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.
In today’s data-driven world, companies across industries recognize data as a powerful asset for decision-making. But what constitutes reliable data? How can you assess whether the data your business relies on is mature and dependable enough to instill confidence in your team and drive strategic decisions? Understanding the 10 characteristics of data quality makes it easier to evaluate and enhance the quality of your data, which will serve as a solid foundation for your organization’s growth and success. What are the 10 characteristics of data quality? The criteria for good data quality comes down to 10 data characteristics, which are: Accuracy Accessibility Completeness Consistency Validity (Integrity) Uniqueness Currency Reliability Relevancy Timeliness These elements of data quality, in one way or another, build the foundation for an organization to make the best decisions for its business and better communicate and engage with customers. 1. Accuracy The first piece of criteria for good data quality to consider is accuracy, which verifies an organization’s information is correct. The measured value should match the actual, true value, and the data must be error-free. Error-free data should be up-to-date, not redundant, and have no typos. Prioritizing data accuracy means organizations can make more informed decisions, foster stakeholder trust, and achieve better business outcomes. When developing data management strategies, remember the actual value of data lies in its accuracy and ability to represent the real world accurately. The overarching goal should be to increase data accuracy, especially as datasets grow continually. 2. Accessibility Accessible data is obtainable at the time it is needed and by those who need it. Having data readily obtainable with traceable changes allows organizations to enhance transparency, accountability, and efficiency. This data quality characteristic is essential for timely decision-making and effective business operations. Data accessibility is not just available when needed but is also usable. The ability to quickly access data allows businesses to respond promptly to market changes, internal demands, and customer needs. If data is inaccessible, its value diminishes, as delays in obtaining the necessary information can lead to missed opportunities, inefficiencies, and poor decision-making. 3. Completeness Completeness refers to whether all the necessary data is available. For instance, while a customer’s first and last name might be crucial for communication, their middle initial might be optional. Comprehensive data lets businesses engage effectively with customers, make informed decisions, and operate efficiently. Completeness is one of the critical data quality characteristics because incomplete information can render data unusable. Imagine sending out mail without the recipient’s last name. The mail might not reach the intended address, wasting resources, time, and money, ultimately leading to missed opportunities to connect with customers. Incomplete data can impact business operations, decision-making, and customer engagement. 4. Consistency Consistency is another one of the necessary characteristics of data quality. It means the same information is stored and used across multiple instances to match each other. Consistent data is the percentage of matched values across various records, making accurate and meaningful data analytics essential. Data consistency prevents organizations from using contradictory information that undermines their operations and decision-making processes. Consistent data supports accurate analytics and effective business operations and fosters trust in the information used across the enterprise. 5. Validity (Integrity) Validity is one of the data quality dimensions verifying values align with specific domain requirements and formats. It signifies that data attributes are appropriate and accurate for their intended use, which any organization needs for maintaining data integrity and usability. This fundamental characteristic of data quality makes sure data is fit for its intended purpose. By maintaining high data validity, organizations can enhance the accuracy and reliability of their data for better decision-making and operational efficiency. 6. Uniqueness Uniqueness verifies that each data point is recorded only once in a dataset, eliminating duplication and overlaps. This data quality characteristic maintains the integrity and reliability of data, supporting accurate analysis and effective decision-making. Prioritizing uniqueness improves an organization’s data accuracy and reliability, leading to more trustworthy analytics. Implementing thorough data cleansing and deduplication processes allows for keeping unique records, further building upon data governance and compliance efforts. As a result, fostering data uniqueness is key to effective data management and organizational success. 