Does Your Organization Need Data Scientists?
You must have data scientists in your organization! Data’s the next oil! Data scientists will tell you what all this data means!! If you don’t want to be left behind, hire your experts now, before they’re all gone!!!
The hyperbole in the tech press about data scientists has exceeded a fever pitch, with the upshot being that there are a lot of young (and not so young) people with PhDs in data analytics that expect to be swept up to nosebleed salaries the moment that the ink on their diplomas dries. The reality, however, is considerably more muddled.
A data scientist, at the end of the day, is an applied mathematician. Their focus may be either in statistical analysis or in solving complex differential equations, typically through the use of specialized graphs called kernels. Most data scientists are also subject matter experts in a given domain, and the tools that they use may or may not be consistent from one domain to another. An economist, for instance, uses a very different set of notations than a biological researcher or an experimental physicist. Because of that, generalists, those who know the tools but not necessarily the domains, may very well not be what your organization needs if you are expecting subject matter expertise.
Analysts may or may not be data scientists. An analyst has domain expertise and the ability to both understand a situation at a strategic level and to make recommendations about how best to proceed in that area to maximize the goals of the organizations. They are, in essence, modelers, and such models can both make sense of past activity and, with predictive analytics, suggest future activity. However, this is all dependent upon having the data that’s needed when it’s needed, and upon having a clear set of objectives about what specifically needs to be modeled – and why.
This means that effective data management means going beyond the data in your databases, and building, through inferences, data – knowledge – about the world outside of the organization’s walls. It means strategic investment in solid data sources or the willingness to invest in data gathering operations, and it means keeping a much of that data contextual as possible. If your organization is not willing to make that investment, then don’t hire data scientists.
I do not believe the hype that if you are not a data-driven company that you will fail. Some, perhaps many organizations aren’t data-driven, not in any meaningful sense. This is not to say that if you are a data-driven organization you can get by without good analysts who are adept with the tools and understand their domain. It’s just important to recognize that a data scientist, or a whole department of them, is not going to transform your business into a data-driven one if the strategic will to do so isn’t there. Data science efforts usually fail not because of poor data science, but due to poor strategic management.
This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.
In media res,
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