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Data science as an experimental process: unsupervised and supervised learning

A fundamental question faced by business analytics professionals and data scientists is whether they have a working correlative and causal explanatory model related to the phenomenon they are observing, be it related to reducing manufacturing error rates, determining the cause of customer abandonment, reducing fraud, targeting marketing, realizing logistics efficiencies, etc.  This is known as an experimental model in science or a conceptual model in broader research venues (i.e. social sciences).

Data Science

Data Science

The increasing interest in analytics has led to a proliferation of powerful tools.  The new tools increase the ease of conducting sophisticated data analysis.  However, the danger is that inexperienced analysts take a shotgun approach, throwing data at a tool and leaping at any hints of statistical causal significance that emerges.

For example, a sophomore data analyst might rush to notify management that s/he detected a strong correlation between the marketing budget and revenues, suggesting the marketing budget should be increased as much as possible.  With a deeper examination of marketing efficacy in relation to mediating factors (i.e. macroeconomic trends, demographic features, competitive forces, trending consumer preferences, seasonality, weather), one will realize that marketing expenditures are rarely a constant direct causal agent in revenue growth (and when a strong factor, only temporary in scope).  Otherwise, marketing would have an infinite budget and run most companies (though this might not stop them from trying to assert this right).

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