7. Currency Currency directly impacts the relevance and accuracy of the information used for decision-making. Currency refers to how recently the data was collected or updated to reflect the most up-to-date and contemporary reality. Data currency checks that data remains relevant and accurate over time. Focusing on the recency of data collection and updates allows organizations to have high data quality for more accurate insights and better decision-making. Maintaining current data is considered part of the criteria for good data quality, as it drives organizational success in a rapidly changing environment. 8. Reliability Reliability encompasses the degree to which data is factual across various sources and systems. Reliable data means the information used for analysis and decision-making falls within the acceptable values for the specific task or question. Reliable data increases trust, improves operational efficiency, and mitigates risks associated with inaccurate data. Implementing validation rules and maintaining audit trails builds data reliability. Ultimately, reliable data provides a solid foundation of accurate and trustworthy information. 9. Relevancy Relevance is one of the elements of data quality determining whether collected data serves a meaningful purpose. It pertains to whether the data collected is necessary and helpful in achieving specific business goals and objectives. Without relevancy, data collection efforts can become wasteful and unproductive. Relevant data collection efforts are purposeful and align with business goals. Data relevancy allows organizations to use resources more efficiently, extract valuable insights, and conduct focused analyses. Implementing practices to define goals, evaluate the necessity of data, and align data with applications creates relevant data. 10. Timeliness Timeliness refers to how current and up-to-date the information is. In an era where information constantly evolves, timeliness can be considered one of the mandatory data quality dimensions. Organizations can gain a competitive edge by designing timely data collection, storage, and updates and making informed decisions based on the most recent information available. Data timeliness contributes to organizational success by enacting data quality management systems supporting rapid data processing and frequent updates. Maintaining data quality High-quality data is essential for a business’s success, and incorporating these characteristics of data quality in your data management plan will save you time, money, and resources in the long term. Developing the criteria for good data quality in your organization helps you avoid relying on invalid or incomplete information. Experian’s data quality management tools can help you with your data quality platform. Contact us today to get started with cleansing and maintaining your data. Connect with a data quality expert today:
With inflation rates climbing and consumers being more conscientious of where they spend their money, maintaining a reputable brand and customer service is extremely important to business success. At the front line is data quality. Data is the lifeblood of a business. It powers users with telling metrics about customers and the company’s overall health, allowing leaders to make informed decisions on operational and marketing strategy direction. The reality is that shoppers want to feel connected with the brands they patronize, which means consumer brands are faced with the challenge (and opportunity) to create an authentic, personalized customer experience to retain customer loyalty and increase revenue. Additionally, e-commerce sales continue to rise with no sign of slowing down when consumers increasingly switch to online shopping. In fact, the International Trade Administration expects global online sales to reach $5.5 trillion by 2027. Enter the direct-to-consumer (DTC) business model, a retail landscape that positions brands to sell directly to consumers. DTC retailers can skip the wholesaler middlemen and third-party shippers altogether and can succeed without depending on larger retailers and brick-and-mortar stores to stock their products. This allows DTC businesses complete control over all operational processes, from the point of purchase to the order’s fulfillment, shipping, and delivery, making this business model increasingly more popular for even long-tenured brands that traditionally rely on wholesale to distribute their products. DTC businesses and the need for data quality go hand in hand. As its name suggests, all aspects, including marketing, of a DTC business go directly to the customer. This gives them a clear and comprehensive understanding of their customer needs. Having direct access to your customer data is incredibly beneficial to decision-making. However, data is only actionable when it is accurate and trustworthy. In this blog, we will cover the importance of data quality for DTC businesses and how Experian can help your company incorporate data quality into its strategy. 