Regulation is no longer just about policies, paperwork and audits. It is increasingly about data.
If your organization needs to meet requirements such as the Basel Committee on Banking Supervision’s Principles for effective risk data aggregation and risk reporting (BCBS 239), the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) or new environmental, social and governance (ESG) expectations, you need to show that the data behind your controls is accurate, complete and reliable. It is not enough to have controls in place. You also need to prove the data supporting them can be trusted.
That is why data quality is no longer a back-office technical task and is becoming a core regulatory control.
Why regulators are looking more closely at data
Regulators are asking tougher questions:
- Can you trace reported figures back to source systems?
- Can you show that data is consistent across different platforms and teams?
- Can you spot and fix errors quickly?
- Can you explain how data moves, changes and is used?
These questions matter because weak data creates real regulatory risk. Inaccurate financial reporting, incomplete customer records and poor visibility across systems can all undermine compliance efforts, including know your customer (KYC) and anti-money laundering (AML) processes.
Why a reactive approach no longer works
Many organizations still handle data quality reactively. When a problem appears, one team fixes it. But without a broader approach, the same issue often appears again in another system, process or team.
Today, you need to build data quality into the full data lifecycle. That means monitoring data continuously, applying consistent rules, and automating validation, cleansing and enrichment wherever possible. It also means making every step visible and explainable.
Benefits of turning data quality into a control framework
When you treat data quality as a control, you move from reacting to issues to preventing them.
That shift helps you:
- Reduce compliance risk
- Produce more consistent, audit-ready reporting
- Improve trust in the data used across your business
- Respond faster to regulatory questions
It also helps your teams spend less time correcting problems and more time acting on reliable information.
How Experian helps your build trust in your data
Experian helps organizations put data quality into practice as a structured, repeatable control framework.
With Aperture Data Studio, you can profile and explore complex data, uncover anomalies and spot hidden relationships across systems. That gives you a clearer starting point, especially when legacy platforms and silos make it hard to see what is really happening in your data.
Aperture Data Studio allows you to define and apply standard rules that support regulatory expectations. Instead of relying on manual checks or disconnected processes, you can monitor data continuously and produce consistent, audit-ready outputs.
- Improve accuracy at scale
Reliable reporting starts with reliable inputs. Trusted data quality helps you cleanse, standardize and enrich data at scale, including capabilities such as address validation, deduplication and data enrichment. That helps you keep customer and operational data accurate and consistent. In areas such as AML and KYC, even small improvements in data accuracy can make a big difference. You can reduce false positives, improve risk detection and strengthen compliance processes overall. - Create a clearer view of customers and counterparties
Many regulations depend on having one accurate view of a customer, counterparty or exposure. Matching and entity resolution capabilities help you connect fragmented records across systems with greater precision. That makes it easier to spot relationships, reduce duplication and avoid missing the links that matter most. - Make your controls easier to explain
Strong controls need strong visibility. Regulators expect you to explain how data is created, transformed and validated. To accomplish this, you’ll need data lineage and governance capabilities that help you document rules, transformations and controls clearly. That visibility makes it easier to answer regulatory questions and demonstrate confidence in your data processes.
Compliance is only part of the value
Better data quality does more than support compliance. When your data is trusted, you can make decisions faster, reduce manual effort and improve the experiences you deliver to customers. What starts as a regulatory requirement can quickly become a business advantage. In other words, data quality does not just help you stay compliant. It helps you work smarter and compete more effectively.
As regulation evolves, organizations need to see data for what it is: the foundation of compliance. By identifying critical data elements, setting clear quality standards and monitoring data continuously, you can turn data quality into a stronger control. With the right tools in place, that control can scale with your business.
Data quality is no longer just a supporting function. It is becoming one of the clearest ways to build trust, improve resilience and turn compliance into competitive advantage.
Get in touch with a data expert to explore how Experian can help your organization.
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Anyone can set a goal of saving money, but it is impossible to reach that goal if you don’t know when to put money away, don’t monitor your spending, or never review your bank statements. Any time you set a goal, monitoring your path to success is crucial to achieving it, and achieving effective data governance is no different. Establishing metrics and performance indicators allows you to measure progress and adjust your data governance strategy as necessary to maximize success. Once you establish proper data governance metrics and KPIs based on your business goals, you can take your data governance strategy and success to the next level. What are data governance metrics and KPIs? Data governance is the process used to ensure data follows strict rules and regulations as it enters a database and is used throughout an organization. Data governance metrics are indicators put in place in order to measure the effectiveness of an organization’s data governance processes. Key performance indicators (KPIs) provide additional ways to measure progress.Data governance centers around three key areas—people, process, and technology. Because data governance is a comprehensive process and is often more difficult to measure, organizations need to establish metrics in each of the three areas to experience success.Effective data governance strategies are crucial for organizations to stay in compliance with regulations, protect consumer data, and ensure that data is reliable for gaining insights and informing future business strategy. By using data governance metrics, organizations and businesses can ensure they remain in compliance and maintain quality, valuable data. Why are data governance metrics important? Data governance metrics and key performance indicators (KPIs) are vital for measuring success and monitoring progress. Measuring progress in data governance is important for determining whether or not your current data governance practices are effective and how to improve them if necessary.When you focus on KPIs for governance and compliance, you are able to determine how effective your processes are and how they may need to be adapted over time. After all, setting a strategy and hoping for the best is not a recipe for success, as you can never address issues that you are not aware of.Data governance metrics are powerful tools for any data-driven business, as they allow businesses to: Highlight the effectiveness of a data governance strategy on areas like data quality and accuracy Demonstrate the success of data governance initiatives to stakeholders Uncover areas of improvement in your data governance and data management strategies Identify the need for a change of approach or priorities in your data governance initiatives Without focusing on data governance metrics, it can be easy for your best processes and strategies to fall by the wayside. Above all else, metrics and key performance indicators help provide accountability and ensure that your business maintains strong standards in its data governance procedures. How to measure success in data governance One of the first steps in measuring success in data governance is determining what success looks like for your business. To do so, you must consider your business objectives, what operations data governance will improve, the desired data governance outcomes, and what stakeholders stand to benefit. Generally, to reach success in data governance, you should focus on four key areas to measure your business’s effectiveness in getting there. Data quality Data quality is a crucial part of any data governance strategy, and fortunately, it is not difficult to measure. Strong data quality is a direct consequence of effective data governance and data management. Some measurable aspects of data quality include: Data accuracy Data completeness Data validity Data integrity The easiest way to track your data quality is through the use of a data quality platform, which can provide a breakdown of your data’s accuracy and completeness through a dashboard and highlight areas of improvement. You can also improve data quality by using tools like email, address, and phone number verification. Each of these tools can be used in real-time or through batch cleansing methods to prevent errant data from entering your database and remove the errors that sneak in over time. People People are one of the driving forces of proper data governance in your organization. Your organization should consider who is accountable for managing data and what resources they need to succeed. For many businesses, this involves establishing set roles and teams. Some of the measurable components in this area include: How many hours of training stakeholders receive The percentage of employees trained on data literacy The percentage of employees trained on data governance strategies The number of stakeholders trained in a given time period The percentage of employees that engage with data management meetings and events Establishing these metrics allows your organization to ensure that data management teams are adequately prepared to handle tasks surrounding data governance. Process The processes within data governance help describe how your organization approaches the strategy. Processes reflect how data is collected, moved, stored, accessed, and secured. Establishing effective processes is vital for ensuring that data remains accurate and valuable throughout its life in an organization. Measurable metrics and KPIs for your business include: The number of passed data audits carried out The number or percentage of areas and standards where your business is compliant The number of employee logins to a data insights tool or dashboard Technology Once your organization has the right systems and a strong team in place, technology is what really seals the deal. Data governance technology helps to support the process in several ways, saving time and energy for the teams responsible for managing and maintaining its quality. Data governance tools can: Assist with data profiling Identify and correct errors in data entries Assess the validity of data that enters a database Securely store and maintain data Seamlessly collect and transfer data throughout the organization Given the potential functions of data governance technology, some measurable metrics in this area can include: How often you run batch cleansing processes The number of attributes profiled The number of data entries corrected by a tool The percentage of accurate data in a database Using data governance metrics to monitor and assess the effectiveness of your technology makes it easier to determine whether your tools are achieving the desired outcomes. If the metrics are not providing the returns you would hope, then you can consider if there are better ways to use the tools or if you simply need better tools. Develop your data governance metrics for sustained success It’s one thing to take strides in your data governance processes, but it is another thing to make sure that these strides are actually effective. Data governance metrics and KPIs play an important role for any data-driven business by ensuring that a data governance program is effective and achieving the desired outcomes. Establishing a strong data governance strategy is even easier with the right tools, and Experian can help. Our data quality solutions are geared toward helping you get the most out of your data, from ensuring its accuracy and completeness to gaining valuable insights and analysis from it. Contact us to learn more about how to take your data governance strategies to the next level.
