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What are challenges for predictive analytics?

Daniel J. Power
Editor, DSSResources.COM

Prediction is a difficult task. The assumption of current tools is that historical and current data can be used to with quantitative models and in some case with the addition of human judgment to "predict" future conditions and outcomes. The past is in some situations an acceptable predictor of the future, but there are challenges and limitations.

1. Falling off the data cliff.

A pattern abruptly ends and the prediction is very inaccurate.

2. The slow data spiral of death.

The data and model become less and less predictive over time and human observers or statistical tools do not identify the increasing errors before serious errors have had a large impact on organization performance.

3. The widening data circle of despair.

The widening variance is observed, but the cause of the error and the solution to adjust the prediction is not identified soon enough and managers feel helpless to correct the error.

4. The data staircase to hell.

The data trend had been steadily predicting growth and success and then the trend is more like a stair downward perhaps even a gentle slope or perhaps more like an escalator. The change in the data pattern is observed, but analysts and managers keep hoping the changing is temporary.

5. The data explosion.

The trend and model predict stellar outcomes and then the predictions are proven unrealistic and the negative outcomes from overly optimistic investment decisions and exaggerated expectations lead to financial ruin.

Last update: 2018-11-20 04:29
Author: Daniel Power

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