topic map to declare the subjects and relationships important for your analytics today.
And merge them with the logics you devise for tomorrows analytics.