3 key benefits of data quality for DTC businesses 1. Targeted marketing campaigns Your customers are your most valuable asset, and staying close to them is crucial to test ideas, learn, and change to fit consumer needs rapidly and efficiently. To capitalize on their direct relationship with customers, DTC businesses must be targeted and intentional with their marketing strategy to ensure their campaigns resonate with customers. The best way to do this is through data enrichment, the process of incorporating new updates and information into an organization’s existing database to improve accuracy and add missing information, allowing you to get the most out of your customer data to make better decisions. Data Enrichment – Experian’s Data Enrichment service includes over 900 data attributes, including demographic data, financial data, and behavioral data, to enhance your existing contact data with additional consumer information. As a result, your team can use these valuable consumer insights to predict preferences and purchasing patterns, personalize and segment communication based on a better understanding of your customers, and create customized messages that allow you to connect and engage with your customers, increasing revenue and enhancing brand loyalty. 2. Decreased operational costs DTC businesses often have more complicated supply chain models. This can leave a wider margin for error and have a more significant potential for risk. Clean and accurate data is the answer to mitigating risk and maintaining a positive customer experience. This is why DTC companies need to invest in the correct data tools to prioritize customer loyalty and decrease operational costs down the line. Email validation – Email is one of the best ways to connect with your customers. Our research shows that 72% of business leaders say email is the top channel they rely on to reach customers. On top of that, customers simply prefer digital communications to stay in touch with a brand. However, marketing campaigns take time and human capital to execute, so they can be costly when they’re not reaching your intended target audience. Experian’s Email Validation corrects all parts of an email address (like spelling and formatting) and confirms that the email address is live and receiving mail through a silent and quick ping. This lets you verify your segments ahead of hitting send, ensuring you’re deploying your important communications to real, active inboxes. Address validation – Imagine creating a seamless fulfillment process, only to be fined for something your business had nothing to do with. Customers mistype address information during checkout all the time, and it can be particularly costly, especially to the company. Experian’s Address Validation solution leverages the most up-to-date USPS® address data to determine the accuracy and completeness of physical addresses by correcting typos from incorrectly entered mailing addresses. Our team can do this automatically and in real-time, enabling your customers to receive their shipment in time while helping you avoid hefty address correction fees. 3. Improved customer service In a macro environment where business competition is fiercer than ever, the only way to stay agile and stand out is to provide superior customer service. Building a robust data quality strategy to produce a single, holistic view of your consumer data is the first step to enabling your business to better understand your customers and their needs to create a personalized customer experience. In fact, 91% of business leaders say implementing data quality has improved business agility. Data quality platform – Experian Aperture Data Studio enables you to ingest data from a variety of sources to bring together previously siloed data sets for a single customer view. With capabilities to validate, cleanse, de-duplicate, and enrich data, the self-service platform helps users build a consistent, accurate, and complete view of their consumer data quickly and effortlessly. Bad data can have negative impacts downstream. Implementing a data quality management platform empowers modern data practitioners to understand their customers for improved marketing and increases competitive advantage through better customer experience. Connect with an expert at Experian Trusted data is a big component of success for DTC businesses, and implementing the right tools to develop a strong data quality strategy has benefits ranging from enabling targeted marketing campaigns to decreased operational costs and improved customer experience. As the macro business environment grows more competitive, organizations need to stay agile and continue implementing data quality strategies, tools, and best practices to protect themselves against competitors and unexpected market conditions. Experian offers a variety of data quality solutions that give users confidence and trust in the data used for decision-making. Get in touch with a data expert to learn how Experian can help your business get started. Have questions? Our team is here to help!
