Industry Viewpoints
Data to Action: How Banks and Credit Unions are Transforming Data into Strategic Gold
You might say I’m a recovering banker. I moved into data software and consulting three years ago, after more than 20 years in data and analytics leadership at community banks and credit unions. Through this diverse journey, I’ve gained a deep understanding of the US banking market and the challenges that face organizations of all sizes.
Today, I’m Director of Financial Services Strategy at Passerelle, a leading data technology consulting company that’s on a mission to help businesses modernize their data estates. We help banking leaders act decisively with their data—extracting data from any source, making it trusted, enriching it, and making it usable for any and all use cases.
Data estate modernization: Top of mind for today’s regional banks and credit unions
The US banking market is undergoing significant shifts, driving an urgent need for modernization. Operational inefficiencies are rampant, largely due to legacy hub-and-spoke technology and point solutions built around outdated core platforms with legacy integrations.
These fragmented systems create pain points across organizations, making it difficult for the C-Suite to gain the insights needed to address even the most straightforward business challenges.
Problems created by siloed data are only compounded by the mounting pressure from FinTech and other non-traditional competition, which are steadily eroding the relationship between consumers and banks. As data volumes and granularity increase, managing data assets—and making them usable—becomes even more difficult.
In the News: The failure of well-known banks highlights the critical need for real-time access to trusted data to respond quickly to market demands
The failure of Silicon Valley Bank and the unwinding of First Republic highlight the critical need for real-time access to trusted data for laser-fast response. In volatile markets, banking leaders must be able to act quickly and use data as a lodestar for decision-making.
Berkshire Bank, a Passerelle client for more than four years, used its modern data architecture to monitor deposits and outflows in the immediate aftermath of Silicon Valley Bank’s closures.
“We were able to immediately turn insights around. With the recent Silicon Valley Bank closure, everybody was concerned about deposits and outflows. Within six hours, we were able to run a full data analysis on deposits, what was coming in, what was going out, where was it leaving from, and who was taking it out,” said Peter Love, Chief Digital Officer at Berkshire Bank. “We had incredible and dynamic insight that would have taken us days, if not weeks to have cobbled together in our own SQL environment with Excel spreadsheets and data silos across the enterprise.”
Institutions that recognize the challenges posed by legacy technologies, a shifting landscape, and competitive pressures are actively seeking a solution. They’re searching for a single platform that can effectively handle all use cases and drive profitability and efficiency from a single point of truth.
Which path to data modernization?
Of course, modernizing and improving data systems is nothing new. I have worked with data professionals in various capacities and many of their companies have been on modernization journeys for a long time, each at different stages and with varying degrees of success.
Depending on where institutions are on their data maturity curve, they’re confronted with three options:
Homegrown, On-Prem Data Architecture: Many small and mid-market banks have this type of architecture. It’s essentially a hub-and-spoke model with add-on pieces, point solution technologies, limited integration, and no real vision of a proper architecture. Among the drawbacks of this approach is the emergence of shadow self-service across the organization, often unguided and lacking transparency. This results in missed opportunities and unleveraged data assets, creating silos and inefficiencies.
Bank in a Box: Smaller institutions with resource constraints often consider what we call the bank-in-a-box solution. It offers some nice conveniences and is generally less expensive than other alternatives. These banks and credit unions, feeling the pressure to modernize and make better use of data, are often attracted by the pre-fabricated architecture with prebuilt dashboards and outsourced data pipelines. However, this approach has some downsides. Outsourcing data management means sacrificing data competency and sovereignty. As technology stacks and data sources evolve, these institutions become dependent on third-party providers, which limits their ability to innovate and adapt. In today’s world, AI sovereignty—the ability to have total control of your data and how it is used—is crucial, so losing control over your data and intelligence can be a substantial pain point going forward.
The Modern Data Estate: The third—and best—option focuses on self-enablement for banks and credit unions. This approach puts the financial institution in charge of managing all its data assets within a scalable infrastructure and architecture. It emphasizes building data self-reliance with robust data governance and self-service capabilities at scale. This method helps institutions overcome legacy technology debt, driving growth and operational efficiency even in a volatile market.
