As Deloitte puts it:
"There's massive confusion about what a data scientist actually is. For some, a person who can manage spreadsheets and do basic reporting might qualify, at least in his or her own mind. For others, data scientists are expected to blend statistical sophistication, data management skills, and business acumen. It's quite possible that the job of the data scientist, as currently defined, requires more attributes than any individual should be expected to have."In other words, as the report puts it: "Just saying you need a data scientist is like advertising a slot for a smart person who's good with numbers."
In some ways, the report's authors allege, the data talent "crunch" may be a self-fulfilling prophesy. Fears about impending shortages led to organizations "hoarding" available talent, versus "real demand":
"In response to predictions about impending shortages of qualified analysts, companies scrambled to recruit talent beyond what they actually needed. This led to experienced people being asked to carry out activities like straightforward reporting that could have been done with lower-level talent. It also led to lower-level talent doing busywork—e.g., cleaning data—that is better done by machines. In addition, startups are hiring like crazy."However, in a countervailing opinion also expressed in the same report, it's noted that data analysts with the high levels of talent and education needed are still working their way through the pipeline -- it will be several years before colleges and universities are turning out highly-trained individuals.
At the same time, organizations that rely on individuals without rigorous training, using computerized analysis and visualization tools, are running great risks. "Users who seek insights in big data have new tools that let them understand, explore, share, and apply data efficiently and collaboratively, often without analytics professionals," the report states. "But that's where the risk comes in. Eager users may choose polished graphics over data preparation and normalization and rigorous analysis. That means they may gloss over important insights and produce erroneous results."
So, is the recent perceived surge in demand for data skills a hyped-up fad, or a real need that is evolving in businesses? It is important to have people who thoroughly understand how to determine what data is telling the business. But perhaps this is an understanding many in the organization need to develop -- not just a handful of specialized and highly-trained analysts. In many organizations, there are many capable individuals who can step into data science roles if they have access to the right kind of training -- there may not be a need to scour the market for statisticians.