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Implementing Data Governance Frameworks in Banking for Effective Decision Making

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By Katie Joll

Banks and other financial institutions are awash in data, with forecasts showing that data volumes are expected to keep growing.

The International Data Corporation (IDC) predicts that by 2025, the global datasphere will reach 175 zettabytes, with a significant chunk of those belonging to banks. Data is a valuable, strategic asset for financial institutions, but managing that data optimally is a key challenge. 

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A Deloitte study found that 88% of banks worldwide consider data governance as a high priority in order to ensure accurate and reliable decision-making processes. Furthermore, banks with strong data governance frameworks achieve a 20% higher return on equity (ROE) than their peers with weaker data governance.

The bottom line is that effective data management and data governance in banking help to inform better decision-making and improve business and customer outcomes. Data governance structures also help financial institutions to maintain regulatory compliance – an area which is often under the spotlight for banks.

Understanding Data Governance in Banking

Here’s how data governance has an impact in banking:

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What is data governance?

Data governance sets out standards and procedures for how data is managed in an organization. These standards cover the entire lifecycle of data, from acquisition, to use, to disposal.

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Processes are a key part of data governance. These set out how data stewards and users will ensure the integrity of the data, that it is uniform across the organization, that those who need to access it can, and that security is maintained.

Key principles of a data governance framework

Data governance frameworks can and should be customized to suit the needs of the individual organization. With that said, there are some common principles that should be part of any data governance framework, particularly in banking:

  • Standardized rules and regulations – These provide the criteria for how data is used and maintained. They account for external regulations as well as internal rules.
  • Accountability – Successful data governance frameworks lay out ownership of data and accountability for how it is handled. A best practice is to install data stewards from a cross-section of departments. This helps to ensure that data is seen as an organization-wide issue, not just the domain of IT.
  • Data quality standards – These are a shared set of standards with the goal of ensuring that data is of high-quality and reliable across the organization.
  • Transparency – Data governance processes should be transparent so that even a third-party auditor can understand exactly how data is handled.
  • Data administration – A dedicated data administrator can help to ensure proper responsibility and accountability for data. Effectively, they would oversee data stewards.

Regulatory and compliance considerations for banking

According to Deloitte’s 2023 Banking Regulatory Outlook, “banks still have ‘“work to do’” to meet supervisory expectations, especially in the area of governance and controls.” As various innovations transform the way that banks operate, regulators are working on additional laws and frameworks which banks will need to adopt.

Banks and financial institutions are heavily regulated and expected to comply with standards for data security and privacy. Not only that, where auditors previously accepted documentation provided by financial institutions, they’re now increasingly requesting to see the raw data. Banks can expect that auditors will check up on every aspect of data governance and how organizational data is impacted. A robust data governance framework is an absolute must-have from a compliance standpoint.

Benefits of Implementing Data Governance Frameworks

Data governance frameworks can drive considerable value for banks. Firstly, by ensuring that data is reliable and of high-quality. Accurate data provides for better-informed decisions across the organization. Anything from risk profiles to product insights can be data-driven.

Secondly, regulatory compliance and risk management practices can be improved with a robust data governance framework. Several banks have faced consequences due to data violations which can be avoided through data governance practices. One thing data governance helps banks to do better is to know exactly what data is located where, meaning they can better enforce controls.

Increased operational efficiency and resulting cost savings are also benefits for banks from implementing data governance. Manual data management processes are tedious, time-consuming, and costly. The right data governance tools automate many parts of data management, including granting the correct access at the right time. This can relieve significant amounts of work from IT.

Data governance can also support organization-wide analysis and market insights. This makes it easier for banks to innovate and develop a data-driven culture.

Challenges in Data Governance Implementation

Data governance is essential, but implementation often has its challenges.

Cultural and organizational barriers

Philip Dutton wrote a piece for Finance Derivative in which he highlighted a warning from the Bank of England that banks have continued to fall short against data governance regulations. One reason for this, he says, is that banks’ governance strategies haven’t kept pace with updates in technology and the proliferation of data.

Why? For many there are cultural and organizational barriers. As Dutton says, “there’s a tendency for banks to have their heads in the sand rather than face and respond to the new reality.”

Change is hard. But it’s even harder if organizational culture doesn’t support it.

Legacy systems and infrastructure constraints

Many banks are working with legacy systems that create infrastructure constraints and challenges for many key aspects of data governance. For example, where data is siloed in different legacy systems, transparency is difficult and accurately integrating that data for a more complete picture can be a huge challenge.

Change management and stakeholder buy-in

Managing the move to a new data governance framework, including getting stakeholder buy-in, can be challenging for banks. As Deloitte describes it, there is still a dearth of knowledge and skills around data management, including the resources required to manage data successfully. Fragmented data ownership can contribute to this, especially if there isn’t clear ownership established between business units and IT. Operational silos contribute to this lack of accountability, making it more difficult to gain stakeholder buy-in.

Best practices for successful data governance in banking

Some best practices for implementing successful data governance in banking include the following:

Establish a data governance strategy and roadmap

A data governance roadmap provides a detailed plan for implementing data governance strategy. It reads like a project plan, with a list of tasks, the people responsible, any dependencies, and target dates for tasks. The roadmap can be shared with stakeholders so that everyone has a common understanding of the plan.

Define clear roles and responsibilities

Good data governance requires defined roles and responsibilities. These may include a data administrator (or administrators) with overall responsibility for overseeing the implementation of the data governance framework.

Data stewards also play a key role. They typically look after a department or specific dataset and are responsible for ensuring the quality of their data.

Engage stakeholders and foster collaboration

A collaborative environment is essential for successful data governance. Where new data governance frameworks are not well-supported, it is unlikely that positive changes will be made. It may be a cultural shift for some banks, particularly those that have typically wanted to stick with legacy systems and methods.

Leverage technology and automation solutions

Best-practice data governance includes putting the right technology, including finance data management software, in place. This should support automations for data management so that large-scale datasets are more easily managed. Automation helps to speed up processes, as well as minimize human error.

Continuously monitor and improve

Setting up a data governance framework isn’t a one-off activity. Continuous monitoring is required so that you notice any improvements that can be made and update procedures in accordance with any technology, regulatory, or other changes.

Case study


Implementation of robust data governance frameworks is an imperative for banks, many of which are lagging behind regulations and best practices for data management. Good data governance helps to ensure that reliable data informs better decision-making and that risks are mitigated for the bank.

Banks that implement data governance frameworks successfully enjoy higher ROE than their peers, including the advantages of better insights. While it may be challenging to implement change, managing valuable data assets well can pay off in terms of strategic advantages.

Banks are building modern, streamlined data management processes with Semarchy. Download our Master Data Management Best Practices guide for banks here.