Real-world success stories
At Passerelle, we take the third approach with our clients with great success. We are a Qlik partner systems integrator, and we equip institutions with the tools, support, and training to build scalable architectures and solve for all use cases going forward. As we know, AI can drive significant efficiency improvements once data is trusted and AI-ready.
Using our strategy, bank and credit union clients have built self-reliance with their data assets. They found immediate impact and ROI with a cloud data estate modernization that supports self-service at scale and integrates governance into the data lifecycle.
One of these examples is Camden National Bank.
Camden National Bank, the largest bank holding company in northern New England, manages nearly $6 billion in assets with 58 branches and 66 ATMs. Like other community banks, Camden National Bank wanted to maintain a strong connection to its community while modernizing its data estate.
Recognizing its existing on-prem infrastructure couldn’t support its goals, Camden National Bank engaged Passerelle to help choose the right tools and implement best practices, ultimately landing on Talend Cloud Data Management and Snowflake. Now integrated into Qlik, Talend, and the platform combines data integration, data quality, and data governance in a single, low-code platform and works with virtually any data source and architecture. Snowflake allows secure sharing of data sets, providing greater visibility across the organization and enabling more data initiatives.
Camden National Bank also adopted Data Rocket, Passerelle’s suite of accelerators, best practices, and blueprints. This enabled the bank to move larger volumes of data faster and focus its team on higher-value output. Since adopting Data Rocket, Camden National Bank has seen more than a 50% increase in generated analytics and saved the cost of seven full-time employees through automation.
Today, Camden National Bank has more than 90% of its data in a modern cloud environment. It has strong data governance and uses trusted data to continually improve efficiencies across the organization.
Another example of modernization with Data Rocket is Berkshire Bank.
Berkshire Bank, with a large commercial portfolio and wealth management subdivisions, generates over $600 million in annual revenue. When they found themselves a little behind the technology curve, a new CEO set the wheels in motion for a strategic transformation.
After adopting a cloud-based architecture, Berkshire Bank turned its focus to democratizing data. The bank adopted Talend Data Catalog and created a horizontal data democratization function, ensuring data visibility and governance across the organization. This has allowed Berkshire Bank to move at greater velocity, with full trust in its data, and with governance embedded as a fundamental part of its processes.
One more notable client is BayCoast Bank.
BayCoast Bank implemented Talend API services, which it uses to create efficiencies, open new revenue opportunities, and eliminate the need for third-party tools.
Using Talend API, BayCoast integrated its loan application process with its core banking system, introducing automation for the first time. Previously, loan approvals took up to 90 minutes and were handled manually. With this integration, loan approvals now take only seconds.
Building on this success, BayCoast Bank developed a range of further integrations to automate and improve efficiency across the organization. This allows account representatives to focus more on customer service and less on tedious tasks.
These three banks' successes are just the start of what they can accomplish with a scalable, future-ready data architecture.
We’re talking to other institutions, too, about driving new use cases with Qlik Sense AutoML, from deposit attrition and outflow to customer attrition and product and service adoption.
Seize the day: The future of banking innovation and data management demands that banks and credit unions take full ownership of their data estate
Any industry with a legacy tech stack and numerous ancillary services faces significant challenges, and banking is no exception.
From my conversations with industry leaders, it’s clear that banks need to better understand, serve, and retain customers. However, many have hit a wall with their existing infrastructure.
They need to make changes, but are often unsure what steps to take and how to best integrate new technologies. They also need guidance on standing up data governance and leveraging the benefits of AI.
The future of banking innovation and data management demands that banks and credit unions take full ownership of their data estate. Whether banks realize it or not, they possess some of the richest data imaginable. It’s crucial for institutions to take advantage of this asset. To anyone looking to harness AI’s potential, it’s vital to embark on this journey now. You don’t want to be left behind.