From Data Hoarding to Data Deployment: How to Make Your Data a Profit Center
The institutions that succeed in 2025 and beyond will be those that approach data not as an operational necessity — but a strategic asset. This means building the right infrastructure, investing in the right talent, and embedding a data-first culture across the organization.
By Pam Kaur, Head of Bank Technology at BankTech Ventures and Alex McLeod, CEO of Parlay
In 2025, financial institutions face a stark reality: Those without a comprehensive data strategy are operating blind in an increasingly competitive landscape. As margins compress and efficiency demands intensify, the gap between data-driven institutions and traditional banks is on the brink of becoming an insurmountable chasm.
The Imperative for Transformation
The hard truth facing financial institutions today is that most lack the fundamental building blocks needed to compete in a data-driven world. The vast majority of lenders and banks operate without intelligent intake systems to capture clean, structured data, without modern data lakes to aggregate and standardize information, and without the sophisticated models needed to derive actionable insights. This trinity — intelligent intake, unified data storage, and advanced analytics — forms the foundation of modern banking, yet most institutions are still struggling to catch up.
Studies consistently show that traditional banks lag significantly behind digital-first competitors in operational efficiency, with basic processes like loan origination often taking days or weeks longer. Compliance costs continue to escalate dramatically for institutions without robust data governance. Meanwhile, digital-first competitors are processing loans faster, optimizing deposit rates in real-time, and acquiring customers at a fraction of the traditional acquisition cost. This allows them to meet — even exceed — the expectations of customers seeking fast, intuitive, and digital-first interactions, products, and services.
Data as a Source of Competitive Insight
Modern data infrastructure unlocks insights that most financial institutions still can’t access. While the majority of banks operate without visibility into their fundamental metrics, leading institutions have built systems that illuminate everything from the true cost to acquire depositors to the lifetime value of multi-product relationships. They can precisely measure the effectiveness of cross-selling efforts and understand the real costs of manual processing across their operations. This visibility eliminates the dangerous blind spots that plague their competitors.
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These data-driven institutions understand the complete financial lifecycle of their customers. They can see how checking account usage patterns predict lending needs, how payroll deposit trends indicate potential for wealth management services, and how digital banking engagement correlates with retention. Unlike their competitors who rely on gut feel and historical assumptions, these leaders can anticipate customer needs and deliver solutions proactively. The competitive advantage is clear: while others guess, they know.
The insights extend beyond individual customer behavior to institutional performance. Banks can now understand the true profitability of each product, channel, and customer segment. They can identify which combination of products creates the stickiest relationships and which early warning signs predict customer attrition. This deep understanding allows institutions to make better strategic decisions faster, while competitors continue to rely on outdated heuristics and gut feelings.
Data as a Profit Center
When properly leveraged, data transforms from a cost center into a powerful driver of profitability across every key banking metric. The impact on core banking margins is profound and measurable. Financial institutions see expanded Net Interest Margins through more sophisticated pricing strategies and better risk assessment. They optimize their efficiency ratio by automating manual processes and reducing operating costs, while improving Return on Assets through more effective capital deployment.
The revenue impact comes through multiple channels. By understanding customer needs more deeply, banks can time their product offers with unprecedented precision – predicting which checking account customers are about to need a mortgage, which small business clients need additional credit lines, and which wealth management clients are accumulating excess deposits that could be better deployed. Cross-selling becomes dramatically more effective when driven by data, with institutions reporting success rates three to four times higher than traditional approaches.
The profitability impact compounds over time. As institutions gather more data, their understanding deepens and their competitive advantage grows. They can fine-tune pricing strategies to optimize margins without sacrificing market share, identify and expand their most profitable customer segments, and discover entirely new revenue streams through monetizing their transaction data.
Data as AI/ML Fuel
While data alone drives significant value, artificial intelligence and machine learning represent the next transformative leap in banking operations. This evolution promises to dramatically amplify the benefits of data-driven operations – but it’s a leap that’s impossible without the right data foundation. Financial institutions cannot simply layer AI onto broken data infrastructure and expect results.
Where basic data analytics might identify cross-selling opportunities, AI systems can predict the optimal product, timing, channel, and messaging for each offer. Where traditional data analysis spots broad patterns in customer behavior, machine learning models can detect subtle signals that predict individual customer needs before they arise. These systems continuously learn and improve, creating an ever-widening competitive gap between AI-enabled institutions and their traditional competitors.
The impact extends far beyond marketing and sales. AI-powered systems such as Loan Intelligence Systems transform core banking operations through automated loan intake and underwriting that becomes more accurate over time. Real-time fraud detection can identify and adapt to new threats. Agentic AI systems can autonomously review, evaluate, and address customer requests. Dynamic models can respond instantly to market changes and cross-check against risks such as portfolio concentration and overexposure. Perhaps most importantly, these systems enable truly personalized banking at scale – delivering customized experiences to millions of customers simultaneously while actually reducing operational costs.
These capabilities extend to customer engagement, where AI-powered systems provide personalized service at scale. They can predict customer needs, automate routine transactions, and flag opportunities for human intervention when needed. The result is a more efficient and personalized operation that feels personal to customers while also freeing up bank staff bandwidth to concentrate on solving more complex, high-touch needs.
The Cost of Inaction (Why Hoarding Is Not a Strategy)
For years, financial institutions have treated data collection as an end goal rather than the starting point. Banks boast about the terabytes or even petabytes of data they collect but fail to realize that the volume of data is meaningless without the tools, processes, and strategies to turn that raw information into actionable insights.
The issue with data hoarding is that it creates a false sense of security. Banks believe that because they have so much data, they’re prepared for a digital future. In reality, this unchecked accumulation often leads to fragmented silos, outdated datasets, and operational inefficiencies. Without investments in intelligent intake, data cleaning, and centralized storage, banks are sitting on a pile of data that is at best underutilized and at worst a liability.
Worse, hoarding data without strategy increases risk. Poorly managed data creates significant challenges for compliance with privacy regulations like GDPR, CCPA, and the emerging U.S. federal standards. Data breaches become more likely when organizations fail to organize and govern their data effectively. For many institutions, the cost of managing and securing disorganized datasets outweighs any potential value they might generate.
The Path Forward
The institutions that succeed in 2025 and beyond will be those that approach data not as an operational necessity, but as a strategic asset. This means building the right infrastructure, investing in the right talent, and embedding a data-first culture across the organization. It means having the courage to act decisively and the foresight to adapt to a rapidly changing environment. The leaders in this space are already reaping the rewards — growing market share, improving profitability, and delivering exceptional customer experiences. The question is no longer whether to embrace data transformation, but how fast institutions can make the leap before the window of opportunity closes.
The views expressed in this article are solely those of Pam Kaur and do not reflect those of BankTech Ventures. BankTech is not an investor or affiliated with Parlay. A list of BankTech portfolio companies can be found on their website.
