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The 2016 McKinsemckinseyy report: The Age of Analytics: Competing in a Data-Driven Worldis an excellent piece of research. My take-away is: Analytics can create substantial value, but only a fraction has been realized due to organizational resistance and lack of talent. The imperative for companies to overcome that resistance is an “ever widening gap between leaders and laggards”, where “leaders are staking out large advantages”.

Change management is always a challenge in theses types of transformations, but the incentives (competition and investor expectations) are there, and skepticism can be overcome with education and pilots (light bulbs go off when people see it working on their own turf). Cost should not be a barrier either – most is “pay-as-you-go”.

So, with those barriers down, the last one is talent. McKinsey describes analytics as a merger of four broad types of roles: data architects, data engineers, data scientists, and business translators. The business translator role (combining domain and technical knowledge) is considered key (McKinsey is projecting a demand of 2m-4m for these people over the next decade, which should be good news to my students). Gartner also wrote about the talent issue in June [1], recommending that companies: Train existing staff into “citizen data scientists” (see my post), and partner with academia.

We have bright, passionate students in Analytics courses at Bauer. We also have an Analytics lab (see my post), which can be used for POCs. We research and share best practices, and we can help with education and visioning. So, we think partnering with academia is a brilliant way to jump start analytics – we’re here to help. Please contact me if you’d like to explore.

[1]G00294588 “Doing Machine Learning Without Hiring Data Scientists” Published: 20 June 2016