All posts by Ashly.Arndt@experian.com

Agentic AI increases autonomy—and risk—making data quality a strategic foundation. Learn three principles for building trusted, scalable data quality in an agentic AI future.

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:

At its core, the financial industry is a data business. We find that 86% of financial institutions see data as an integral part of forming business strategies. Banks and other financial service firms have always relied on data to make informed decisions. From lending money and approving credit, to managing investments, business leaders rely heavily on information gathered from data. Still, as the volume of available data grows exponentially every year, many financial institutions are struggling to best understand who their customers are and how to meet their needs effectively. The answer: Data enrichment. Data enrichment enables financial institutions to cater better to the needs of customers and make online banking more convenient while increasing efficiency through a tailor-fit user experience. Data enrichment is based on real-world data obtained from customers that explore more than what is stated on an application. Let's take a look at what data enrichment is, its relevance to financial institutions, and the ways that data enrichment can help those in the financial sector make smarter business decisions. What is data enrichment and how can it help financial institutions? Data enrichment are the attributes appended into a database—so you can get to know who your customers are and what they care about. Enriching the data can ensure that financial institutions don’t overlook valuable customer attributes, such as age range, marital status, financial data, buyer propensity, and automotive data. Not only could these changes be critical in helping institutions identify whether a customer is a risk or a good fit as a potential client, but it enables institutions to personalize their messaging and offerings. Data enrichment can help banks increase their revenue as it improves customer service and enables them to make products relevant to customers based on their specific needs and desires. Utilizing data enrichment tools will allow financial institutions to take raw customer data, data that tends to be incomplete and unreliable, and turn it into an accurate and high-quality resource for better decision-making for marketing campaigns to loan approvals. Data enrichment gives the financial industry a competitive advantage Enriching your data is getting to know who your customers are and what they care about through appending insight to your existing records. With this insight, you can better understand your customers’ needs and help them achieve whatever financial goals they came to your bank or financial institution for. Data enrichment gives banks and others in the financial sector a competitive advantage in this age where organizations are pushing to be more data-driven. Financial institutions with enriched data have an edge over those who don’t because it enables them to: Know their customers deeper than competitors by identifying trends in customer spending habits, product usage, and lifestyle preferences. Further understanding of what leads people toward certain decisions (like opening new accounts) so that banks can focus on initiatives that drive results for both parties involved in the transaction Find opportunities to partner with tech companies and other financial institutions (banks, credit unions) for innovations that could lead to collaborative partnerships or joint ventures where each party can leverage each other's strengths while helping themselves grow at a larger scale and increase their revenue. How to get started In the end, data enrichment is a powerful tool that can help companies of all sizes compete in this new data-driven world. Whether you're a small startup or a large bank, it is important for everyone in the financial industry to make sure their data is working as hard as possible for them. Data enrichment is a great way to do just that: By taking raw information and turning it into meaningful insights that can help your business grow. As financial institutions continue their efforts to make sense of their data and develop more sophisticated ways of analyzing it, we expect that enrichment will begin playing an even bigger role in this process. If you haven't started enriching your data, connect with our team and learn how Experian can help you get started. Speak to a data quality expert today

With cyber threats rising and consumer privacy at the forefront, organizations must prioritize protecting sensitive information while navigating the complexities of data privacy and compliance. Data compliance is a significant aspect of keeping your customers' data safe, as it fosters trust and transparency in the current digital landscape. What is data compliance? Data compliance makes sure that an organization adheres to all legal, regulatory, and operational standards regarding data collection, storage, processing, and management.Overall, data compliance involves: Adherence to Laws and Regulations: When handling sensitive data, organizations must follow relevant data compliance standards, such as the GDPR in the European Union or the CCPA in the United States. Responsible Data Handling: Data compliance verifies that data is collected, stored, and processed safely, preventing unauthorized access or misuse. Mitigating Risks: Businesses reduce the risk of data breaches that can lead to financial penalties and reputational damage. An organization needs strong data governance for effective compliance and data protection. Data governance is the framework