Predictive Analytics: Follow These Puffy Thought Bubbles

September 21, 2020

Predictive analytics is about mathematics; for instance, Bayesian confections and Markov doodling. The write up “Predictive Analytics: 4 Primary Aspects of Predictive Analytics” uses the bound phrase “predictive analytics” twice in one headline and cheerfully ignores the mathy reality of the approach.

Does this marshmallow approach make a difference? Yes, I believe it does. Consider this statement from the write up:

These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data. These statistical models are growing as a result of the wide swaths of available current data as well as the advent of capable artificial intelligence and machine learning.

Okay, marketers. Predictive analytics are right in your wheelhouse. The assumption that “statistical models are growing” is interesting. The statistical models with which I am familiar require work to create, test, refine, and implement. Yep, work, mathy work.

The source of data is important. However, data have to be accurate or verifiable or have some attribute that tries to ensure that garbage in does not become the mode of operation. Unfortunately data remain a bit of a challenge. Do marketers know how to identify squishy data? Do marketers care? Yeah, sure they do in a meeting during which smartphone fiddling is taking place.

The idea of data utility is interesting. If one is analyzing nuclear fuel pool rod placement, it does help to have data relevant to that operation. But are marketers concerned about “data utility”? Once again, thumbtypers say, “Yes.” Then what? Acquire data from a third party and move on with life? It happens.

The thrill of “deep learning” is like the promise of spring. Everyone likes spring? Who remembers the problems? Progress is evident in the application of different smart software methods. However, there is a difference between saying “deep learning” or “machine learning” and making a particular application benefit from available tools, libraries, and methods. The whiz kids who used smart software to beat a human fighter pilot got the job done. The work required to achieve the digital victory was significant, took time, and was difficult. Very difficult. Marketers, were you on the team?

Finally, what’s the point of predictive analytics? Good question. For the article, the purpose of predictive analytics is to refine a guess-timate. And the math? Just use a smart solution, click and icon, and see the future.

Yikes, puffy thought bubbles.

Stephen E Arnold, September 21, 2020

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