The data science valley of despair is real. Time after time, leaders who’re well-versed in case studies and industry research extolling the returns of data-driven insights seek to innovate their business — and land in a hole of frustration and write-offs. It may be more accurate to call it a crater of despair given that Gartner predicts 85% of data science projects will fail (2018). What do the 15% of successful data science projects have in common? A lot — including careful consideration regarding whether the data scientists working on a given project were hired immediately after graduating from an analytics program or were existing employees upskilled in-house.
On the surface, it may seem inane. Now that leaders can hire a candidate right out of school with a bachelor’s or master’s degree in data science who is well-versed in the latest and greatest tools and techniques, why would they bear the cost and time delay to train one instead? Assuming you had the time and ability to replicate a world-class data science program, wouldn’t it be at best, inefficient and at worst, ineffective?
It depends on your domain — or more specifically, your data’s complexity and lineage.
Formal data science education delivered by universities, MOOCs, and other means can only effectively cover 2 of the 3 interdisciplinary skills required to be successful in the role: statistics and computer science. The 3rd interdisciplinary skill, domain knowledge, cannot be taught en masse because it isn’t consistent across industries — or even companies. No institution can teach the intricacies of your data. There will be a knowledge gap. The question is, how wide? Crater? Valley? Or navigable pass?
Data is a language — every company, if not every business unit, speaks its own dialect. As with the spoken word, these differences came about organically, and vary or evolve based on the group’s needs. Remember life before “bling?” The same is true of “channel partner.” These dialects become especially confusing for general terms which don’t conform to a common taxonomic definition. For example, IT’s “customer” is likely an employee, whereas Sales’ “customer” is typically an individual with purchasing power, who may be different from the “end user” who is referred to as the “customer” by your company’s external contact center.
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