for checking that data meets business standards as it is entered into systems by focusing on people, processes, and technology. Data compliance is also integral to cybersecurity and protecting personal information through data security practices, such as conducting regular audits and assessments to find potential risks. Why data compliance matters Data compliance is a strategic necessity that can impact a business's reputation. Not following data protection laws can result in severe legal and financial repercussions. Regulatory bodies can penalize and fine organizations mishandling data, potentially compromising financial stability. Non-compliance can inflict lasting reputational damage, as data breaches or lapses in data handling erode consumer trust and diminish an organization's credibility over time. Embracing robust data compliance measures yields significant benefits for organizations. Implementing stringent security protocols can dramatically reduce the risk of data breaches and unauthorized access. This approach secures sensitive information and elevates operational efficiency by standardizing data management processes. Prioritizing data compliance also builds customer trust, as individuals are likely to remain loyal to organizations committed to safeguarding their personal information. Key data compliance regulations and standards Data compliance regulations and standards protect sensitive information and ensure robust data security. One example of these data compliance regulations is the Data Protection Act from the United Kingdom. Other examples of data compliance regulations include: GDPR (General Data Protection Regulation): Sets a global benchmark for data privacy, enforcing strict guidelines on data collection, processing, and storage. Enacted by the European Union, it reinforces individual privacy rights regardless of an organization’s geographic location. CCPA (California Consumer Privacy Act): A U.S. regulation that enhances consumer control over personal data by making businesses transparent about their data practices. It guarantees consumers understand how their information is used, shared, and stored while promoting higher accountability in data management. HIPAA (Health Insurance Portability and Accountability Act): A data compliance regulation protecting patient information by establishing strict protocols for handling medical records and personal health data. This set of data compliance regulations enforces administrative, technical, and physical protections so that patient information is confidential and secure. PCIDSS (Payment Card Industry Data Security Standard): A critical framework for securing financial data in payment transactions. It enforces network security, encryption, and regular monitoring to prevent breaches and fraud for secure payment processes. ISO 27001 (International Standard for Information Security): An international standard supporting a structured framework for managing information security. It helps organizations establish, implement, and improve an Information Security Management System (ISMS) while promoting ongoing risk management and resilience against evolving threats. These data compliance laws and standards address everything from consumer privacy and healthcare data protection to the secure processing of financial transactions. Common challenges in data compliance Navigating data compliance presents several challenges for organizations, such as: Managing secure and compliant data storage and access: To protect their storage and access, organizations must implement solutions that protect sensitive information and ensure that only authorized personnel can access it. Maintaining data accuracy and integrity: Errors or inconsistencies in data collection, storage, and access can compromise reliability, decreasing data integrity and compliance. Managing consent and user rights: Organizations need robust systems to promptly address requests for data access, correction, or deletion. Organizations must balance and uphold data security and regulatory adherence when developing processes aligning with data compliance standards. Best practices for achieving data compliance Achieving enterprise data compliance requires a multi-faceted strategy centering on establishing robust data governance frameworks, maintaining high data quality, and implementing continuous audits and monitoring. Data governance frameworks: Effective data governance begins with defining clear policies and assigning specific responsibilities to employees so everyone understands their role in safeguarding data. This structured approach standardizes data management practices across the organization and aligns operations with regulatory compliance data management. Data quality: Verifying that data is accurate, consistent, and complete minimizes errors that could lead to breaches or misinterpretations. Regular audits and monitoring: Regular audits and risk assessments reinforce this framework by providing ongoing oversight, identifying potential vulnerabilities, and enabling proactive adjustments to data compliance strategies. Together, these best practices form a comprehensive approach that protects sensitive information and demonstrates a firm commitment to data integrity and security. Future trends in data compliance regulations The future of data compliance is poised for significant evolution as emerging laws and evolving regulations reshape global and regional standards. Governments worldwide increasingly focus on tightening data privacy and security mandates, responding to technological advancements, and heightened public expectations for transparency and accountability. This trend could result in more comprehensive