Commercial banks understand that data, advanced analytics and artificial intelligence (AI) are potentially powerful instruments for navigating a difficult landscape that is characterized by compressed margins, high costs to serve and disruptive competition in key segments such as small and medium businesses (SMBs). Yet, for many, the journey towards realizing the full value of their data is slower and more difficult than anticipated.

Many commercial banks have developed pockets of data and analytics excellence and scored some incremental wins along the way. Yet the goal of data-driven reinvention remains elusive for most, and the promise of using data to drive truly transformative change is tantalizingly out of reach—leaving rich veins of real-time transactional data underexploited.

Commercial banks that turn this situation around will gain handsome rewards—they will be able to uncover substantial opportunities to grow revenues, reduce costs and curb customer attrition. Perhaps even more significantly, they will be empowered to compete in new ways with fintechs, e-commerce companies, payments platforms and other rivals emerging from adjacent markets.

Such companies are using their mastery of data to target lucrative parts of the banking value chain. Consultancy 11:FS, for instance, highlights the fact that Shopify is now the tenth-largest platform providing financial services for SMBs in the US. Shopify plans to embed an end-to-end lending application programming interface (API) from Stripe Capital within its platform to offer financing to SMBs.

The market expects companies like Stripe and Shopify to show strong growth in the next few years, some of which may come at the expense of banks. It is rewarding the two companies with rich market valuations relative to even digitally mature incumbent banks. Shopify and Square command price/book ratios of around 17 and 40 respectively, compared to averages of between 1 and 2 for most large US banks.

How can banks compete more effectively with these new-age rivals? By making full use of the enormous volumes of data at their disposal to drive better decision-making, empower relationship managers, automate processes and add value for their customers. In addition to their first-party data and data from third-party providers, this includes ‘new data’—the digital dust that consumers and businesses create and that niche data technologies collect.

With these goldfields of data at their disposal, commercial lenders should have ready access to insights that empower agile decision-making. But despite the investments they have made in data, analytics and AI, many commercial banks are struggling to scale data-driven reinvention beyond proofs of concept or isolated centers of excellence.

We believe that there are four barriers preventing commercial banks from unleashing exponential returns from their data:

Breaking through these barriers starts with the recognition that data-driven reinvention is not a departmental or technology project—it is an enterprise-wide effort that requires leadership from the C-suite. Companies that excel in leveraging data—Shopify, Stripe, Alphabet, Amazon and others—have leaders who treat data as an asset and data capabilities as a core competence.