SCTR7: Data Science and Analytics

The adage ‘garbage-in-garbage-out’ is an analytics mantra so ingrained it has its own shorthand: GIGO. Yet, in the mad, blind rush toward all things ‘big data’, there is the danger of sidelining the crucial-but-dreary topic of data quality, to which GIGO refers. While data quality is not as ‘sexy’ as big data, anyone who wants […]

Increasingly sophisticated analytics tools and methods are available to derive business insight from data.  However, as a discipline which drives insight from data, the crucial ‘last step’ in the analytics process is about organizational decision making.  A sophisticated, intensive analysis may all be for naught if the crucial last step, framing and committing to a decision, misses the […]

As a companion to my recent post “Correlation versus Causation: The Science, Art, and Magic of Experimental Design”, I wanted to offer a more technical exposition concerning data science approaches to focused causal model development. A fundamental question faced by business analytics professionals and data scientists is whether they have a working correlative and causal […]

Recently I was chastised for being ‘unnecessarily obscure’ for reacting to a specious conclusion by suggesting that it risked ‘conflating correlation with causation’. Guilty as charged!  I apologize:  the expression is quite a mouthful and requires unraveling for those unfamiliar with the nuances of the applied experimental method.  However, I feel passionately that this concept […]

. The en masse retirement of Baby Boomer generation professionals underway is witnessing an unprecedented ‘brain drain’ from a host of industries in the industrialized world.  Can Machine Learning methods be used to capture some of this exiting expertise?   The untimely, and arguably demographically linked, Global Economic Crisis underway has unfortunately pushed rigorous ‘expert […]