and harmonized legal frameworks addressing the complexities of cross-border data flows and digital innovation. Another trend in data compliance regulations is integrating artificial intelligence and automation, which is transforming how organizations manage compliance. AI-driven compliance tools streamline processes with real-time monitoring, risk assessments, and automated reporting, enhancing the accuracy and efficiency of compliance efforts. As these technologies become more sophisticated, they let businesses adapt swiftly to regulatory changes and mitigate potential risks proactively. How Experian helps with compliance Robust data compliance is a regulatory necessity and a strategic asset that can protect your organization from legal risks while further developing customer trust. We cutting-edge solutions, empowering your organization to maintain high-quality, compliant data. Our advanced compliance automation and reporting capabilities allow you to streamline your regulatory compliance data efforts, reduce operational risks, and focus on strategic growth. Contact us today to learn how Experian Data Quality can help you with your data protection compliance strategy and protect your business for the future. Connect with an expert to get started

Major consumer credit bureaus require data furnishers to submit consumer credit information using the Metro 2® reporting format, the industry standard for credit reporting. While the Metro 2® format is designed to promote accurate and consistent reporting, maintaining high-quality data and meeting evolving compliance requirements can still be challenging. Many organizations rely on manual processes to review, validate, and reconcile Metro 2® reporting files. These workflows can be time-consuming, resource-intensive, and increase the risk of errors. At the same time, consumers are more informed than ever about their credit reports and are more likely to identify and dispute inaccurate information, making data quality a business and compliance priority. Poor data quality can lead to reporting inaccuracies, increased operational costs, slower issue resolution, and greater regulatory risk. It can also reduce confidence in analytics and make it more difficult to deliver a positive customer experience. If you're looking to simplify your Metro 2® reporting process while maintaining FCRA compliance, focus on three key areas: Accuracy Automation Resourcefulness Data accuracy When data furnishers, like you, prepare their Metro 2® reporting files, it’s important that they be as accurate as possible. Incorrect data means inefficiencies across your processes which will negatively impact your resources and ability to quickly respond to customer dissatisfaction, disputes, media backlash, and regulators. In the credit reporting lifecycle, as files are sent to the Credit Reporting Agencies (CRAs), the furnishers are receiving information in return to reconcile discrepancies and rejects, among other data points.  All this information can be overwhelming to manually aggregate, address, and appropriately update. More importantly, manually managing your data limits your ability to build risk controls. This is where automation becomes your best ally. Data automation Experian’s DataArc 360™ powered by Experian Aperture Data Studio is a data quality management tool that automates the data quality process to help data furnishers comply with credit reporting industry standards and the Fair Credit Reporting Act (FCRA). This is a tool that helps your organization build risk controls. DataArc 360™ is a powerful solution that removes manual processes and enables you to proactively manage your reporting analysis. It will flag any discrepancies against a pre-built set of rules and measure the results through an interactive dashboard where a user can even drill down to account level details for root cause analysis. In addition, furnishers can also run the analysis as often as desired to monitor trending statistics, as well as adapt or create new rules to enhance the process further. With this ability, DataArc 360™ makes it easy to monitor consumer credit reporting and gives you confidence that a solid process is in place to enhance data accuracy.  The tool brings discovery in-house to address data quality concerns proactively while helping to reconcile any discrepancies post-submission from the CRAs. Resourcefulness DataArc 360™ can help you stay ahead of the game when it comes to your data integrity. The tool helps you make the most of your resources, allowing your staff to focus on more strategic efforts like understanding risk appetite and tolerance. Homepoint, a DataArc 360 client, saved their team 50 hours per month by automating and operationalizing their data with our tool, boosting productivity, and increasing data accessibility. Jill Cannon, Senior Director of Default Administration at Homepoint, says, “DataArc 360 is accessible to everyone and anyone within the business who wants to see credit reporting and understand exactly what the current status is. It allows management to sign in and view dashboards to immediately see the percentages of loans that are passing, the rules that are failing and at what rate and, then report on this to our leadership in a way that just wasn’t possible before.” When you streamline and automate your Metro 2® analysis with Experian, you will see: Accuracy in your credit reporting. Advanced analytics tools that can assist during regulatory agency visits. Alignment between your credit reporting and larger data strategies like data quality or data governance. With a platform that hosts results, you can easily share information and collaborate across your organization with risk, fraud, anti-money laundering, sales, marketing, and compliance. More accurate reporting also leads to a better customer experience, and ultimately, to greater customer loyalty. Are you interested in easing your Metro 2® reporting quality analysis process? DataArc 360™ can help. Connect with a data quality expert today:

