Stuck in analytic bureaucracy?
In “You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role” McKinsey’s Henke, Levine and McInerney recommend developing “Analytics Translators” who are professionals with a “working knowledge of AI and analytics [who] convey these business goals to the data professionals who will create the models and solutions.”
To understand more about what translators are, it’s important to first understand what they aren’t. Translators are neither data architects nor data engineers. They’re not even necessarily dedicated analytics professionals, and they don’t possess deep technical expertise in programming or modeling.
Their idea is that focusing on developing and hiring professionals with these talents will alleviate the challenges of developing and hiring the forever rare data scientists. Perhaps this is because I’m in the Bay Area, where LinkedIn’s Workforce Report lists Perl/Python/Ruby as the most abundant skills, but it seems to me that domain expertise, within an industry and a company, is significantly more rare than a “working knowledge of AI and analytics” (which the authors recommend acquiring by taking their courses).
Instead of building larger and more complex analytic bureaucracies, enable your existing organizations to increase their throughput.
Create analytic throughput
If the answer isn’t hiring more data scientists, and it’s not training a new layer to put in-between business leaders and data scientists, then what is it? It’s increasing the analytic throughput of your existing analytic organizations.
Analytic organizations spend 60% of their time getting to insight, but just 27% of that time is spent on actual analysis, the rest is on finding and preparing data.