The field of data analytics as we know it today owes much to John Tukey, a pioneering statistician and mathematician. His groundbreaking 1962 paper, “The Future of Data Analysis,” redefined statistics by shifting its focus from theoretical work to practical, exploratory data analysis. Tukey emphasized the iterative nature of data exploration and the importance of asking the right questions, helping to lay the foundation for modern data science. These principles remain critical in industries like banking, where financial institutions rely on data-driven insights to make strategic decisions, manage risks, and deliver personalized customer experiences.Yet, despite advancements in technology and methodology, many community financial institutions (CFIs) struggle to harness their data’s full potential. Analyzing data can point to ways of improving and targeting operations and strategies, thereby enhancing performance. Yet, some CFIs have difficulty analyzing their own data because of clunky old technology, lack of expertise, and questions about returns on investment.The Value of Data AnalyticsFinancial institutions have a wealth of data about their customers including details about their transactions and their demographics. However, that data tends to be scattered and difficult to massage. Collecting and standardizing that data can enable deep analysis, which can point to opportunities for targeted or tailored services, and areas that need improvement. Instead of making educated guesses about what customers want and need, data analytics can provide more accurate scenarios.Effective data analytics can transform the time-consuming and difficult task of extracting information and then analyzing it. This in itself can be a big boost to efficiency. The ability to better analyze your data can then be used to find areas and ways to improve operations. Data analytics can power benchmarking, which can be used to reveal how well one CFI is performing at certain tasks and services versus another. This type of benchmarking can help CFIs see where they might need to focus. Spotty Record on Data Analytics for CFIs CFIs are, of course, aware of the potential benefits of better data analysis. A survey of banks with assets below $100B found that 80% had considered investing in data analytics at some point in the previous 18 months. Yet, only 32% said they had upgraded or implemented data analysis platforms during that period. About 40% said they use data analytics across several areas of their banks, while 34% were still in the early stages of developing a data strategy. Another 16% had no strategy at all for using data analytics.When asked to grade their own effectiveness in using data analytics in particular departments, surveyed banks generally gave themselves low marks. Only around half of the respondents reported being effective in using data analytics in risk analysis and regulatory compliance. When it came to operations and marketing, only about one in three thought they were using data analytics effectively. What’s Holding Up Data Analytics Adoption and UsageCFIs face several roadblocks in their efforts to improve data analytics:
- Cost. One key hangup is the question of funding. Launching and enhancing data analytics platforms requires investment in technology. While three out of every four banks said they increased tech spending in 2024 over 2023, the average amount of that increase came to just 4%, which was spread among a host of technology demands. As the slow pace of analytics adoption suggests, there may not be enough money to go around to adequately fund effective data analytics.
- Talent shortage. Starting, running, and enhancing data analytics requires specialized skills that small banks may be hard to come by. Hiring or contracting experts runs up against the cost factor. Even if money is budgeted, finding and hiring talent can prove challenging.
- Data issues. While banks collect lots of data, the quality and accessibility of that data may pose problems. Data may be stored in different locations using differing protocols. Legacy systems may make it difficult to collect and get an accurate reading. Clean and comprehensive data is a key starting point for data analysis. Bank systems must also be compatible with data analytics platforms.
- ROI questions. Data analytics proponents may face difficulty demonstrating value-added results. While there are statistics out there touting the return on investment, how this would play out at a particular bank — and how long it would take — can prove challenging.
How CFIs Can Improve Data AnalyticsCFIs face unique challenges in leveraging data analytics, but with a strategic approach, they can unlock its full potential. Here are key steps banks can take to improve their data analytics capabilities:
- Overcome legacy system issues. Outdated legacy systems remain one of the largest barriers to effective data analytics. These systems often lack the flexibility required for modern data integration, making it difficult to extract meaningful insights. Upgrading to scalable, cloud-based solutions can provide the infrastructure needed to process and analyze data efficiently. Start by conducting a comprehensive audit of existing systems to identify weaknesses and prioritize updates in alignment with your operational goals.
- Catalog data sources and locations. Many CFIs struggle to leverage their data because they don’t have a clear understanding of what data they collect or where it’s stored. A thorough data inventory is essential. Catalog all sources of information, such as customer transaction records, CRM systems, and third-party integrations. Centralizing this information into a unified repository can improve accessibility and remove silos.
- Invest in expertise. Data analytics is only as effective as the expertise behind it. Hiring experienced staff or training existing staff in advanced data analysis, AI, and machine learning is crucial.
- Implement an advanced analytics system. Once foundational issues are addressed, CFIs should adopt robust analytics platforms tailored to their needs. Look for systems that can provide real-time insights, predictive modeling, and scalability. First, focus on a few high-impact use cases, such as risk assessment or customer segmentation. This not only allows for a smoother implementation but also highlights the early benefits of data analytics, supporting broader adoption across the institution.
By addressing these areas systematically, banks can not only improve their data analytics capabilities but also position themselves as leaders in providing data-driven financial services. With modern systems, skilled personnel, and a privacy-first approach, CFIs can transform their raw data into actionable insights that enhance decision-making and customer outcomes.
