Predictive Analytics: Intersection of Humans, Machines

Putting all your eggs in one cloud basket is risky, because clouds are not immune to denials of se

Is big data moving your decision-making needle, or, has it only confirmed what you already knew? Predictive analytics and a human touch help make sense out of your data.

Businesses have had a good, long run of succeeding based on instincts and intuition. As data-driven decisions come into vogue, advocates are making convincing arguments that the data knows more than humans do. The argument has merit, to a point.

Humans have limitations, especially when facing huge amounts of data. For instance, we’re not adept at remembering lists with more than seven things on it. Even facts we once knew, like last year’s Oscar winners or the champion of the 2016 Super Bowl doesn’t remain with us. Our brains have thresholds that prevent us from seeing patterns and nuances in large data sets like those in big data. Among analytics advocates, predictive analytics combines the best of machine learning and humans. It can do what our brains can’t, and it can find the data correlations and causations that lead to better business outcomes. But it needs the touch of someone who is business-savvy.

Consider this scenario; the board at a high-tech company is adamant that executives do a better job retaining its developers and engineers. Lots of funding has gone into hiring personnel but high turnover is preventing the company from meeting software development goals. Looking for an answer, the CEO takes this problem to the lead data scientist who creates an algorithm in the predictive analytics software and applies it to the data.

In short order the software reports back this correlation: Most employees quit on Mondays. The company cafeteria serves sushi on Mondays. The recommendation is to change the Monday menu. Clearly, even though the data shows a correlation, it’s not causation. Left alone the machine learning software could flounder forever continually deciding a new lunch plan; it will take a human mentor to re-direct the algorithm.

A human knows that employees come back after a weekend of mulling life goals and give notice. That intuition will quickly reveal that lunch-time sushi isn’t relevant to the problem and reset the algorithm to find insights and recommendations that make sense.

Predictive analytics with machine learning is the intersection between humans and analytics. Take the usual BI report that develops when an executive asks a data analyst to review supplier costs across three regions during the past three years. After a week or more of waiting, the analyst submits a barebones report of color-coded price points across vendors, and it’s not particularly useful to the executive.

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