Last time we described the simplest class of analytics (i.e. descriptive analytics) that you can use to reduce your big data into much smaller, but consumable bites of information. Remember, most raw...
Updated 7 months ago
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Excellent article and series.
I believe you clarify a common misperception - fueled in part by companies with roots in complex event processing - that predictive analytics can predict the future. Perhaps "anticipatory analytics" would have been a better, albeit less marketable, name.
That said, the statistics professor in my MBA program many years ago introduced himself to the class by saying that statistics can be used to prove any point of view. With all the hype around big data analytics, not enough attention is being given to the quality of data nor the validation of models built on the data.
Coefficients of determination can easily be manipulated to fit the hypothesis behind the model. As such, doesn't this also distort the analysis of the residuals? Models for spatial and temporal data would only appear to complicate validation even further.
Data management tools have improved to significantly increase the reliability of the data inputs. Until machines devise the models, focus on the validity of the data would improve model validation and reduce, not eliminate, inherent bias.