Don't Put the Cart Before the Horse

The buzz around the datasphere is that the data economy is in  apparent disequilibrium. Data prevalence is rising at a seemingly exponential rate, while the quantity of data scientists with the relevant quantitative and methodological prowess to extract value for organizations is in short supply. A 2011 McKinsey report suggests that there will be 140,000 to 190,000 unfilled positions for U.S. data analytics experts by the year 2018.  Other estimates suggest that industries where analytics might assert its most potent impact, healthcare for example, are only utilizing advanced analytical approaches and tools to their full potential at a rate of about 10%. 

Therefore, what we are seeing is an astounding proliferation in data quantity with concurrently greater efforts to actualize this data into empirically-driven solutions and results. However, at the same time, we must undertsand that we are in the midst of a tangible transition. We know understand the power and potential of the insights that analytics might realize. Nevertheless, we are sunken deeply in an experimental phase of how we might best apply analytically-centered methods, software, and strategies to accomplish existing goals and chart new territory. We have familiarized ourselves, but make no mistake, all of this is still very immature. 

Characterized by an influx of demand  for professionals equipped with the expertise to employ necessary methods in tandem with an understanding of the required analytical software, finding a data scientist is no short task, much less creating and developing individuals with these skills sets. Thus in any discussion regarding the pervasiveness of data analytics, we must first ask if we have sufficient expertise to match the scale at which we desire to apply analytic-intensive solutions. 

In this sense, putting the cart before the horse will not be in the best interest of the global community. Rather, identifying and developing the appropriate skill sets to achieve objectives (such as a number of universities worldwide have done by implementing graduate programs in data science and analytics) will ultimately set the framework for sustainable, effective unlocking of the analytic, data-centric potential we all know exists.