In today’s digital age, data is at the core of every business operation. Whether you find yourself leading an enterprise business or a burgeoning startup, organizations of every size across all industries are becoming increasingly reliant on data to drive everyday business strategies. But it takes more than just mere data acquisition and analytics to surpass your goals. Enter proactive data quality into the spotlight—the foundation of any data-driven strategy that will successfully guide your company. From reducing operational costs to improving the customer experience, the benefits of prioritizing data quality from the onset are unsurprisingly abundant. In this blog post, we’ll dive deeper into what proactive data truly encompasses while highlighting several of the key benefits this data offers for your business. What is proactive data? Proactive data refers to the approach of preventing data errors and issues before they occur, rather than reacting to them after they have already impacted operations. It involves implementing strategies and processes to ensure that data is accurate, complete, and reliable from the outset by emphasizing the importance of data quality. Proactive data management is crucial for businesses because it helps them avoid costly mistakes and inefficiencies. By ensuring that data is clean and accurate from the beginning, businesses can make better decisions, improve customer relationships, and enhance operational efficiency. Clean data allows businesses to trust their information, enabling them to act confidently and strategically. It also helps businesses comply with regulatory requirements, as accurate data is essential for meeting legal standards. In essence, proactive data management sets the foundation for a successful data strategy, enabling businesses to leverage their data effectively to drive growth and innovation. Proactive vs reactive data: what’s the difference? Proactive data quality focuses on preventing errors before they occur, while reactive data quality deals with correcting errors after they have impacted operations. By proactively managing your data quality, you can avoid costly mistakes and ensure that your data is always accurate and reliable. Let’s use two businesses in the retail sector as an example: “Business A” leverages email validation and phone number validation software as customers enter this information on contact forms, ensuring accuracy from the start via a proactive approach. “Business B,” in contrast, engages in reactive data management and collects information without validation, leading to issues like incorrect email addresses and customer insights. This approach requires future corrections, such as cleaning up the database via a batch cleanse and re-sending emails, and increases the likelihood of bounce rates and failed marketing outreach. Because “Business A” leverages proactive management, they’re able to save time and resources by ensuring data accuracy upfront—a feature that will lead to more effective marketing campaign, better customer relationships, and much more. Why is proactive data important for your business? Relying solely on reactive data quality measures can be risky. By proactively managing your data quality, you can improve decision-making, enhance customer relationships, reduce costs, ensure regulatory compliance, and increase operational efficiency. 5 benefits of proactive data quality Proactive data quality measures are essential for ensuring that your data is accurate, reliable, and up to date from the start. Let’s take a look at 5 key benefits your business can enjoy by adopting this approach: 1. Enhanced decision-making Inaccurate or incomplete data can lead to costly errors and misguided decisions. Proactive data quality measures ensure that organizations have access to reliable information when making critical choices. By maintaining high data quality standards, businesses can minimize risks, identify opportunities, and make informed decisions that drive success. Whether you leverage enrichment services to expand on your existing database or make use of data validation software, be sure to prioritize complete and accurate data that will empower you to make better decisions for your business. 2. Improved customer relationships Customers expect personalized and seamless experiences from the brands they engage with. Proactive data quality ensures that customer data is accurate and up to date, enabling businesses to deliver targeted marketing campaigns, personalized recommendations, and tailored services. By understanding their customers better, organizations can build stronger relationships, foster loyalty, and drive long-term growth. 3. Long-term operational cost reduction Proactive data quality measures can help reduce costs associated with data errors and inefficiencies. Proactively addressing data quality issues streamlines operations by reducing errors, eliminating redundancies, and automating workflows. This not only saves time and resources but also enhances productivity and agility, enabling businesses to adapt swiftly to changing market demands. A great first step in proactive data management that will achieve this feature is via data cleansing. By removing duplicates and ensuring data accuracy, you can avoid ineffective outreach campaigns and optimize resources, saving your business time and money. 4. Regulatory compliance With the increasing focus on data privacy and security regulations, businesses face mounting pressure to protect sensitive information and ensure compliance. Proactive data quality measures help organizations maintain compliance by ensuring data accuracy, integrity, and confidentiality. By implementing robust data governance frameworks and monitoring mechanisms, businesses can mitigate compliance risks and avoid potentially costly penalties. 5. Adaptability and competitive advantage Proactive data quality enables organizations to unlock key business insights, uncover market trends, and identify emerging opportunities. By leveraging high-quality data as a strategic asset, businesses can innovate faster, enter new markets confidently, and stay ahead of the competition. Not only does this prove your business can adapt quickly, but it also serves as a strong signal of your ability to remain ahead as business strategies evolve, too. Connect today to ensure optimal data efficiency Streamlined, proactive data processes improve operational efficiency and productivity, helping your business to make the right decisions necessary for success. Ready to learn more? Our team is here to help. Fill out the form to get started:
