To see the future of banking, look no further than your local hardware store. What banks must do with data for investments in banking tech like cloud and AI to truly pay off, hardware stores are already doing with nuts and bolts.
Consider the 3/8-inch bolt. A 3/8-in. nut will fit regardless of the bolt’s length or maker. You can mix and match the nuts and bolts to build whatever you like. That standardization increases reliability, flexibility, creativity, and, over time, maintainability. In short, you can bank on the bolt.
Now consider the digital nuts and bolts behind a simple financial product–say, a checking account. You have a product number, various field contents, types, and lengths, and approaches to packaging it all in an product-defining record. But each bank defines its checking accounts and manages the associated data differently. The differences are typically vestiges of a bygone eras in which data storage and IT considerations carried a lot of weight while connectivity and interoperability of enterprise data mattered less.
Financial institutions that align with the top 2025 banking trends reshaping the industry can get their slice of a $20 trillion value creation opportunity by using technology to make them more competitive and appealing to digital-first consumers.
First, let’s be clear: No one’s better than banks at managing data. It’s just that data management tools have improved drastically as the need for real-time access to a plethora of data sets has exploded. That’s changed the data-management game, and many financial institutions are now playing catch-up.
Even today, an expansive view of a bank’s data often means extracting and duplicating data from various sources into data warehouses to support analytics-driven what-if scenarios and predictive capabilities. Then specialists run queries and kick out reports at business users’ requests.
Generative AI is changing that. Non-technical staff can now run their own complex queries by asking questions in plain English (or Spanish, or Chinese). Less obvious are the impacts on the digital machinery that deliver the answers. AI and machine learning thrive in huge, diverse, well-vetted, fresh data sets.
Creating these sorts of data compilations through recurrent regimes of copying data into data warehouses is no longer the state of the art. It’s time-consuming and resource-intensive, requires data integrity checks, and adds IT-security and compliance risk. Moreover, the data gets stale quickly, diluting the value of the results or even driving flawed conclusions.
Business data cloud tech offers a better way. Data clouds are cloud-based data management systems that integrate and unify all of a company’s data sources, stores, and supporting data infrastructure. They do so not by physically integrating it all or copying it into data warehouses, but by tracking and tapping into data where it resides in business systems.
A major benefit here is that implementing data cloud compels a bank to deeply understand its data landscape—a precursor to successful AI implementations.
The second initiative is around banking data standardization, part of a natural extension of banks’ move to the cloud. First came infrastructure as a service and, on its heels, platform-as-a-service, where processing moved from on-prem to managed data centers with increasing service levels.
More recently came software-as-a-service, in which banks rely on partners such as major ERP vendors to handle traditionally back-office business areas such as HR, procurement, sales and service, and, increasingly, finance and risk management.
This evolution has changed the relationship between banks and major software providers. Vendors have become partners engaged in regulatory compliance (such as with the EU Digital Operations Resilience Act, or DORA) and, just as importantly, in extracting maximum value out of the cloud transition. To date, this work focused on exploiting the cloud transition as an opportunity to improve processes under the banner of business transformation (as opposed to settling for old “lift and shift” approaches).
The near-term goal is to establish standards for business semantics for core data tech products as related to HR, finance, spend management, customer information, and sales and service.
Doing so can unlock immense value in two important ways. First, a common language will cut IT complexity and costs in terms of day-to-day operations and also reduce the price of transitioning to new service providers–the latter which could spark increased competition among hyperscalers and others vying for a bank’s SaaS business.
Second, common business semantics will enable smoother partnering with fintechs and others in a bank’s business ecosystem whose solutions can integrate much more easily. That sets the stage for creative new business models, improved customer service, and greater profitability.
It’s quite a leap from the simple 3/8-inch bolt to the re-envisioning banking industry data infrastructure. But the nuts-and-bolts concepts do apply: simplicity and standardization breed efficiency and creativity. Banks can certainly build on that.