In today’s world, businesses can fall behind the competition by neglecting just one practice. Data mining offers strong capabilities for several industries and has become crucial for success in each one. The benefits of data mining for organizations are immeasurable and automatically give businesses an advantage in competitive industries. What is data mining? Data mining is the process of analyzing large sets of gathered data to recognize patterns, predict outcomes, and develop solutions to problems. Generally, businesses keep a large database or tool to maintain data that is collected by customers or highlights information from around an industry. Data mining is a comprehensive process and involves sifting through large amounts of information to determine what is the most valuable and what can be used to make informed decisions. Data mining is also referred to as knowledge discovery in data (KDD). Why data mining is important Today, data mining is used across almost every industry. Because we continue to grow as a data-driven society, the importance of data mining is higher than ever. Data mining provides vital information about customer habits and industry trends, allowing businesses to respond and take action as necessary. Specifically, data mining allows businesses to: Compile reliable information Recognize fraud Identify risk factors Respond to industry adjustments Make informed decisions Improve customer relationships What are the benefits of data mining? Data mining allows businesses and organizations to understand their audience, identify patterns, and adjust to trends. Businesses and organizations that use data mining are often more responsive and more resilient than those that do not. The following are some of the biggest benefits of data mining for businesses and organizations. 1. Better decision-making Gathering data allows businesses to make more informed decisions on a regular basis. Data mining is crucial for recognizing patterns in customer habits or business needs. For example, keeping consistent data can provide information on when inventory will need to be replaced or understand when more staffing is needed to address consistent periods of higher customer volume. Staying ahead of these decisions ensures that business operations are able to flow without interruption and that money is being spent on the correct needs. Keeping data also tells businesses when marketing campaigns, sales, or products are ineffective. 2. Improved marketing targets Marketing is always more effective when it is personalized and geared toward an individual and their needs. With data mining, businesses are better able to understand their demographic, develop personalized ads, and generate offers and incentives that will interest them most. By using more targeted marketing campaigns, businesses can reach out and connect with those they already know are most likely to buy their product. In strategically marketing toward specific audiences, businesses generate more conversions and see a higher return on investment by not wasting campaigns on those who are uninterested. 3. A competitive edge In a world where data is crucial, businesses that can use data to respond to customer needs are sure to stand out. Customers gravitate toward businesses that provide them with a personalized experience. By providing customers with personalized ads and discounts for the products they actually want and need, businesses are able to build brand loyalty and keep customers coming back. Data mining also helps businesses recognize industry trends and how to respond to them. If a business does not respond to customer demand because they did not have the data to see it coming and prepare for it, customers will start to go elsewhere. With all of the ways that data can benefit a business, one that does not use data mining will fall behind. Benefits of data mining by industry Every industry is different. Although data mining has several benefits for businesses and organizations in general, it also offers specific benefits related to various industries. From banking to healthcare, businesses from all over the utilize data mining in their day-to-day operations. Each industry uses data mining a little bit differently in order to benefit their businesses. Here are some examples of how data mining is used by industry. Banking Data mining can play a special role in banks helping to prevent fraud and meet customer needs. Data mining allows banks to gather consistent data about customer spending and recognize patterns. As a result, it becomes easier to flag instances where this pattern is broken. For example, if a customer's card is used for a purchase in a location that does not fit their patterns, banks can highlight the activity as fraud and verify with the customer before shutting down the card. Benefits of data mining in banking can also include the ability to use gathered data to give customers personalized loan offers and deals based on their habits. Healthcare In healthcare, data mining plays a huge role in developing a patient profile, compiling information from their medical history, past treatment options, medications, and more. This profile gives doctors the ability to develop more accurate diagnoses and treatment options based on past results. Benefits of data mining in healthcare include helping to recognize generalized patterns in