In September 2019, McKinsey Quarterly released an article titled, “The analytics academy: Bridging the gap between human and artificial intelligence.”
When McKinsey (a “Big Three” management consultancy) talks, people listen. And it was a market-moving piece in the idea of an institutionalized data education strategy that has influenced my thinking greatly on the topic.
But in many ways, this article misses the mark. More specifically, I’m not sure it’s a realistic vision for what most organizations need.
Before I explain, take a moment to check out the article. Again, it’s McKinsey, so it’s not entirely meritless! But it’s not the last word on data education. So after you’re done the article, please come back here. 😉
What McKinsey gets right
The instinct among so many organizations when they know they need to dig in on data is to hire oodles of highly-credentialled outsiders, and maybe put them in a department with a cool-sounding name like the “Center for Radical Experimentation.”
This strategy is essentially a shortcut to data success, but organizations should heed Tolkein here: “Short cuts make long delays.”
In consulting-ese, McKinsey explains it like this:
While hiring new talent can address immediate resource needs, such as those required to rapidly build out an organization’s AI practice at the start, it sidesteps a critical need for most organizations: broad capability building across all levels. This is best accomplished by training current employees.
“The analytics academy: Bridging the gap between human and artificial intelligence,” McKinsey Quarterly
This statement is absolutely true and confirms to me that McKinsey can’t be all fluff.
McKinsey doesn’t go too deep into the strategy or the tactics here, in terms of things like measuring ROI, or finding candidates to build the data academy. And that’s understood, since there’s only so much you can get into a flyover consulting thought-leadership piece.
The cursory nature of this article leads to pure hand-waving, particularly when it comes to those two most enticing words: artificial intelligence.
What McKinsey gets wrong
Digging into the needs for wholesale organizational transformation to improve data capacities, McKinsey focuses entirely on artificial intelligence:
Quick-fix tactics aren’t enough to transform an organization into one that’s fully AI-driven and capable of keeping up with the blazing pace of change in both technology and the nature of business competition.
“The analytics academy: Bridging the gap between human and artificial intelligence,” McKinsey Quarterly
The thing with AI, thought is that it’s at the top of an organization’s data capacities:
So, the whole article warns against “jumping past Go” in building data capacities, but encourages organizations to focus on building AI capacities, which is “jumping past Go.” In other words, if an organization needs a data academy in the first place, they are probably not ready for AI.
Otherwise, they would have some similar institutionalized knowledge and culture for building data capacities.
Consultants will be consultants
Let’s be honest: “Artificial intelligence” sells better upstairs than “business intelligence” or “report automation,” so we can’t be too surprised that it’s the focus of consulting reports, even when most executives have admitted that their organizations aren’t ready for AI.
Take these statistics from the data science consultancy NewVantage Partners’ Big Data and AI Executive Summary 2019:
- 71.7% of firms report they have yet to forge a data culture
- 69.0% of firms report they have not created a data-driven culture
- 53.1% of firms state they are not yet treating data as a business asset
- 52.4% of firms claim they are not competing on data and analytics
I’m sure McKinsey has similar data indicating that most organizations aren’t ready for AI. Yet the focus remains there, because that’s what consultants do: get executives excited about big words, while having the little people figure out the details like, for example, having an organization that can’t keep data definitions start building AI applications. It becomes apparent only after grueling and thankless work by underlings that the tactics can’t meet the strategy, at which point the consultants are onto the next steak dinner client meeting.
Start where you are with the data academy
I fear this came off as more of a diatribe against McKinsey than intended: this article’s authors could well understand that a minority of organizations are ready for the type of academy they envision.
However, in a broader business environment where everyone wants to do AI for the sake of doing AI, when they aren’t ready for AI, it’s such an important caveat that it should be made explicit.
If you’re looking for strategy on how to up-skill your organization’s data skills, listen to McKinsey: institutionalized education is your best path. Once you’ve settled on that, though, you’ll need to find more grounded partners for the tactics.
For more on building your data academy, check out my series of posts on the topic. You can also subscribe to my newsletter for exclusive access to my data education resource library. Don’t hesitate to contact me directly, too, or schedule a call.
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