Global data is expected to reach 175 zettabytes in 2025, and a significant chunk of this data is contributed by the financial sector. Yet most financial institutions struggle to harness this data’s full potential.
Here why? Critical to utilizing this vast data effectively is ensuring high data quality and its accessibility. However, data quality and accessibility become challenging if the data governance is inadequate. Without the right data governance frameworks, data integrity is compromised, leading to inaccurate insights and flawed decision-making.
According to Deloitte, while 88% of the global banks recognize data governance as a high priority to ensure accurate decision-making, they continue to fall short of effective execution. The consequences? Compliance fines, operational inefficiencies, missed revenue opportunities, and weakened trust.
So why is an industry built on trust and precision still failing to get data governance right?
Three Major Roadblocks
1. Data Silos Undermine Governance
A global banking leader recently shared with Thoughtworks that they manage data across 20,000 unique systems. Siloed data restricts data accessibility and complicates large-scale governance efforts. As a result, institutions find themselves vulnerable to compliance failures and poor risk management.
2. Striking a Balance Between Governance and Innovation
As AI adoption accelerates in financial services, leaders struggle to reconcile robust data governance with the need for innovation. Traditional data strategies often fail to balance data accessibility with necessary controls. Consequently, some organizations implement restrictive governance measures that, while ensuring control, stifle innovation and limit data access for specific teams.
3. Inconsistent Data Culture Across the Organization
Many financial institutions struggle with fragmented data ownership—IT controls the infrastructure, while business units own the data. This lack of alignment leads to inefficiencies, mismanagement, and inconsistent governance policies across departments.
Roadmap to Eliminate Governance Gaps and Unlock Data Value
There’s a consensus that data strategies must advance to transform bank’s data governance. A two-pronged approach is suggested to unroot the existing gaps.
The first step in this shift is evaluating data maturity—a critical measure of how well a bank manages, governs, and extracts value from its data. Since data maturity levels vary, a one-size-fits-all approach doesn’t work. Instead, governance strategies should be tailored to the bank’s current capabilities. Evaluating data maturity involves assessing accessibility, quality, governance, technology adoption, and data culture. This holistic approach helps identify strengths and areas for improvement in a bank’s data landscape.
Once a bank understands and maps its data maturity level, the next step is defining a governance roadmap that eliminates roadblocks, closes compliance and operational risks, and turns data into a high-value business asset.
Here’s how the path forward looks at each stage of data maturity:
Stage 1: Foundational Governance (Early Data Maturity)
At this stage, the risk of poor governance is highest. Banks must build a secure foundation by establishing ownership, control, and cross-functional alignment.
- Establish Clear Ownership and Operating Procedures: Create data steering committees with product and functional leaders. Define clear SOPs to manage the data lifecycle and ensure enterprise-wide consistency.
- Break Down Silos with Interaction Frameworks: Create governance councils that include Risk, Compliance, IT, and Business—ensuring all stakeholders work toward common standards.
- Adopt Federated Governance Models: Enable domain-level governance execution while maintaining alignment with enterprise-wide controls. This approach reduces friction and increases accountability.
- Document and Classify Data Assets: Begin with metadata capture—taxonomies, business glossaries, data lineage—and leverage automated tools for classification to avoid manual inconsistencies.
- Implement Role & Attribute-Based Access Controls: Assign access based on data sensitivity and user roles, ensuring alignment with privacy and regulatory mandates.
- Build Basic Data Quality (DQ) Controls: Establish data quality baselines through rule-based validations, exception reporting, and reconciliation mechanisms.
- Ensure Regulatory Preparedness: Align foundational practices with global standards like BCBS 239, GDPR, and ISO 8000 to minimize exposure to compliance risks.
Stage 2: Standardized and Monitored Governance (Intermediate Maturity)
At this stage of maturity, banks need to shift their focus to scaling governance and improving real-time responsiveness to reduce the lag between data issues and business impact.
- Upskill and Monitor Governance Roles: Train data owners and stewards with domain-specific programs and track execution with governance KPIs.
- Enable Real-Time DQ Monitoring: Leverage ML-powered tools to detect anomalies early and initiate automated resolution workflows.
- Automate Compliance Submissions: Use RPA and rule engines to remove human error from recurring regulatory reporting cycles.
- Visualize Data Health: Build dashboards to monitor data quality scores, issue patterns, and unresolved risk indicators in real time.
- Enable Policy-Guided Data Sharing: Use API-driven frameworks and standardized data-sharing agreements to promote secure, governed access across teams.
Stage 3: Federated and Predictive Governance (Advanced Maturity)
When a bank is at this level, governance becomes an embedded capability—driven by automation, decentralization, and predictive controls. Here’s how to move forward:
- Proactive Risk & Compliance Management: Use AI and advanced analytics to detect non-compliance trends and risks before they escalate.
- Embed Governance in Data Pipelines: Integrate governance into Continuous Integration (CI)/Continuous Delivery (CD) pipelines through DataOps, ensuring that every data asset deployed meets policy standards.
- Empower Teams with Self-Service Governance: Build intuitive governance platforms that allow product, risk, and business teams to manage rules, access lineage, and assess data quality—without waiting on central teams.
By aligning governance initiatives with their data maturity and business strategy, banks can unlock innovation, reduce risk, and drive sustainable growth.
Robust data governance empowers banks to achieve a 15 – 20% increase in return on equity (ROE) compared to those with less effective ones according to recent reports. Beyond boosting profitability, effective data governance prevents banks from falling into serious risk traps such as regulatory penalties, operational inefficiencies, and competitive disadvantages through smarter decision making. Banks that get it right will drive higher profits, reduce fines, and unlock AI-powered innovation with more efficiently trained models at a scale.
However, banks are burdened with siloed legacy systems, fragmented data ownership, and ever-evolving regulatory landscapes. That’s why leading financial institutions are turning to tech partners like Mu Sigma—a decision sciences company that brings deep expertise in data strategy, governance frameworks, and AI-driven analytics.
Data governance is a competitive advantage when prioritized today, but a costly liability tomorrow, if neglected. The decision is yours!
About the Authors:
Richa Gupta, Business Unit Head at Mu Sigma, partners with Fortune 500 BFSI institutions to navigate digital transformation and thrive in an algorithmic world, leveraging a Continuous Service as a Software approach. Todd Wandtke is a Business Unit Head and Head of Marketing.