medical claims, prescriptions, and referrals. Seeing patterns like this in a particular area or hospital can help medical professionals adjust staffing, prepare for common issues, or even recognize fraudulent claims. Marketing The benefits of data mining in marketing are huge. Effective marketing campaigns understand their target audience, including their needs and spending habits. Data mining can provide valuable information on age, gender, interests, location, and income, which all influence a person’s interest in a product or service. Understanding the target audience saves time and money by allowing businesses to narrow their scope and market the right products to people who have the highest potential of purchasing them. By targeting their marketing only towards those who are likely to purchase a service or product and adjusting techniques based on a specific audience, businesses see a higher return on investment in their campaigns. Insurance Insurance companies are able to offer personalized, reasonable, location-specific rates to customers thanks to data mining. By collecting a range of data about customers and incidents, companies can assess risk and generate appropriate rates. Understanding their customers also allows insurance companies to provide them with offers for products that may fit their needs, like more advanced plans or special promotions. At the same time, data mining in insurance is largely important for studying past incidents and patterns to determine how to respond to incidents in the future. Capitalize on the benefits of data mining Altogether, data mining has become a critical function for businesses in order to reach customers, make informed decisions, and gain a competitive edge over other businesses in the same field. To learn more about the benefits of data mining for organizations, contact Experian Data Quality. We offer a wide range of tools for mining and analyzing data, and we are happy to be your partner in accessing helpful data in your daily operations. Contact us today to get started:

Data governance and compliance: it’s safe to say the two go hand in hand. Without proper data governance, how can you be confident your organization is adhering to regulations? On the other hand, when organizations are compliant, you can bet there is an effective data governance strategy in place. If you’re asking yourself, “how can I get started,” we are here to help! First let’s take a look at the terms data governance and compliance, and see how they are related. Ensuring your data is fit for purpose starts by building a data governance program. By aligning your organization’s people, processes, and technology around a central data strategy, you can begin to leverage your data to benefit larger business goals. In terms of compliance, this means having clear control processes over your data to align with pre-set business rules. This is especially important in highly regulated industries such as finance and insurance. Data governance means making sure you have processes in place to control your data and assure that all regulations are met in all your organization’s data practices. Effective compliance can only come with a holistic and complete approach to your data governance strategy. How can you expect to be 100% certain you are adhering to regulations without complete control over your data and how it is collected and stored. The three critical aspects of building an effective data governance strategy are the people, processes, and technology. With an effective strategy, not only can you ensure that your organization remains compliant, but you can also add value to your overall business strategy. People Data is at the heart of every organization. You need the right people to manage your data so it can effectively support your organization’s goals. A data team is responsible for owning and defining data assets to meet the needs of your organization. Ideally, the data team comprises individuals from across your organization to closely align your data governance strategy with the most impactful business needs. This is especially important if you are an organization that needs to be compliant with government or financial regulations. Your data governance team should consistently monitor your data to ensure it stays in line with regulations. A data team can also empower your organization to make better data-driven business decisions. By aligning your data governance strategy around business users (and not strictly IT), it opens the connection between data and the overall strategy of the organization. Processes There needs to be clear definitions in place as to how data is to be stored, leveraged, and interacted with within an organization. Why? Because without this level of control, you will not be able to understand your data or monitor and resolve issues effectively. Without defined data processes, when it comes to compliance, your organization could be in the dark. This is why an effective data governance strategy is so important, especially in regard to regulation. By implementing powerful control, auditing, and monitoring processes within your organization, you can be certain that you are adhering to regulations. The greater business value that is derived from this is the saved time and money that comes with less manual rework and eliminated fines due to noncompliance. Clear data processes are essential when it comes to leveraging an effective data governance program within your organization. Technology Along with identifying the correct people and processes for your organization’s data governance program, it is necessary to select the right technology to help execute and maintain this strategy. The right