Big Data and the Academic Practitioner Divide

I find the divide between academics and practitioners fascinating. It is clearly very large and I find it amazing how easily many people on both sides accept it almost without question. Academics are often woefully unaware of the topics managers discuss. Reading the Managerial Implications section in academic papers can vary between boring (incredibly predictable) to weird (a recommendation to change an entire business model because a couple of students ticked 7 instead of 6 in a survey). On the other side managers often seem to dismiss academics concerns as, well, academic. But typical academic concerns, e.g., does an intervention really cause an improvement, are critical to business success. Academics often do have good points and shouldn’t just be ignored.

Against this background I am happy to see articles highlighting the problem and, even better, suggesting ways to improve. Brian Gillespie (an academic) with Christian Otto and Charles Young (marketers) have suggested that big data may help bridge the gap. They argue that “While many expect big data may be effective in connecting practitioners to consumers… we argue big data can also be effective in connecting practitioners to academics” (Gillespie, Otto and  Young, 2018, page 13)

It is optimistic but I share some of their optimism. There is so much data being collected but practitioners sometimes “lack the ability or resources necessary to analyze the data. Academics, in contrast, have the training necessary to develop and test related theories using big data, but often lack access to real-world data sets” (Gillespie, Otto and  Young, 2018, page 12). Collaboration makes sense in a such a situation. Academics can bring skills and ideas while practitioners can bring data and questions — keeping academics focused on non-trivial issues.

Such work has got to be good for the analysis of big data itself. In big data it is easy to find a result (there are just so many results to find that you will find one) but if both academics and practitioners agree that the result is a) interesting and b) valid, there is more of a chance that the results really will mean something.

Read: Brian Gillespie, Christian Otto and Charles Young, (2018) Bridging the academic-practice gap through big data research, International Journal of Market Research, 60 (1) pages 11-13