Why Strategy and Analytics (Together) are the Future of AI … And how AI will thus kill data visualization tools

In my last company, I led the development of a major data visualization addition to our core product. It allowed our product to be used for the first time by the C-Suite of our client base and that alone raised our price point considerably. But one problem kept recurring: smart people didn’t have the time or wherewithal to figure out what to do with the reports. They wanted guidance from a system that had seen more than they had which could automatically bring the next steps to them. They wanted AI, not charts.

So when my colleagues and I started our newest company 18 months ago, we said “no charts, no reports” would be our mantra. We invested our time deeply in strategic understanding of our buyers’ needs and built AI engines to support day-to-day nudges. The convergence of strategy and analytics was a journey that we’re still on, but the road we’ve traveled so far has made it clear to me that strategy is an equal partner with analytics in the pursuit of AI.

Strategy and analytics should already be inseparable even without considering AI
I believe the most valuable and innovative companies of the next twenty-five years will be those that build their strategies and product around unique data or analytics/algorithms.

Corporate strategy today needs to be heavily-anchored in data: data should guide strategic decisions and investments. I also believe building an analytics flywheel to create new innovation and customer value over time needs to be a major consideration of most strategies.

So why shouldn’t the corporate leadership in strategy and analytics be the same role? It’s a joint role I’ve played many times now and one I’m seeing it gaining in popularity.

In short, analytics is the new strategy.


In a prior article I wrote that strategy “identifies a buyer, their need/value pool, our chosen mechanism for addressing that need and why it will be effective, the unique skills/assets/capabilities we have, and how all those things will come together to uniquely serve the buyers’ needs while delighting our users.”

To do this, you need to do real problem-solving work. You need real data from your market and your customers. You need to think. It takes time. It might take a full-time team year-round to keep it alive, tested, and updated.


Deploying analytics as a function is not super hard to do these days. There has been a proliferation of tools and knowledge in recent years. Companies looking for analytics expertise can simply hire it.
Unless there’s a clear, well-tested user need that a distinctive analytic solves that your company can expand the natural value of… then what you’ve got is analytics without strategy.

Analytics is not “adding pie charts to a dashboard”
Nowadays, anyone can add charts to their platform. Dashboards are everywhere, but what users really need us a thought partner: “What do we do with the data? Tell me the actions I should take now based on the data.”

Analytics is the tool that lets you bring fast-product-feedback-loops and network effects into your product strategy and product development. This is not a technical capability. Like everything else strategy-related, it is the hard, problem-solving work of finding a path to outsized, meaningful customer value based on the core skills and assets you are already working with.

Analytics and strategy together will power the rise of AI … and “data visualization” tools, like Tableau, will soon become collateral damage
Data visualization tools have been great at democratizing the ability to make beautiful charts. But the vast majority of users don’t know how to act on charts. They don’t want more things to login to, filter and explore, then figure out what it is telling them, then figure out what to do about it. That “pull” approach requires sophisticated curiosity. 99% of users, at least in my experience, struggle to interact with those visuals. What they want is the simplest answer to “what do I do?” pushed to them.

For me that’s why strategy (thinking outside the data) and analytics are a powerful combination.

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All books and other resources referenced in this article