What is Data Governance in Banking? (Benefits, Real-Life Use Cases & More)

By Matt Turner

Published on September 9, 2021

The Benefits of Data Governance in Banks and Financial Institutions

This blog was last updated in October 2023

Introduction to Data Governance in Banking

Banks collect and manage a lot of sensitive data. The moment an individual opens an account, you’re asking them for information that needs to be protected, from name and address to social security number. And, the data collection doesn’t stop there — rich insights like transactions and purchasing information help to round out customer profiles. With all of this collected data, financial institutions have an incredible opportunity to improve services and make informed decisions – if they can use this data safely.

Today, financial institutions find themselves competing to deliver the personalized experiences consumers have come to expect from companies like Amazon. Data is both the enabler and the competitive advantage behind these experiences.

Satyen Sangani

CEO and co-founder, Alation

Of course, bank leaders’ number one priority is their customers. Internal and external auditors work with many different systems to ensure this data is protected accordingly.

This is where data governance comes in: A robust program allows banks and financial institutions to use this data to build customer trust and still meet compliance mandates.

What is Data Governance in Banking?

Data governance means putting in place a continuous process to create and improve policies and standards around managing data to ensure that the information is usable, accessible, and protected. In banks, this means:

  • Setting data format standards

  • Identifying structured and unstructured data that needs to be protected

  • Tagging data types

  • Creating roles and assigning responsibilities

  • Establishing metrics to quantify effectiveness

  • Incorporating automation

  • Monitoring and measuring to identify improvements

Establishing a data governance program can feel like an overwhelming task, especially at the beginning. But, when you have all of the data governance puzzle pieces in place, it allows for better business strategies and supports ongoing compliance programs.

The Value of Data Governance in Banking

Data Governance in Banking: Key Steps Infographic

It’s easy for banks to look at everything from a compliance point of view. After all, they have regulators coming into their offices on a regular basis, checking off boxes and monitoring their activities. But data governance does more than just keep the auditors happy — it can support business initiatives as well. Some key benefits of a data governance program include:

Driving Innovation

Your bank may be thinking of ways to better connect with customers, creating new products, or changing how you operate. Or perhaps, your bank is looking to expand its reach by acquiring a smaller bank, or by opening a new branch. These decisions aren’t done in a silo; for senior leadership to make educated decisions to adopt innovation and move the business forward, they need access to the right data.

Enabling data access is just the first step. This data also needs to meet quality standards to be trusted. If there is duplicate, outdated, or incorrect data, you simply can’t rely on this information to make decisions. Everyone in the organization, from analysts to senior leadership, should feel comfortable and empowered to use data. Data governance programs ensure you are delivering trustworthy data.

Unlock Market Insight

Senior leadership teams and the Board of Directors at banks are continuously looking at market trends. And throughout the year, your Asset-Liability Committee (ALCO) also reviews risks from a balance sheet and market perspective.

If you want to use analytics as part of this review, you need data governance. Whether you’re looking at market share or stock prices, you want to use all the data possible (both structured and unstructured).

Improve Existing Policies and Procedures

Perhaps you already have data governance policies and procedures in place. The question is, are they working to enable your employees? And can you measure that impact and put that knowledge to use to improve your governance?

For instance, if your analytics team spends more time cleaning up data than analyzing it, that is a problem. At this point, you’re not using either your digital or human resources effectively. Adoption of a continuous governance process that includes monitoring current policies to understand their actual usage and impact, and iterating your current policies and procedures, can result in both better data management and higher quality data. This means that you’re optimizing all your resources, not just the technology ones.

Maintain Regulatory Compliance

Banks need to keep all collected information secure. As a result, you need to know what data you have access to and where it’s located at all times to ensure the right controls are in place. This is especially important as banks migrate data to the cloud. Knowing where your data is, how it needs to protected, and what regulations it may fall under is a critical step in migrating to the cloud.

With appropriate tagging, data governance helps you meet compliance requirements by establishing appropriate security and access controls.

What are the Roles of Data Governance in Banking?

Banks prioritize data protection due to handling sensitive data, such as personal info and financial transactions. Data governance is crucial to ensure safe and responsible data use.

Data Stewardship

Data stewards oversee the daily management of specific datasets, ensuring data quality and consistency. They set standards, document data lineage, and resolve data-related issues.

Data Ownership

Data owners are ultimately responsible for specific data assets, including decisions about access, security, and alignment with business goals.

Data Custodianship

Data custodians manage the technical aspects of data, including storage, access controls, and encryption, in collaboration with data owners and stewards.

