Many banks focus on adopting the latest data platform or pushing a leading-edge application feature—without building the culture to sustain seamless, end-to-end value creation.
By Young Pham
Banking is undergoing a significant digital transformation. Data increasingly powers decision-making at every organizational level, from sales and marketing to risk mitigation. In other words, banks today are going “data-first.”But too often, this transformation is unfocused. Or focuses on issues that creates problems for banks rather than the initial solutions they set out to find. It’s a matter of emphasis. Often, there’s a laser focus on data collection and enabling technologies—without examining the underlying culture around the data.
The effect is like trying to fix a faulty car engine with a few nuts and bolts instead of diagnosing the core problem. Your car might make it out of the shop. But it will start sputtering again down the road.
To become a data-first organization, banks must ensure all teams share a common approach to data. And workgroups should align on desired outcomes and opportunities for value creation. The key, though, is to understand which barriers get in the way of creating cultural change. Let’s look at three of the biggest ones.
Barrier #1: an overly cautious approach to data
If the last 20 years of digital transformation have shown anything, speed is critical to success. Just look at fintech firms, from which new solutions emerge continually.
That speed-first mindset has proven valuable for customer-focused data teams. Who have been able to make fast-paced, iterative improvements to customer-facing technology, like enabling instant bank account creation and advanced autosave features. And as a result, they are well-equipped to keep pace with changing customer expectations.
But this mentality has yet to take hold for teams working on core platforms. That is in part because they have different priorities. Instead of building customer-facing apps, these teams are likely using customer data to improve internal operations, such as credit risk analysis and loan repayment prediction.
This work demands high-quality, secure data—especially given the recent data breaches throughout the financial services and financial data industries. But it also results in an incredibly cautious approach to data. And a slow-and-steady mindset can keep banks from proactively leveraging data throughout the organization.
Instead, when leveraging data, teams benefit when they strike a balance between speed and caution. The key is to help internally focused employees see the impact of their work on customers. These teams need to understand the value of data not just as a defensive tool but as an enabler of marketing, revenue and customer satisfaction.
Barrier #2: unclear data goals
Many banks take a “more, more, more” approach to data. Many are focused on collecting as much information as possible, thinking they can refine it into something workable down the line.
Instead, banks benefit when they set clear data goals.
A customer service team, for instance, might want a data team to analyze customer service call logs regularly. But the data team might need more direction upfront (e.g., to look for factors impacting wait times, such as average call duration or inadequate staffing). As a result, it can’t appropriately gather information or build models. And the collected data becomes useless.
But the impact goes beyond usability. At the cultural level, an unfocused data approach can breed mistrust among teams. For example, the customer service team might blame data folks for irrelevant or inaccurate information, and the data team might say they should have told them what to look for. To overcome these problems, define your desired data-driven outcomes before collecting data.
For example, if a business team wants to offer preapproved credit cards to increase revenue from existing customers instead of adding new customers, the data used to create a target list for the preapproved offer should align with that goal. By aligning data strategy with desired outcomes, business and data analysts work together more efficiently and effectively.
Barrier #3: siloed perspectives within a value stream
Data teams often support specific divisional projects or applications. In retail banking, for example, one group might work exclusively on improving a customer service chatbot, while another might focus on refining the digital money movement process.
With such narrow focus areas, it’s easy for teams to silo the data they work with without considering its big-picture relevance. Improving the chatbot experience, for instance, is about more than the chatbot itself. It’s part of a broader value stream to enhance the retail customer experience.
To become data-first, banks must encourage teams to think about the big-picture value stream and collectively use data to work toward a common cause. So if a chatbot team analyzes chat logs or aggregated user feedback, it can share that information with other parts of the value stream (e.g., teams optimizing the online banking portal or digital money movement process). By de-siloing data, teams can work together to deliver more value to customers.
The good news is that data analysts are helping bridge the gap between silos at many banks. They are extracting insights from one side of the business and relaying them to the marketing or business enablement teams, resulting in broader applications for data throughout the organization.
Culture impacts everything
The conversation around data culture has mostly stayed the same over the last 15 years. Too many banks still focus on adopting the latest data platform or pushing a leading-edge application feature—without building the culture to sustain seamless, end-to-end value creation.
My message to bank leaders is simple. Strengthen your data culture now, and you can wield your data to differentiate your bank’s offerings.
Young Pham is chief strategy officer at CI&T, a global digital specialist.