software that will help your organization to enforce business rules through monitoring, reporting, and overall data quality. A data governance tool allows the data team to collaborate and gain insight, while applying processes across the business. It can help your organization define the data itself, consistently apply those definitions across systems and databases, as well as establish who owns the data and whether it can be trusted. Once you have developed these three areas of data governance, your organization can be proactive in managing its efforts to remain compliant. The connection between data governance and compliance is clear. Your organization needs the ability to effectively manage data throughout its lifecycle, in order to maintain compliance, and this can be achieved through an effective data governance strategy. Once your organization has put in place all of the right people, processes, and technology, you can focus on overall business strategies. This means that with data governance, you can ease compliance efforts and free up valuable time, money, and other resources to be leveraged in more meaningful or impactful ways for your organization. Regulatory requirements are constantly evolving. Particularly for financial institutions, you need to be proactive in your data management efforts to keep up. Connect with a rep to learn more:

Automation is a hot topic across industries today, and incorporating automation into your business’s best practices can be tempting. But before you dive head-first into the world of automation, generative AI, and the like, there are significant potential risks to be aware of. In this blog, we will explore 3 important risks to consider before implementing automation into your business. 1. Data protection This one might seem like an obvious risk, but there's more to it than you think. Automation systems often handle large volumes of sensitive information, private personal information (PPI), protected personal information (PII), consumer data, financial records, etc. If this data is not properly managed and controlled, these automated systems can become highly coveted targets for cyber-attacks or data breaches. When using automation to improve your processes, it's important to ensure that your systems are equipped with strong security measures to protect your data from unauthorized access. Some examples of robust data protection include: Implementing encryption Regular security updates Robust access controls And more Be sure to have a crystal-clear data protection policy in practice and train your employees on best practices for handling highly sensitive data. There can be severe consequences at stake if this is not properly executed, including legal penalties, loss of data, reputational damage, and more. It's imperative to take the proper precautions to safeguard your data before adding automation to your repertoire. 2. Ethical and legal implications Automation in the business world often raises ethical and legal concerns. As generative AI and other forms of automation are evolving to take on more decision-making roles, it's essential to consider how these decisions align with your company's legal requirements and ethical standards. From a legal perspective, you must ensure that your automation practices comply with the relevant regulations. This includes data protection laws like GDPR or CCPA, which mandate specific requirements for handling personal data. Failing to comply with these regulations can result in significant fines and legal repercussions. Ethically, your business should be transparent about how adding automation into the mix may impact its stakeholders. This includes informing employees about potential job expectations and ensuring that automated decisions are fair and just. Balancing efficiency with ethical considerations is the key to maintaining trust and upholding your business's integrity. 3. Operational changes Integrating automation into your business can also present several operational challenges. First, there's the risk of system integration issues. Automation often involves connecting various systems and software, which can sometimes lead to incompatibility, or problems with technical glitches. These issues can disrupt your operations and potentially affect your company's productivity. With the added challenge of increased complexity in your internal processes, there is more room for error. While automation attempts to simplify tasks, the technology itself can pose different, more complex technical challenges. You'll need to be prepared to spend a significant amount of time and allocate the proper resources to set up and maintain your automation software. Ensure your employees receive proper training and documentation to help them operate as efficiently as possible with your new processes. Moreover, the over-reliance on automation technologies can sometimes result in a loss of human oversight. It's essential to strike a balance between automated processes and human intervention to ensure that systems function correctly and adapt to any unforeseen challenges. Mitigating risks of automation with data governance While the risks of automation are real, they are not completely insurmountable. By proactively addressing data protection, ethical and legal concerns, and operation challenges, your business can navigate the complexities of automation more effectively. One way to mitigate these risks is by investing in robust data quality and data governance products. These tools can help ensure the accuracy and security of the data processed by your automation systems, providing an added layer of protection and reliability. Have questions? Our team is here to help!

