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With more and more non-bank companies now offering banking products, data that was previously accessible only to financial institutions is now in the hands of fintechs and other companies launching embedded banking products. This data can provide a wealth of insights into the financial health of customers, along with information about how they spend their money and where.

The only question is, what to do with all of it?

This is a question banks have grappled with for decades, but for newcomers — and especially untraditional finance companies like X, Apple and Walmart that are joining the game — it may be tough to know where to even start.

As a former chief architect for a large financial institution, a previous CTO at an innovative challenger bank, and an adviser for a fintech investment firm, I’ve seen this question from all sides.

I’ve seen large banking institutions miss out on valuable revenue opportunities because their legacy systems and poor data architecture prohibit them from getting the right data at the right time. I’ve also seen fintech newcomers struggle to fully understand how their customers are using their product due to not knowing how to interpret the banking data in front of them.

The companies that really succeed are the ones that do both well. They structure their banking data in a way that allows their team to easily understand how customers are using their product. Then they’re able to turn this understanding into actionable insights that allow them to improve their customer experience and mitigate fraud.

Proper data structure

As a recovering banking chief architect, I still keep in touch with some of my friends who remain in that world. And I used to have a running joke with the chief architect of one of the largest banks in the world, based in the U.S. and who will remain nameless. Whenever I would see this friend, I’d ask him a simple question: “How many data systems do you have now?” It was a joke because he would never know the answer.

Forget learning how to use data. If you don’t have a single source of truth for your customer data that is easily readable, you’ll never be in a position to properly leverage it to scale. Unfortunately, my friend’s dilemma is all too common in the banking world, both in legacy institutions and emerging fintechs.

Fixing such a dilemma can take years — I should know, because I had to do it. But getting your data structure right can make a world of difference in the long run.