The June 8th edition of Bloomberg Businessweek features the article, Help Wanted: Black Belts in Data. This article reinforces others brought to reader attention by Supply Chain Matters, that a new techie is in demand across the globe, that of data scientists. The article cites a rather timely McKinsey study indicating that by 2018, the U.S. alone may face upwards of a 60 percent gap between supply and requisite demand for deep analytic talent. Once more, because of such demand, starting salaries are reported to now exceed $200,000. But there are other important and crucial aspects related to the pent-up demand for data scientists, namely that the need relates to both technical as well as organizational change skills.

In August of last year, Supply Chain Matters called reader attention to one of the hottest skills demand areas for supply chain and decision-making focused technology, that being the need for data scientists. We cited a report published by The Wall Street Journal describing the scramble to find talent both experienced data analysis and interpretation skills along with knowledge of customers, markets and business processes. Our takeaway message was that organizations not actively investing in identifying talent needs and nurturing the skills needed to harness such analytics focused technology will not only not be able to take advantage of such capabilities, but may well find themselves with a distinct competitive disadvantage.

We further featured a follow-up Supply Chain Matters guest commentary provided by Joannes Vermorel, Founder and Chief Software Architect at Lokad SAS, a technology provider specializing in quantitative optimization of decision-making needs for supply chain and commerce leveraging Big Data and cloud computing concepts. In this commentary, The Challenges and Obstacles of Big Data and Analytics Applied in Supply Chain and Commerce Decision-Making, Vermorel provided two powerful observational insights; namely that Big Data or predictive analytics efforts only succeeds with heavy support from top management, and that most Big Data focused initiatives suffer from “Naïve Rationalism”, that is, the usage of metrics that look scientific from afar, but that actually fail at truly capturing the drivers of the business. Vermorel’s powerful observations need repeating:

Most of the Big Data initiatives that I see in supply chain fail short on both points: companies are trying to introducing such innovations without committing themselves to the drastic organizational changes involved; and companies are letting their data scientists, who are mostly clueless about the business, overlook too many critical business drivers because management is not sufficiently involved in what appears to be a very technical undertaking.”

The Businessweek report notes how well respected universities are scrambling to initiate formal academic undergraduate or post-graduate programs concentrating on data science. Summer intern programs involving students with data science skills are reportedly paying $6000 to $10,000 per month.

However the message for both supply chain focused organizations and students needs to include not only the requisite technical skills of data science and analysis, but deep knowledge of value-chain processes, cross-functional program and change management concepts. Finally, before getting carried away with hyper focused recruitment efforts, senior leadership and sales and operations planning teams need to couple any talent management recruiting efforts with efforts directed at actively practicing a deeper understanding of key business metrics that lead to expected business change or changed business outcomes.  Launching the brightest data science talents on misunderstood or misdirected decision-making practices is a waste for the candidate as well as the organization.

Finally, we encourage students and graduates seeking to pursue data science careers to further channel their studies toward a deeper understanding of the cross-business and cross-functional aspects of supply chain management in additional to the technical aspects of data.

Bob Ferrari