Data Governance Management

Data governance managers lead the overarching data governance program. They define policies, coordinate roles, and align data governance with organizational strategies.

Chief Data Officer (CDO)

In some organizations, the chief data officer oversees the data strategy and governance, affirming alignment with business goals.

Data Quality Analysis

Data quality analysts improve data quality by creating metrics and working with data stewards to resolve data issues.

Data Compliance Oversight

Data compliance officers ensure that data governance practices meet legal and industry regulations, especially crucial in the banking sector.

Business Data Analysis

Business data analysts bridge the gap between business and IT, ensuring data governance aligns with business needs.

 IT Data Architecture

Data architects design data infrastructure and models to support data governance objectives efficiently.

Data Users

End users play a role in data governance by following data policies, using data responsibly, and providing feedback on data quality.

Executive Sponsorship

Senior executives lead and support by securing resources and encouraging the adoption of data governance.

In the banking sector, these roles work together to set data governance frameworks, define standards and policies, maintain data security and privacy, and support data-driven decision-making while complying with regulatory requirements.

5 Key Benefits of Data Governance in Banking

In banking, data governance has significant benefits while ensuring data is managed effectively, securely, and compliantly. The top five key benefits of data governance in the banking sector are:

1. Improved Data Quality

Enhances data quality by establishing standards and validation processes, leading to more accurate insights and better decision-making.

2. Enhanced Data Security

Protects and secures sensitive customer information through access controls, encryption, and data classification.

3. Regulatory Compliance

Ensures compliance with data-related regulations, reducing the risk of non-compliance penalties.

4. Effective Risk Management

Identifies and mitigates data-related risks, allowing banks to manage credit, operational, and compliance risks more effectively.

5. Data-Driven Decision-Making

Supports data-driven decision-making by providing high-quality, well-managed data for strategic choices, performance evaluation, and innovation.

Other benefits of data governance in banking include operational efficiency, enhanced customer trust, reduced costs, deeper market insights, thorough auditing, and more. Banking leaders should consider these primary benefits as a foundation for establishing effective data governance practices.

Implementing Data Governance in Banking

Implementing data governance in banking involves establishing a structured framework and processes to effectively manage, protect, and utilize data assets within the financial institution. This framework ensures data accuracy, security, and compliance with regulatory requirements.

How Does Data Governance Work in Banking?

In banking, data governance operates through a set of key principles and practices:

Data Classification

By categorizing data based on sensitivity and importance, banks can prioritize security measures and access controls for different data types. These include customer information, financial transactions, and internal records.

Governance Framework

This framework includes roles and responsibilities for data stewards, data owners, and compliance officers. It also outlines decision-making processes and establishes accountability.

Data Standards

Standards are developed to maintain data consistency and quality. This includes naming conventions, data formats, and data quality rules. Standardization ensures that data is accurate and reliable.

Data Stewards

Data stewards are assigned to specific data sets and are responsible for their ongoing management. They monitor data quality, resolve data issues, and enforce data governance policies.

Access Controls

Access controls restrict data access to authorized personnel only. By granting access rights based on roles and responsibilities, sensitive data remains protected.

Compliance and Privacy

Banks adhere to data privacy regulations, such as the GDPR or industry-specific requirements. Data governance practices ensure compliance with these regulations, reducing the risk of non-compliance penalties.

Data Quality Management

Data quality is continuously monitored and improved. Data profiling, cleansing, and validation processes maintain high data quality standards.

Documentation and Metadata

Detailed records, including data lineage and metadata, are maintained. This documentation assists in finding data, comprehending it, and meeting regulatory reporting requirements.

Training and Awareness

Employees and stakeholders receive training on data governance policies and best practices. Promoting data governance awareness and responsibility is essential.

Monitoring and Auditing

Tools and processes monitor data usage, data quality, and compliance. Regular audits help identify areas for improvement and ensure adherence to data governance policies.

Iterative Improvement

Data governance is an ongoing process that adapts to changing business needs, regulations, and technology advancements. It requires continuous assessment and refinement.

Technology Support

Data governance is supported by technology solutions that automate processes, track data quality, and facilitate collaboration among data stakeholders.

Implementing data governance in banking establishes a robust framework that ensures data is treated as a valuable asset, protects against risks, and enables informed decision-making while maintaining regulatory compliance.

Common Challenges in Implementing Data Governance within the Banking Industry

Implementing data governance in banking is essential but poses typical challenges. These stem from intricate financial data, regulatory demands, and the delicate balance between security and access. Here are common hurdles in banking data governance:

Data Complexity and Volume

Banks deal with vast volumes of data, including customer information, financial transactions, and market data. Managing this sheer volume and ensuring its quality can be overwhelming.

