Short cuts make long delays.
J.R.R. Tolkein
There’s a tendency in building data capacities to attempt to hire or delegate the problem away: simply bring in the smartest data scientists, the thinking goes, and voila, a data culture.
This strategy may seem like the easy path, but it ignores the institutional changes really needed to accomplish things with data.
You don’t hire a data scientist and put him or her in an isolated room with a powerful computer and unlimited access to data and then expect miracles to happen.
Sascha Schubert (source: Datanami)
Take a look at the below “Data Science Hierarchy of Needs” as an example why you can’t skip steps in thriving at data. The first steps involve collecting your data, storing it well, and building reliable workflows. Without this piping, there won’t be reliable data worth analyzing.
These tasks may sound boring, but they make all the difference in how an organization fares with data. A lone data ranger can only do so much toward your strategy without an environment of clean data and repeatable processes. Putting a data scientists in “an isolated room with a powerful computer” does not cultivate that environment.
That’s why merely hiring data scientists does not make a data culture.
So, what’s the alternative?
Data capacities must ultimately be built from within to be successful. They need to evolve up the Data Science Hierarchy of Needs: no number of isolated genius hires can shortcut it.
Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed.
Nick Harrison and Deborah O’Neill, “If Your Company Isn’t Good at Analytics, It’s Not Ready for AI” (source: Harvard Business Review)
This is a combination of improving your processes and the people entrusted with them: they are inextricably connected. Take the time to up-skill the staff that you have now. You may be surprised at their untapped potential and ideas for data strategy. After all, domain expertise is one-third of the Data Science Venn Diagram: don’t pass off this knowledge!
For these reasons, up-skilling from within is a more effective strategy than making new hires or forming new departments. I call this type of institutionalized data training program a “data academy.”
If your organization can benefit from such an offering, please don’t hesitate to get in touch or book a call with me. You can also subscribe for access to my data analytics education resource library.
Don’t take shortcuts with your data strategy. They’ll take too long.
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