Are Data Silos Standing Between Your Institution and Growth?

During the Middle Ages, mapmakers sometimes left unexplored portions of their charts blank, marking them with the phrase "Here be dragons.” The Hunt-Lenox Globe, circa 1510, is one of the oldest known globes and bears the Latin equivalent, “Hic Sunt Dracones.” These uncharted areas stifled exploration and progress until tools like the compass and astrolabe allowed navigators to connect the dots and see the bigger picture. Similarly, data silos in community financial institutions (CFIs) act as modern-day "dragons," isolating valuable customer insights and hindering innovation. By integrating data and breaking down these silos, CFIs can chart a clearer path toward customer-centric growth and groundbreaking financial solutions.The Problem with Data SilosA data silo is any data collection that’s controlled by one part of a business and isolated from the organization as a whole. Data silos are named after grain silos: big containers that hold the contents apart from outside elements. The data inside a data silo is often stored in a standalone system in a configuration or language that’s incompatible with other data sets within the same organization. They can occur naturally, usually because the business unit that creates a silo operates independently and has its own goals and IT standards. However innocently they’re created, data silos limit an organization’s ability to use all its data to make good decisions, manage its processes, and help its customers. Companies with data silos also have data that’s incomplete, inconsistent, inaccessible, insecure, and sometimes duplicative. Siloed data means less collaboration between departments and can even mean that departments have a siloed mentality, keeping their data and the insights it holds to themselves.Data silos prevent organizations from seeing the full picture. As a result, it becomes harder to identify opportunities for new products or personalized solutions. For instance, artificial intelligence (AI) runs on data. Many financial institutions are investing in AI, but those investments can’t reach their full potential without full access to an organization’s data. According to Deloitte’s 2025 Banking Industry Outlook, technology gaps caused by siloed data are a significant barrier to growth and innovation.Benefits of Breaking Down DataBreaking down data silos isn’t easy, but it is worthwhile. Many financial institutions see personalized service as a primary driver in attracting and keeping customers. A 2025 Global Banking Consumer study by Accenture notes that 75% of customers expect their banks to know them and anticipate their needs. According to McKinsey, personalization can boost revenue by 10% to 15%.When part of a financial institution doesn’t have access to a customer’s full data, though, customers must start over when they cross into different departments. Breaking down silos can help ensure continuity in a banking relationship, remembering past interactions and creating current options that align with customer needs. Analyzing customer data can reveal new opportunities, too. Data analytics can help determine which customers likely have deposits at other financial institutions. That lets your institution send out a targeted sales team to gather potentially substantial new deposits. Their chance of success is much greater if data analysis involves all of your customer data, not just some of it.AI can also be an engine for preparing data for core conversions, automating the integration of microservice-based applications, and assessing the current condition of banking systems. This work can help executives make better decisions and allocate resources more efficiently as they work to modernize their technology. Leveraging Data for Personalized Product InnovationAccenture’s study notes that banking customers want to feel like they’re talking to another person who knows them and remembers the details of their situations. A digital memory that organizes data around each customer can create this experience. That means capturing data from every interaction with any part of the institution and collecting it in one place. To provide that, begin by analyzing transaction histories. Customers leave feedback every time they do or don’t use a product. Noticing and analyzing this information can help banks offer customers personalized product recommendations and financial advice. LinkedIn’s insights on customer data in banking emphasize that analyzing existing products can inspire profitable new offshoots tailored to customer needs.A UK-based bank called Monzo does this by using behavioral segmentation to track engagement levels. Segmenting customers by attitude helped Monzo launch an investment product aimed at young, first-time investors and align its product offerings with their attitudes toward risk and saving. Incorporating that often unconscious feedback lets Monzo offer a product that meets its customers’ evolving needs. How To Break Down SilosBy their nature, silos resist data sharing. To break down data that’s spread across multiple digital “buckets,” develop integrated data pipelines. Consider migrating data to a centralized, cloud-based single platform. Data integration processes such as Extract, Transform, and Load (ETL) can send data from source systems to a data warehouse. Reverse ETL can compile data from multiple sources and hand off the compilation to be used in sales, marketing, and customer service.CFI teams should recognize that success isn’t just about individual performance — it’s about working together across departments to share data and insights. This collaboration creates a more complete view of each customer, supporting smarter decisions, better service, and stronger long-term outcomes for the institution. According to Deloitte, more than 75% of banks plan to increase investments in data management and cloud infrastructure to advance their enterprise-wide AI strategies, highlighting the critical role of modernized technology in enabling seamless data integration.Use tech tools that centralize customer data and allow real-time analytics. Consider adding this functionality to chatbots, which currently are a bit like an interactive FAQ. They’re designed to respond, not understand. Use customer data to help a chatbot anticipate customer needs. The ABA’s 2025 Bank Marketing Trends report reveals that personalized, hyper-targeted marketing — enabled by customer data platforms — has become a top priority for banks, with many using data analytics to identify customers likely to have deposits at other institutions, driving millions in new funds.By leveraging technology and good professional practices, a CFI can free data from silos and use that information to create personalized products and customer interactions that drive revenue.
 
Data silos limit an organization’s ability to use all its data sets to make good decisions, manage its processes, and help its customers. Leveraging a full set of customer and bank data can help financial institutions deliver personalized banking advice and identify new opportunities while also fueling technology updates. Good tech tools and professional practices can help CFIs break down data silos.To move forward, evaluate your institution’s current data strategy. Are you using tools that integrate data and enable real-time analytics? Are your teams aligned on customer-driven innovation? Breaking down silos isn’t just a technical project — it’s a strategic step toward building stronger relationships, delivering more value, and preparing your institution for what’s next.