Predictive Enough?

Back in early 2000s, Business Intelligence professionals were talking about the novelty of automated cross advertizing. An example could be advertizing greeting cards to someone who just purchased a gift, or vice versa. If you bought a present then you must need a greeting card, so how about some of the choices right there, presented to you before or after your check out. While this wasn’t predictive analytics, it was still based on past shopping behavior. Market baskets are a more sophisticated version of shopping behavior analysis.

Modern predictive analytics is more comprehensive – it is analysis of a pattern across a time span aimed at predicting what you are likely to do or need next. There is, probably, an infinite number of ways to analyze various data to try to predict human behavior – past shopping data, weather pattern data, seasonal data, income level data, credit score data. Creative analysis of combinations of these data and many more factors can uncover what’s about to happen before it happens.

However, many contemporary predictive analysis examples still amount to little more than simple cross advertizing that can, potentially, be achieved without the associated expense. It’s hardly necessary to go through extensive data analysis to “predict” that I am likely to need a greeting card to go with that gift I just purchased. It would be wise to understand the real business need for analytics. If the goal is to develop a competitive edge in the modern marketplace, then predictive analytics is likely the way to go, and it is a journey that takes methodology and patience.


Predictive analytics and advertising: the science behind knowing what women (and men) want

Competence & Honesty Is Hard Work

Hard as it may be to master data analysis, honest unbiased reporting is even more difficult.

We all make mistakes, and quite a few of us give in to the temptation to mislead others for personal gain. While mistakes are par for the course, deliberate manipulation is not.

Charts are one of the most common data visualization choices. It’s not uncommon for people to “distort” reality either by mistake or intentionally when presenting bar, line or other charts. In my career, I saw people spot and correct charting mistakes and make “clever” misrepresentations designed to help someone get ahead.

Labeling of X and Y axes can be misleading. The context of what’s being presented can be made unclear. Cherry picking is a common practice for making things look better than they really are. Misusing charting conventions can “trick” people into seeing something that isn’t there such as a pie chart seemingly showing a favorable picture overall when, really, the chart is focused only on a small subset without an accompanying explanation.

This brings me to the point I am trying to make – be extremely vigilant with analytics. It’s just as dangerous as it is helpful. If you know what you are doing or if you are not very good at it, the result can be a disaster just the same.


Is a chart lying to you? This video has some tips to figure it out. – Vox

Emotion is Also a Data Point

We must strike a balance between relying on data and emotion when making decisions. A glaring example of this is our last Presidential election here in America. Clinton relied on data and lost. Trump played on people’s emotions and won.


Tools for predictive analytics took a hit in presidential election