Big Data Has a Bigger Execution Problem

The financial services sector has spent the better part of a decade treating data as the definitive competitive asset. Whoever has the most transaction history, the richest behavioral signals, the deepest customer profiles, wins. That intuition has shaped acquisition strategies, technology investments and regulatory arguments alike.

An analysis from RBB Economics Associate Principal Simon Lee pushes back on that framing, and the pushback has real implications for how banks, payments firms and FinTechs think about competitive positioning, deal-making and the regulatory scrutiny coming their way.

Lee does not dispute that data confers competitive advantages. He identified four channels through which data strengthens market position: product improvement, more precise targeting and monetization, customer retention through personalization and network effects that compound over time. Each of those channels runs directly through the core business models of financial services firms.

Fraud detection models improve with richer transaction data. Credit decisioning sharpens when behavioral and income signals are combined. Personalized financial product recommendations depend on longitudinal customer data that new entrants cannot replicate in months. And the network effect dynamic is particularly acute in payments: more users generate more transaction data, which improves risk models, which attracts more users.

But Lee’s central argument is that none of this automatically translates into a competition problem.

“What matters is not simply how much data a firm has,” he wrote, “but how useful, unique, and replicable those data are, and whether they materially enhance the firm’s ability to compete in the market, or to prevent others from doing so.”

That distinction is not just relevant to regulators. It should shape how executives in financial services assess their own positions, and that of their rivals.

Six Conditions That Change the Analysis

Lee outlined six considerations that determine whether a data advantage crosses from competitive strength into something regulators should act on.

The first is substitutability. If competitors can access data of comparable utility through their own operations, commercial partnerships or open-source alternatives, the dominant firm’s dataset is not the weapon it appears to be. In banking, this is increasingly relevant as open banking frameworks in the U.K., EU, and now the U.S. create new pathways for challengers to access customer-permissioned data that was once locked inside incumbents.

The second is analytics capability. Data without the infrastructure to act on it produces no competitive advantage. Lee made a point that cuts against the pure data-hoarding narrative: a firm with a smaller dataset and stronger analytical capabilities may extract more competitive value than a larger institution sitting on vast but poorly utilized archives. This is the operational reality for many established banks competing against FinTech challengers with leaner data stacks but more sophisticated modeling.

Third is dependence on third-party infrastructure. In digital advertising, firms with large first-party datasets still depend on platforms like Google Ads to translate that data into commercial outcomes. In financial services, the parallel is firms that hold customer data but rely on third-party processors, card networks or cloud infrastructure to operationalize it. When the monetization layer sits outside your control, raw data volume tells an incomplete story.

Fourth is the diminishing returns question. Beyond a certain threshold, adding more data produces marginal gains. Where rivals have already crossed that threshold, even a substantial data gap may not translate into a meaningful performance gap.

Fifth is competitive significance. Not all data is equally useful. Real-time behavioral signals from active customers are competitively different from historical aggregates of dormant accounts. Regulators and executives who treat all data as strategically equivalent will misread both the threat and the opportunity.

Sixth is the availability of other competitive levers. A firm with less customer data than a competitor can still win on product design, pricing, speed, customer service or trust. Data advantages matter most when they are the primary battleground, which is not always the case.

What This Means for Financial Services Firms Now

The regulatory environment is tightening around data in financial services. Open banking mandates, data portability requirements and merger scrutiny focused on combined datasets are all expanding. The instinct among many incumbents has been to treat large proprietary datasets as a defense against both competitors and regulatory intervention. Lee’s framework complicates that instinct.

A dataset that regulators flag as a competition concern may not actually be delivering the advantage the firm believes it has. Conversely, a firm that assumes its data position is secure because it has more customer records than any rival may be underestimating how quickly competitors with better analytics close the gap.

Lee proposed a more useful frame for both strategic planning and regulatory engagement: assess whether the data in question is genuinely difficult to replicate or offset, whether rivals have credible paths to compete around it, and whether the advantage is actually translating into outcomes that weaken competition rather than simply producing better products.

In many cases, he argued, the answer will be that data-driven improvements produce better products and sharper rivals, not foreclosed markets.

“In such scenarios,” Lee concluded, “the possession or accumulation of data may in fact intensify rivalry by prompting innovation, improving product performance, and/or delivering efficiencies that ultimately benefit consumers.”

That conclusion will not satisfy everyone. But the underlying framework is more useful to financial services executives than the simpler story that data equals power. The real question is what the data actually does, who can get something comparable, and whether your analytics engine is good enough to make the difference.