A March 2025 Cornerstone Advisors and Apiture survey of 128 financial institutions found that many banks still run repetitive, conflicting data marts across departments — and some large institutions were only recently weaning themselves off Excel as a "source of truth" for critical business data. Cornerstone Advisors and Apiture's Data IQ report makes it clear: data fragmentation isn't a legacy problem. It's a current one.
This guide covers the governance, security, quality, and infrastructure practices that separate banks that use their data from banks that just store it.
Key Takeaways
- Treat governance, security, quality, and accessibility as one system—not separate workstreams
- Fragmentation, legacy cores, and spreadsheet reliance are still the top barriers holding banks back
- Put Master Data Management and clear ownership in place before you trust reporting or scale AI
- Strong data practices help satisfy GLBA, Basel III, and Dodd-Frank while unlocking personalization
What Is Data Management for Banks?
Data management is the end-to-end process of collecting, storing, protecting, and using data to support both compliance and business goals. It covers everything from opening a deposit account to a loan officer pulling a credit report.
Data governance is the policy layer — who owns which data, how it's classified, who can access it, and how long it's retained. Data management is the operational execution of those policies. Think of governance as the rulebook and management as the day-to-day gameplay.
Why It Matters More Than Ever
Banks are building AI-ready data foundations, and the stakes are higher. Ungoverned, inconsistent data no longer just creates reporting headaches — it undermines AI-driven underwriting models, fraud detection systems, and portfolio risk decisions.
Deloitte frames this as a dual mandate. Data management must serve:
- Offensive goals — automating finance and risk processes, enabling real-time insights, using granular customer data for hyper-personalized services
- Defensive goals — meeting regulatory demands for high-quality data, improving accuracy early in the data lifecycle, managing cyber and data risk
Deloitte's financial services data management framework notes that corporate spending on data is projected to grow 15% annually between 2025 and 2030. Banks that invest only in compliance checkboxes will lag peers who treat data as core infrastructure for both risk control and growth.
Common Data Management Challenges in Banking
Most banks already hold vast amounts of data. Too little of it is clean, connected, and usable.
Fragmentation and Silos
Core banking systems, departmental spreadsheets, and legacy platforms rarely talk to each other. The Cornerstone/Apiture research found banks running duplicate, conflicting local data marts, and some institutions only recently retired Excel as a system of record. When your commercial lending team and your risk team pull different numbers for the same customer, trust in reporting erodes fast.
Data Quality Issues
Duplicate customer records. Inconsistent formatting. Outdated addresses. These issues cascade into bad credit decisions and inaccurate regulatory filings.
Regulatory Complexity
Banks juggle overlapping frameworks that each demand different things:
- GLBA (Safeguards Rule) — requires a documented security program for customer information
- Basel III — capital, liquidity, and risk-management reforms from the Federal Reserve
- Dodd-Frank — broad 2010 financial reform touching mortgage lending and derivatives
- DORA — applies to EU financial entities and relevant ICT providers as of January 2025 (relevant to US banks only with EU operations or ICT relationships)
Each framework demands its own flavor of data lineage and auditability. Meeting them all with fragmented data is nearly impossible.
Cybersecurity Risk
Banks are high-value targets. FS-ISAC threat intelligence, drawn from thousands of member firms globally, flags fraud, ransomware, and supply-chain compromise as persistent top risks.
IBM's 2025 Cost of a Data Breach Report found that 97% of organizations with an AI-related security incident lacked proper AI access controls. Balancing broad data access for decision-making against tight protection remains the constant tension.
5 Best Practices for Data Management in Banks
1. Establish Data Ownership and Governance
Assign stewards to key domains (customer, product, and risk) and document data lineage for each. Without a named owner, data quality issues sit unresolved for months. Give stewards authority to set quality rules and escalate gaps so ownership is enforceable, not ceremonial.
2. Catalog Existing Systems and Eliminate Silos
Map where data actually lives across core systems, spreadsheets, and departmental tools before attempting consolidation. You can't fix what you haven't inventoried. Rank sources by business criticality and known error rates so integration work starts where risk and value are highest.