Data Silos

Financial institutions often have disparate systems and databases that lead to data silos. These isolated data sources hinder data visibility and accessibility.

Data Security

Banks are prime targets for cyberattacks. Striking the right balance between data security and authorized access is a delicate task.

Regulatory Compliance

The banking sector is subject to numerous data-related regulations, such as GDPR, Basel III, and the Dodd-Frank Act. Compliance with these regulations requires meticulous data governance practices.

Data Privacy

Protecting customer data and ensuring its privacy is paramount. Data governance must address data anonymization, encryption, and consent management.

Legacy Systems

Many banks still rely on legacy systems that lack modern data governance features. Integrating data governance into these systems can be challenging.

Change Management

Introducing data governance typically requires cultural and organizational shifts. Gaining support from stakeholders and employees is vital but may face resistance.

Data Quality

Data duplication, inconsistencies, and inaccuracies are data quality issues banks face.

Data Governance Framework

Creating a governance framework that matches the organization's goals and meets regulations can be challenging.

Resource Allocation

Allocating resources for data governance initiatives, including personnel and technology, requires careful planning and budgeting.

Data Ownership

Defining data ownership and assigning responsibilities can be contentious, especially in large organizations.

Data Access Controls

As in any industry, banks must balance data accessibility and security, ensuring only authorized people have access.

Data Governance Tools

There are many data governance tools and technologies available, but banks must select the ones that best align with their needs.

Training and Awareness

Continuously educating employees about data governance and promoting a culture of data responsibility is an ongoing task.

Measuring Success

Measuring the effectiveness of data governance initiatives includes quantifying their impact on the organization.

Cloud Adoption

Banks transitioning to the cloud must extend data governance practices to their cloud environments.

Tackling these challenges requires a well-planned data governance strategy, leadership commitment, team collaboration, and a focus on balancing data security, compliance, and accessibility. In banking, effective data governance is a regulatory requirement but can also be a competitive advantage.

How to Overcome the Challenges in Implementing Data Governance in the Banking Industry

Here are some key strategies to help banks overcome the challenges of implementing data governance:

Establish Clear Objectives and Roadmaps

Start with a well-defined vision and objectives for data governance. Clearly articulate what you aim to achieve and create a roadmap to guide your efforts.

Executive Buy-In and Sponsorship

Support from senior leadership and executive sponsorship can help secure necessary resources and promote a culture of data governance.

Cross-Functional Collaboration

Foster collaboration across departments and teams. Data governance is not solely an IT function; it involves business units, compliance, risk management, and more.

Data Governance Framework

Develop a comprehensive data governance framework that outlines roles, responsibilities, policies, and procedures. Ensure it aligns with regulatory requirements and industry best practices.

Data Stewardship

Appoint data stewards responsible for specific data domains. Data stewards play a critical role in ensuring data quality, ownership, and compliance within their areas of responsibility.

Data Quality Management

Implement data quality assessment tools and processes to continuously monitor and improve data quality. Address issues such as data duplication, accuracy, and consistency.

Data Classification and Tagging

Categorize data based on sensitivity and importance. Apply data classification labels and metadata to enable appropriate access controls and security measures.

Security and Access Controls

Implement robust security measures, encryption, and access controls to protect sensitive data. Allow authorized personnel to have appropriate access while preventing unauthorized access.

Change Management

Recognize that data governance may require cultural and organizational changes. Communicate the benefits, provide training, and involve employees in the process to minimize resistance.

Compliance Adherence

Stay updated on data-related regulations and ensure compliance with them. Regularly audit data governance practices to verify adherence to regulatory requirements.

Data Governance Tools

Invest in suitable data governance tools and technologies that align with your specific needs. These tools can automate processes, track data quality, and facilitate collaboration.

Data Governance Office

Consider establishing a dedicated Data Governance Office or Center of Excellence (CoE) to centralize governance efforts, provide expertise, and ensure consistency.

Education and Training

Offer ongoing training and awareness programs for employees to promote data governance best practices and create a culture of data responsibility.

Metrics and KPIs

Define key performance indicators (KPIs) and metrics to measure the effectiveness of data governance initiatives. Regularly report on progress to stakeholders.

Continuous Improvement

Embrace a mindset of continuous improvement. Regularly assess and adapt data governance practices to evolving business needs, regulations, and technological advancements.

Cloud Integration

If transitioning to the cloud, extend data governance practices to cloud environments. Ensure data is managed securely and in compliance with regulatory standards.

