In this era of the software-driven business, we’re told “data is the new oil”, and that predictive analytics and machine intelligence will extract actionable insights from this valuable resource and revolutionize the world as we know it. Yet, 2016 brought three highly visible failures in this predictive view of the world: the UK’s Brexit plebiscite; the Colombian referendum on FARC; and finally, the U.S. presidential election. What did these scenarios have in common? They all dealt with human behavior. This got me thinking that there might be lessons to be learned that are relevant to analytics.
The fact that data can be noisy or corrupted is well known. The question is: how does the uncertainty within the data propagate through the analytics and manifest itself in the accuracy of predictions derived from this data? For the purposes of this article, the analysis can be statistical, game-theoretic, deep learning-based, or anything else.
There is also an important distinction between what I call “hard” data and “soft” data. This is not standard terminology, so let me define what I mean by these terms.
Hard data comes from observations and measurements of the macroscopic natural world: the positions of astronomical objects, the electrical impulses within the brain, or even the amounts of your credit card transactions. Typically, such data is objective. The observations are numerical, and the uncertainty is adequately characterized as an error zone around a central value. There is an (often unstated) assumption that the observation is trusted and repeatable (i.e., nature is not being adversarial and presenting the observer with misleading results).
Much effort has gone into designing measurement apparatus, calibration techniques, and experimental design to reduce the error zones. There is even the so-called “personal equation” to account for observer bias.