What do we know about Big Data And The Academic Practitioner Divide? Is there cause for hope? I think so. At least a bit.
The Big Divide
I find the divide between academics and practitioners fascinating. It is clearly very large. More strangely, I find it amazing how easily many people on both sides accept it almost without question. People just assume it has to happen.
Academics are often woefully unaware of the topics managers discuss. Reading the Managerial Implications section in academic papers is an experience. (In a bad way. Not in an “experiences are better than things” way). Implications can vary, mostly they are very boring. They are usually incredibly predictable. Sometimes though implications are truly weird. You might see 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. They shouldn’t just be ignored.
Big Data And The Academic Practitioner Divide
Against this background, I am happy to see articles highlighting the problem. Even better, are suggestions for 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 and I share some of their optimism. There is so much data being collected. Yet 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 such situations. Academics can bring skills and ideas while practitioners can bring data and questions. Questions are critical. These keep academics focused on non-trivial issues.
Big Data Can Be Good
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. You will certainly find one if you look long enough. Still, 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.
For more on the academic/practitioner divide see here, here, and here.
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
