Data and analytics professionals seem to be at the center of the next big race for talent. In 2015, there was a surplus of people with data science skills. Now there’s a significant shortage. By 2020, IBM expects broader demand for data and analytics talent to reach 2.7 million positions in the U.S. alone.
How to Actually Put Your Data Analysis to Good Use
A third of data analytics professionals say their company never, or only sometimes, puts their analyses to use. This calls into question the practicality of funnelling analyses through centralized teams focused on big-picture challenges. In our experience, most companies don’t need a small army of data scientists or bleeding-edge analytical techniques. What they do need are analyses that solve key commercial and operational problems. The good news is that the tools to do so are readily available — and relatively inexpensive. The same is true for processing power. Meanwhile, the vast majority of companies already store (but don’t analyze) vast amounts of commercially-relevant data and are collecting it at a faster rate than ever before. What’s missing, more often than not, is a clear strategy and operational model for using these capabilities in ways that are specific to the company’s business requirements. Any such effort depends on three basic components: People who can combine their commercial expertise with advanced analytics methods; an evidence-based approach that translates analytical know-how and an understanding of the business problem into actionable insights; and a small team of analytics professionals (not necessarily data scientists) to develop appropriate analytical tools and techniques.