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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An often overlooked first step in maximizing your data quality is data profiling. Data profiling is valuable in improving your ability to use and analyze your data to guide decision-making. You can’t cook a recipe if your ingredients are all scattered in different areas, difficult to find, and not prepared. Your first step to cooking should always be to have your ingredients in order. Similarly, data profiling is the first step to using your data for the right purpose and simplifying your data strategy down the line. What is data profiling? Data profiling is the process of sorting, cleansing, and analyzing data to obtain a clear and accurate overview of your data. Before the data profiling process, data is harder to analyze and use appropriately. The data profiling process involves: Monitoring data Identifying errors Properly formatting information Sorting data Ultimately, data profiling helps your organization cleanse and organize your data to ensure that it can be easily understood and used to guide decision-making. Profiling your data helps to highlight quality issues, group-related data, and identify trends. Data profiling is often confused with data mining. However, there are some key considerations to keep in mind when comparing data profiling vs data mining. Profiling focuses more on maintaining the quality and consistency of the data and preparing it for use. Meanwhile, data mining focuses more on extracting valuable information from the dataset, like mathematical values and trends. What are the benefits of data profiling? Data profiling plays an important part in organizing, understanding, and interpreting your organization’s data. Better organization of your data is extremely valuable for achieving your business goals. Benefits of data profiling include: Improved data quality Better data accuracy Better insight for business decisions Saved time Easier-to-find data Fewer errors When your data quality management strategy incorporates data profiling on a regular basis, you can maximize the usability of your data. Data profiling helps to highlight and erase data quality issues to ensure that your data is consistent and easy to interpret. By addressing errors before they become a problem, you can avoid spending unnecessary money by preventing situations like returned mail and bounced emails. In doing so, you also preserve valuable time and resources by ensuring resources are allocated appropriately and that staff is not spending energy on sending communications to the wrong email or address. Altogether, your organization sees improved efficiency and saves valuable time. By highlighting errors early, data profiling saves your staff time correcting them down the road. By grouping data together, data profiling makes it easier to find data and identify trends, saving your team the time doing so. When done properly, data profiling is highly valuable. What are the types of data profiling? Data profiling is not a one-size fits all process. In fact, there are different approaches to data profiling depending on the needs of your organization. There are three important categories of data profiling. Structure discovery Structure discovery focuses primarily on validating your data and ensuring that it is formatted correctly and consistently. An example of structure discovery in action includes ensuring that a list of email addresses are formatted properly with the domain properly entered. This process may also be called structure analysis as it provides valuable information about your data, depending on the type of data you are working with. Analysis data may include basic statistical values like mean, median, mode, and standard deviation. Content discovery Content discovery is targeted toward ensuring data quality and focusing on the accuracy of the data in your database. This process targets individual pieces of data and identifies issues. Errors highlighted can include an address with no zip code or a phone number that is missing an area code. With content discovery, you are able to minimize the chances of issues that can arise from poor data quality, like sending information to an incorrect address or receiving bounced emails due to incorrect email addresses. Relationship discovery Just as one may guess based on the name, relationship discovery focuses on finding relationships and connections between your data sets. This process may highlight relationships between data elements or cells in a single database or highlight connections across multiple databases. Relationship discovery is valuable as related data should remain together to highlight trends and identify patterns. These patterns help to improve analysis and direct further business moves and communications. For example, customer contact information and order history are closely related and can guide future communication with that customer. Best practices for implementing data profiling? Properly implementing data profiling into your data quality management strategy requires a consistent commitment to the process. One of the first data profiling steps is cleansing your data to reduce errors and erase inconsistencies. It’s important to note that data profiling and data cleansing are not one-time processes and should be carried out on a regular basis. In order to carry out the process effectively, there are several data profiling techniques that you may use. You can carry out data profiling using one of three methods: Column profiling: This method highlights how often each value appears in a table, to identify frequency distribution and potential patterns. Cross-column profiling: Cross-column profiling involves two important processes. These are key analysis and dependency analysis. Key analysis scans through an entire collection of values in a table to identify a primary key Dependency analysis, on the other hand, finds dependent relationships within a dataset. Cross-table profiling: Through this method, one can identify foreign keys that link data. This approach helps sort data based on similarities and differences while also highlighting potential redundancies. To maximize the effectiveness of your data profiling, you should incorporate data profiling tools into your data management strategy. Performing data profiling without tools is tedious and time-consuming. The right data quality tools can help simplify the data profiling process and maximize its success by regularly cleansing your data and ensuring accuracy. Incorporate data profiling tools in your management strategy Altogether, data profiling is a vital part of a strong data quality management strategy, ensuring accuracy and maximizing efficiency in your organization’s data management. With an entire catalog of tools geared toward improving data quality, Experian is a valuable partner along the way. At Experian, we support your data management strategy from data cleansing to data enrichment. To learn more about data profiling tools and how they can benefit your organization, contact Experian today. Contact us to learn more
