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The terms “big data”, “data science” and “analytics” have pervaded the global common speak over the past decade. While populist in many cases, these terms are rooted in the real practice of being able to measure and analyze phenomena in larger amounts, faster and with a longer and more robust historical perspective, all facilitated by technological advances and the lower cost of data storage. Data, once defined by a numerical representation of some measurement, has today evolved into an atomic unit that can be captured – that is measured, seen or heard – and thus extracted, analyzed and converted into information and ultimately into new knowledge. What began only a few years ago as a growing swell of the data ocean has become a tsunami of impacts into everyday life, or the “datafication” of the economy (Dumont, 2016).
This datafication has resulted in many organizations sprinting to better leverage the data they collect and capture the data they do not. The argument that knowledge, as a summation of data through the knowledge management pyramid (Ackoff, 1989), is the only sustainable source of competitive advantage is arguably more relevant today than when it was first posited (Drucker, 1995). It has also led many companies to declare that they are, in fact, data and information organizations more so than they are purveyors of the products they sell (e.g. Capital One (Dee, 2016), Alibaba (Liyakasa, 2015) and Ford (Blanco, 2016)). Cities too are becoming “smarter” with data-driven innovations geared at efficient energy consumption, optimized traffic and parking, and the promotion of green and healthy practices. And individuals are becoming more data driven, with many exploring opportunities by an ever increasing “quantified self”; a concept related to the self-tracking of any number of physical, behavioral, social and many other phenomena by individuals (Swan, 2013). A revolution, or perhaps evolution, to be sure.
An unexpected consequence of these rapid (r)evolutionary changes has been the emergence of the ubiquitous and pervasive “talent gap” – the term used to describe the challenge of organizations to find people with the necessary skills to extract and analyze massive amounts of data (structured and unstructured) to generate meaningful information. Simply put, the demand for these skills has materialized so rapidly, traditional sources of supply for new talent (i.e., colleges and universities) have been ill-equipped to develop and train talent at the scale and pace demanded.
The issues related to the emergence of data science and the associated talent gap have implications for larger conversations related to organizational knowledge management. Jennex (2017) recognized the role of Big Data in the revised knowledge management pyramid. The traditional pyramid first presented by Ackoff (1989) established the framework that organizational wisdom derives from knowledge, information, and finally from data. In the revised pyramid, Jennex places a finer lens on the lowest level of the pyramid by calling out incremental layers between information and reality. These new layers include “Data”, defined as “discrete facts…that can be stored in a database” (Jennex & Bartczak, 2013), “Big Data”, defined as data that is “too big, too fast or too hard for existing tools to process” (Madden, 2012), and “IoT”, defined as a sensor network of networks with devices continually generating vast amounts of data and facilitating the evolving definition of what data even is. This evolution in thinking from a simplistic single layer at the base of the pyramid to a more detailed treatment of data within the knowledge management pyramid increases the resolution of the lens through which reality can be detected.
It is the concepts, tools, and algorithms around “data science” that will enable a sustainable organizational approach to the translation of the layers of data into information, knowledge and ultimately to organizational wisdom/intelligence. However, where those organizational knowledge activities meet societal ones and who addresses those “fault” lines become an issue as data sources become more democratized and real time (Spender, 2007; Money & Cohen, 2018).