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.
Email remains one of the most important ways businesses communicate with customers and prospects. From account notifications and service updates to marketing campaigns and sales outreach, organizations depend on accurate email addresses to reach the right people. But an email address appearing in a database doesn’t necessarily mean it’s valid—or that a message sent to it will be delivered. Invalid, outdated, or incorrectly entered email addresses can increase bounce rates, waste marketing spend, and negatively affect sender reputation. Email validation helps businesses identify these issues before they interfere with customer communications What is a valid email address? A valid email address is an email that follows the correct format and can be assessed for whether it is associated with a domain capable of receiving email. A typical email address contains three basic components: name@example.com Local part: The unique name before the @ symbol, such as “name” @ symbol: Separates the local part from the domain Domain: The domain associated with the email address, such as “example.com” Correct formatting is an important first step, but syntax alone doesn’t tell you whether an email address is usable. An address can look perfectly legitimate and still be inactive, mistyped, associated with a nonexistent domain, or otherwise unable to receive messages. That’s where email validation becomes important. What makes an email address valid? Email validation can evaluate an address using multiple checks to determine whether it appears accurate and deliverable. Depending on the validation process, those checks can include: Syntax: Does the address follow accepted email formatting rules? Domain: Does the domain exist and appear capable of receiving email? Mailbox: Is there evidence that the mailbox can accept messages? Typographical errors: Does the address contain common mistakes or misspellings? Risk indicators: Are there characteristics that could make the address less useful or appropriate for business communications? Checking email addresses at the point of collection can help prevent inaccurate information from entering your database in the first place. Existing customer and prospect records can also be validated periodically to identify email data that may have become outdated. What does an invalid email address look like? Some invalid email addresses are easy to recognize. Others are much harder to spot without automated validation. For example, john.example.com is clearly invalid because it doesn’t contain an @ symbol. Other problems can be more subtle, such as a misspelled domain. Common signs of an invalid or inaccurate email address include: A missing @ symbol A missing local part or domain Incorrect email syntax Misspelled domains Accidental spaces or invalid characters A domain that doesn’t exist An address that is no longer active or deliverable These errors often originate from simple data-entry mistakes. Customers may mistype an address when completing a form, employees may enter information incorrectly, or previously accurate information may become outdated over time. At scale, even a relatively small number of bad email addresses can create significant data quality problems. Why is email validation important? An email address is often a critical link between a business and its customers. When that information is wrong, communications may never reach their intended recipient. Maintaining accurate email data can provide several business benefits. Reduce email bounce rates Sending messages to invalid or inactive addresses can result in hard bounces. Identifying problematic addresses before sending can help organizations reduce unnecessary delivery failures. Protect sender reputation Repeatedly sending email to invalid addresses can negatively affect email performance and sender reputation. Maintaining a healthier email database can support stronger email deliverability practices. Improve marketing efficiency Every message sent to an unusable address consumes resources without creating an opportunity for engagement. More accurate email lists help marketing teams focus their efforts and budgets on contacts they can actually reach. Create better customer experiences A mistyped email address can mean a customer never receives an order confirmation, account notification, password reset, or other important communication. Validating an address while a customer is entering it can give them an opportunity to correct mistakes immediately, helping create a smoother experience. Improve overall data quality Email validation isn’t only an email marketing concern. Email addresses frequently connect records across CRM, customer data, ecommerce, service, and analytics systems. Improving email accuracy can therefore contribute to better contact data and more dependable customer records throughout the organization. How to improve email data quality Accurate contact data requires ongoing maintenance. Even a correctly entered email address can eventually become outdated as people change jobs, switch providers, or stop using an account. Organizations can strengthen email data quality by: Validating email addresses during data capture Regularly reviewing existing email databases Monitoring hard bounce rates Correcting common domain and formatting errors Removing or suppressing addresses confirmed as undeliverable Establishing consistent data-entry standards across systems Integrating contact data validation into CRM and customer workflows These practices can help prevent inaccurate email data from accumulating and affecting future communications. Build more trustworthy email data with Experian Reliable customer communication starts with reliable contact data. Experian’s email validation capabilities help organizations assess email addresses for accuracy and deliverability, whether data is being entered in real time or already exists within a database. By identifying problematic email addresses earlier, businesses can reduce unnecessary bounces, protect sender reputation, and get more value from their customer communications. Email validation can also be part of a broader contact data quality strategy that keeps customer information accurate, complete, and ready to use across the organization. With better email data, businesses can spend less time dealing with preventable data quality problems and more time connecting with customers. Fill out the form to learn more:
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