Data governance in banking was once just another function of the IT department, but it has become a vital component of a modern bank. Back when it was one of the many technical tasks handled behind the scenes, data governance in banking was not a priority. Today, banks better understand the need to set standards to keep data accurate, clean and secure. Effective data governance in banking enables faster decision-making, giving banks a competitive edge. Data governance also provides opportunities for banks to try something new, like offering personalized financial recommendations with the help of generative AI.
Establishing a data governance framework does not happen overnight. Banks and financial institutions must develop policies and high standards for managing data to ensure data quality, availability and security. Data governance also must be scalable to account for changes in the market and business needs. Furthermore, data governance for banks lays the foundation for applying artificial intelligence or machine learning techniques.
Banks are spending a lot of money on their data initiatives. According to Gartner, IT spending on banking and investment services will increase to $1 trillion by 2028. Developing a durable data governance framework will help banks ensure this money does not go to waste.
What is data governance in banking?
Data governance in banking refers to the framework of policies, processes and standards that ensure data across a bank is accurate and complies with laws and regulations. A data governance framework includes many ways to manage data, including through access controls, data lineage and data risk management.
Deloitte’s 2024 Banking & Capital Markets Survey indicates that US banks allocated over $5 billion to data initiatives in 2023. Yet, surveyed bank leaders cited many roadblocks — including delays in retrieving data and insufficient technical skills — that prevented them from fully capitalizing on those initiatives. This demonstrates a need for more robust data governance plans.
Data governance in banks is a starting point to apply advanced data analytics and integrate AI solutions. Creating and training an model during an AI project requires access to high-quality data. A data governance framework makes such data available while preventing data leaks and meeting compliance requirements, including with regard to data privacy.
Roadblocks to data initiatives — US banks
Source: Deloitte, 2024 Banking & Capital Markets Survey
Data governance principles
A solid data governance framework provides access to data while ensuring the data remains secure, data owners are held accountable, and banks meet regulatory compliance requirements. Such a framework is built on fundamental principles that ensure banks manage data responsibly, help to reduce risk and define standards that improve efficiency and build customer confidence.
The core principles of data governance are:
- Accountability: According to McKinsey, establishing clear responsibilities begins by assigning data ownership roles. The data owner’s responsibilities include ensuring that data is secure and meets compliance standards.
- Transparency: Banks can achieve transparency vis-à-vis stakeholders in many ways according to Deloitte. They include documenting data lineage, maintaining audit logs and standardizing reporting methods to ensure data is used as intended.
- Integrity: McKinsey notes that data accuracy and consistency must be maintained. Banks should implement data validation rules and audit data use. They also should ensure that data is consistent across platforms and systems.
- Protection: A data breach is serious. Banks that fail to protect their data may face millions of dollars in fines. They also risk losing the faith of their customers. Deloitte says that banks should use encryption to secure data, apply role-based access controls (RBAC) to limit exposure and anonymize data when analyzing it. Additionally, banks should continue to monitor new threats.
- Compliance: Data governance policies are often developed to comply with regulations and laws like the GDPR and the Gramm-Leach-Bliley Act. Regular audits, automated compliance monitoring tools, staff training and documentation are needed to meet requirements.
Data governance frameworks for banks
In addition to adopting the core principles listed above, banks need to understand how data will be used and who will use it. This helps them to establish roles and responsibilities for managing data and determine how data owners will be held accountable. This also means that the data is clearly defined and traceable.
A data governance framework for financial services includes many ways to protect banks and secure data, including encryption, access controls and regular security audits. A scalable data architecture supports the storage, flow and retrieval of data.
Data governance also ensures data compatibility and helps banks integrate existing data into various systems or migrate it to the cloud. A data lifecycle management policy establishes rules for data retention and disposition when data is no longer needed. Frequently, banks will align their disposition policy with sustainability goals.
Finally, a data governance framework sets standards for data quality. This includes making sure that data is accurate and reliable. In addition to being necessary for compliance purposes, high-quality data is necessary to develop and train AI models. Ensuring data quality prevents a delay in deployment when global banks adopt these technologies for predictive insights and automation.
Data governance challenges in banks
As banks work to establish data governance frameworks, they learn that they have challenges to overcome. In addition to data complexity, these challenges include:
- Outdated infrastructure: Legacy systems may not be capable of meeting modern data needs. They also might not communicate well with new systems, creating integration issues.
- Regulatory compliance: Banks may need to make changes to meet country-specific compliance requirements.
- Privacy and security: In addition to meeting privacy requirements, banks must safeguard their data from cybersecurity attacks and fraud.
- Bias in AI models: As AI solutions become more common, banks must ensure that their AI systems are not making prejudiced decisions.
Reasons that organizations update their data management framework
Source: Gartner—Data Governance Frameworks and Challenges
Data governance best practices for banking
In its essence, a data governance framework protects a bank and its data and mitigates common data challenges. Data governance best practices establish who owns data, how data quality will be managed and how well the bank is adhering to regulations. Below are best practices that address these concerns and are essential for developing a strategy for data governance in banking.
Clarify data ownership
Establishing who owns data ensures accountability. Designating specific employees or teams to oversee data assets helps to prevent mismanagement and improves compliance with policies and standards in the data governance framework.
Create comprehensive data policies and procedures
Creating data policies and procedures helps banks take a structured approach to handling data across operations. Regular policy updates help banks adapt to evolving regulations and data security concerns.
Manage data quality
By ensuring that data is accurate and consistent across systems, banks use reliable data to make decisions and perform risk management. Banks should also validate and monitor processes to detect and correct errors before they affect operations.
Commit to regulatory compliance
Aligning its governance framework with industry regulations helps a bank avoid fines and other penalties while maintaining legal compliance. Real-time monitoring and regular auditing ensure that banks meet legal requirements.
Manage the data catalog and metadata
Organizing data improves accessibility and usability across banking systems. Metadata improves data searches, but the information might be relevant to the user. Proper cataloging also supports better data integration and compliance tracking.
Implementing data governance in banking
Creating a framework is only part of data governance. The framework also must be implemented. There are many things to consider before implementing data governance in banks.
Classification and access controls
Banks categorize data based on sensitivity and importance. Categories might include customer data, financial transactions and internal records. Aligning access controls with roles and responsibilities helps ensure that sensitive information remains protected.
Roles and responsibilities
Data stewards, owners and compliance officers are held accountable for data and establish access controls and ensure data governance policies are enforced. Data stewards also develop data strategies, manage specific datasets, monitor for quality concerns, resolve issues and uphold data governance standards.
Compliance and privacy
Setting data compliance standards ensures banks follow regulatory and banking sector-specific requirements for managing and protecting data. In banking, these include the GDPR and the CCPA. Compliance frameworks help banks avoid millions of dollars in fines. They also help them maintain customer trust by enforcing policies on data collection, storage, access and retention. In addition to taking measures to safeguard information, there are many other steps a bank should take to ensure it is meeting compliance requirements. Regular assessments, reporting mandates and employee training sessions ensure that users are handling data as intended.
Standards and quality control
Data complexity becomes a problem when data when standards do not exist. Consistent data standards — including naming conventions, format requirements and quality rules — foster data integrity. They also improve accessibility and compatibility across systems. Information about data, including metadata and data lineage, should be documented for regulatory reporting. Data quality must be continuously monitored and improved, including by cleaning data, profiling it and validating it.
Monitoring for continuous improvement
Real-time monitoring tools let banks make changes (and monitor for external changes that affect them) before data quality becomes a concern. Despite the rigidity of policies and standards, a data governance framework for banks should be able to flex as technology improves or business needs change.
A platform for data governance
A major US bank was dealing with a complex network of branches and digital services and needed a scalable solution to improve data management. The bank wanted to make data more accessible and improve compliance. However, its existing data infrastructure had significant integration limitations. Adding new data streams was slow, and operational costs were rising. As a result, performance was inconsistent, and service was delayed.
Intellias worked with the bank to plan, develop and implement a new data governance platform. The platform included a centralized data governance system that established clear data governance policies, improved metadata visibility and created a standardized framework for handling data. It also included a business glossary, data catalog and data lineage tracking. These features helped the bank be transparent and consistent across its systems.
In addition to centralizing data, Intellias integrated the new data governance platform with AWS Databricks. This gave the bank advanced analytics capabilities. The connection to Databricks also lets the bank easily add new data sources while maintaining high governance standards. Finally, Intellias automated governance workflows with Collibra’s AI governance software. Automating workflows reduced manual intervention and operational overhead. It also improved policy enforcement and shortened issue resolution and approval process times.
The new platform by Intellias lets the bank process data more efficiently, manage metadata and secure operations more effectively. By increasing productivity while reducing risks, the platform supports the bank’s mission to improve data-driven decision-making and achieve long-term growth.
Where to start
Data governance integration for banks is more than just a compliance requirement; it’s necessary to modernize digital banks. A well-structured data governance framework designed by Intellias engineers ensures that banks maintain high data quality, security and accessibility while meeting regulatory demands. It also helps banks get deeper insights with advanced analytics and prepare for AI.
Intellias provides data governance consulting services for banks. Our engineers are also skilled at data governance software development and data governance software integration for banks. With Intellias, you will find value beyond compliance with data governance integration. We will keep your bank complaint today and prepare you for tomorrow’s legal and regulatory challenges with the next generation of financial technology.
Contact us to see how a data governance platform can help you manage your bank’s data.