3. Define a Shared Semantic Layer
Create a common business vocabulary. Cornerstone and Apiture research recommends that each institution build its own data dictionary so departments interpret metrics like "deposits" the same way across the organization.
4. Build Management-Ready Dashboards
Prioritize dashboards on strategic metrics such as customer profitability, concentration risk, and deposit trends over static operational reports that just recite numbers without context. Pair each metric with a clear owner and decision trigger so leaders act on the signal instead of watching charts.
5. Embed Continuous Improvement
Set measurable goals for data quality, access, and usage. Then monitor and refine on an ongoing basis. Treat data management as an ongoing operating discipline with regular reviews, not a one-time project.
Real-world example: Texas National Bank built a data-driven small-business lending process using Plaid, Yodlee, and Finicity data alongside OCR of bank statements and a transaction classifier. According to American Banker's 2023 case study, the bank originated 15,000 loans and funded roughly $400 million for small businesses using this integrated data approach.
Data Governance, Security, and Compliance Essentials
Core Governance Pillars
Solid governance rests on three things: data classification (what's sensitive, what isn't), documented access controls, and clear retention/lifecycle policies. Skip any one of these and audits become painful.
Security Measures That Matter
- End-to-end encryption, both at rest and in transit
- Audit trails for every data access event, with role-based access and segregation of duties
- Metadata management for full traceability
Master Data Management (MDM)
Gartner defines MDM as a discipline where business and IT collaborate to ensure uniformity, accuracy, and accountability for shared master data: customers, products, and account hierarchies. In practice, MDM means one "golden record" per customer instead of five slightly different versions scattered across systems.
AI-Era Governance Needs
Newer governance demands include model lineage tracking and third-party AI vendor risk oversight. Federal Reserve guidance (SR letters on model risk management) calls for model inventories, documented validation, and ongoing monitoring. That same discipline now extends to AI models used in underwriting and fraud detection.
Clean, well-governed, consistently structured data is what holds up in exams and earns customer trust. Treat classification, access, retention, MDM, and model oversight as one operating system—not separate projects—and compliance stops being a scramble before each audit.
Choosing the Right Tools and Technology
Banks don't need a seven-figure IT budget to start improving data management. Options range from cloud data warehouses (like Snowflake's financial services platform) to dedicated data catalogs (like Alation) to governance-first platforms (like Collibra).
Centralized governance platforms offer tighter control but slower adoption, since every change goes through approval workflows. Flexible data catalogs encourage collaboration and faster self-service but require stronger internal discipline to avoid drift.
When evaluating tools, prioritize:
- Integration with core banking systems: a beautiful dashboard is useless if it can't pull live core data
- Ease of use: usable by non-technical staff, not just data engineers
- Total cost of ownership: implementation and training costs, not just license price
Treat analyst rankings as a starting point, then validate shortlisted tools against your core systems and workflows.
Frequently Asked Questions
What are the 5 steps to data management?
Establish ownership and governance, catalog existing systems, define a shared semantic layer, build management-ready dashboards, and embed continuous improvement. These steps move a bank from fragmented data to a trusted, usable system.
What is MDM in banking?
Master Data Management is the practice of maintaining a single, accurate source of truth for critical data, such as customer and product records, across all banking systems. It eliminates duplicate or conflicting records that undermine reporting.
Which CRM is used by banks?
Banks commonly use enterprise CRMs like Salesforce Financial Services Cloud, Microsoft Dynamics 365, or core-banking-integrated CRMs. Selection typically depends on integration needs and compliance requirements rather than brand preference alone.
Why do banks struggle with data management despite having so much data?
Fragmentation, legacy systems, and weak governance are the main culprits. Banks end up "data rich but insight poor" because the data exists in silos that don't talk to each other.
How does data governance differ from data management in banking?
Governance sets the policies and accountability: who owns data, how it's classified, and how long it's kept. Management covers the operational execution: actually collecting, storing, and using that data day to day.
What regulations most affect bank data management practices in the US?
GLBA's Safeguards Rule, Basel III, and Dodd-Frank are the core US frameworks. GDPR applies only where a bank has qualifying EU operations, and emerging state-level AI laws (like Colorado's) are increasingly relevant too.