These best practices can help banks overcome the common obstacles of implementing data governance effectively. Successful data governance not only ensures regulatory compliance but also empowers organizations to harness the full potential of their data for informed decision-making, risk mitigation, and enhanced customer experiences.

Data Strategy in Banking: Taking an Offensive and Defensive Approach

Data strategy in banking requires taking both an offensive and defensive approach.

Most banks and financial institutions understand defensive strategies. These include things like:

  • Protecting data and enforcing data privacy

  • Ensuring the integrity of financial reports

  • Identifying, standardizing, and governing authoritative data sources

But, you also need to keep offensive data strategies in mind. This includes using data to increase:

  • Revenue

  • Profitability

  • Customer satisfaction

Balancing the offensive and defensive can be difficult, especially for banks. You want to use data to drive better customer experiences, yet you’re also saddled with intense regulatory oversight. Using data catalogs effectively can support a flexible and robust data governance strategy.

Examples of Data Governance in Banking: Real-Life Use Cases

Ultimately, data governance should be an asset for your business, not a hindrance. Take a look at how other institutions are using data governance to their advantage:

Mission Lane Enables Collaboration

With over 100 data sources, Mission Lane needed a way to document the data they have and put it to practical use to acquire and manage customers, reduce risk and determine fraud. Creating a data catalog makes it easier for analysts to discover data and assess quality. This not only increases productivity, but it also lets everyone collaborate better and make decisions more effectively.

Fifth Third Bank Reaps the Benefits of a New Approach

Fifth Third Bank has over 450 people using data every day. To better use data to improve compliance and provide an improved customer experience, they moved from a command-and-control governance approach to a non-invasive governance approach and saw significant results. Says Sebastian LaRosa, Director of Data Platforms, Vice President at Fifth Third Bank, “Data is at the core of running our business and Alation is at the center of our data ecosystem. By empowering our teams with knowledge about our data, we are lowering the barrier for generating insights, and can provide a better customer experience.”

5 Essential Data Governance Best Practices for Banking

Although there are many data governance best practices in the banking sector, there are five key principles to remember:

1. Clear Data Ownership

Assign explicit ownership of data assets to individuals or departments. This fosters accountability for data quality, security, and compliance — vital components in banking for maintaining trust and regulatory adherence.

2. Comprehensive Data Policies and Procedures

Establish well-documented data governance policies and procedures covering data classification, access controls, data retention, and data lifecycle management. These policies ensure consistent and secure data handling.

3. Data Quality Management

Implement data quality assessment tools and processes to uphold data accuracy, consistency, and reliability. High-quality data is especially vital in banking for informed decision-making and regulatory compliance.

4. Regulatory Compliance Commitment

Stay updated on data-related regulations and consistently follow them. Conduct routine audits of data procedures to ensure compliance with regulations such as GDPR, Basel, and others.

5. Data Catalog and Metadata Management

Use data catalogs to categorize and classify data. Implement metadata management for easier data discovery and tracking, making data use efficient while ensuring security and compliance.

These best practices provide a solid foundation for banks to manage data effectively, meet regulatory requirements, and leverage data for improved customer experiences and strategic decision-making.

Using Alation for Banking & Financial Services

Alation makes it easier for financial institutions to balance their offensive and defensive data strategies. Our data catalog breaks down the department-specific data silos that financial institutions often have in place, which enables leadership to drive innovation and deliver better services and experiences.

Because banks need to meet strict compliance requirements, Alation enables active data governance and facilitates compliance activities. We provide a way to comply with Basel, GDPR and many more regulations without limiting your ability to use data effectively.

The ability to use data effectively while still mitigating risks is more important than ever. Today’s data-driven businesses can use data governance to grow without worrying that their analytics will put them at risk.

Eager to discover more? Learn how Discover Financial Services accelerates innovation through faster, metadata-driven pipeline creation with Alation. Dive into our Discover Financial Services Customer Case Study.

CTA banner image of Alation’s Discover Financial Customer Case Study

  • Introduction to Data Governance in Banking
  • What is Data Governance in Banking?
  • The Value of Data Governance in Banking
  • What are the Roles of Data Governance in Banking?
  • 5 Key Benefits of Data Governance in Banking
  • Implementing Data Governance in Banking
  • Common Challenges in Implementing Data Governance within the Banking Industry
  • How to Overcome the Challenges in Implementing Data Governance in the Banking Industry
  • Data Strategy in Banking: Taking an Offensive and Defensive Approach
  • Examples of Data Governance in Banking: Real-Life Use Cases
  • 5 Essential Data Governance Best Practices for Banking
  • Using Alation for Banking & Financial Services


Tagged with