A lot goes into confirming that an address is valid. For the United States Postal Service® (USPS), there are several pieces of information needed to verify an address. When you use address validation software, it checks the information entered against USPS’s database and requirements. A couple of terms to know when it comes to address validation are CASS™ and Delivery Point Validation® (DPV).  The Coding Accuracy Support System, or CASS, is a certification process used by USPS to evaluate the accuracy of address correction and matching software Delivery point validation, or DPV, which is one of the USPS-sourced files included in CASS data, is used to check the status of a specific address within the United States. What is CASS certification? According to the USPS, "The CASS™ (Coding Accuracy Support System) software certification program provides hardware and software developers, service bureaus, and commercial mailers a common measure by which to test the quality of address-matching software." Software that meets these standards can earn CASS certification, helping mailers qualify for USPS mailing discounts by improving address accuracy and reducing the amount of manual processing required by the Postal Service. Using CASS-certified software to validate addresses can help organizations qualify for discounted USPS mailing rates. As a CASS-certified vendor, Experian's data quality solutions cleanse, verify, standardize, and validate addresses before matching them against USPS CASS requirements. This helps improve address quality, reduce undeliverable mail, and enable customers to take advantage of available USPS mailing discounts. CASS processing Each software provider has its own unique processes, but some are standard for all CASS processing. First, address standardization is necessary to organize the list of addresses and bring them in compliance with the standards of USPS. The address is put in the correct format with the right abbreviations. The system will correct spelling errors and other mistakes with CASS address correction software. The next step is to update any addresses that may have changed. This step is separate from comparing addresses to the NCOA for businesses, which is the national database for forwarding addresses when someone moves. Updating changed addresses with Locatable Address Conversion SystemLINK (LACS) means that any changes to that address format will be recorded, regardless of who lives at that location. Address validation is the third step in all CASS processing. This stage determines if the address is real. It not only checks that the address is a real location, but it validates that it is in use. What are the requirements to be CASS certified? CASS data is comprised of the following USPS sourced products and files: 5-digit ZIP coding Zone Improvement Plan (ZIP) code (length: 5 digits) - Postal code system used by USPS. (Length: 5 digits) ZIP + 4 Extension of a ZIP code which determines a more precise location within the ZIP. (Length: 4 bytes) eLOT Enhanced line of travel allows mailers to sort their mailings in approximate carrier-casing sequence. Extended LOT allows mailers to sort their mailings in approximate carrier-casing sequence. Once data has been processed through a CASS-certified product, a CASS Summary Report can be generated. This report contains information about the condition of the original addresses and how the addresses have changed since cleaning. It is required where USPS mailing discounts are sought. Providers that are CASS-certified must support specific tools: Delivery point coding (DPC): Delivery Point Code (length: 2 bytes). This field contains the last two digits of the house/box number, or if the match is made to a high-rise record, the secondary unit number representing the delivery point information to form the 11-digit or delivery point barcode (DPBC). Possible values: “00” through “99” or spaces. (Length: 2 bytes). Suite: The Suite dataset returns information about changes made to an address following comparison with the USPS Suite data. A 'Y' indicates a Suite match, where secondary information is automatically appended to the output address. An ‘N’ indicates no match. A blank indicates that no lookup was performed Carrier route coding: Code assigned by the USPS to a group of addresses to aid mail delivery within a ZIP code. Consists of a carrier route type and carrier route code (Length: 4 bytes). For example, “C001”. LACS: Locatable Address Conversion System is a dataset provided to allow addresses that have been converted due to USPS changes or for 911 emergency systems to be linked with their new address.  What is USPS Delivery Point Validation™ (DPV?) DPV is another dataset used for address verification. The actual process for DPV uses the ZIP+4 to validate whether the address has verifiable primary and secondary mailing information. This is different from standard address validation because its purpose is to tell the user whether the address exists—not just that it’s in the postal database. Experian offers a DPV Confirmation Indicator in our address validation services which allows customers to verify that mail has been delivered to the address entered within the last two months. This is important as packages shipped to bad addresses can lead to several losses for the business: Returned mail and postage costs Time spent contacting the customer to alert them of delays Downturns in customer satisfaction For marketing campaigns, there is not only a loss of postage but also decreased customer retention due to poor communication and brand perception. By validating an address against DPV data, you can avoid the costs associated with shipping to inaccurate addresses. How do businesses benefit from CASS-certified mail software? Using CASS-certified software, including DPV Confirmation, offers your business considerable cost savings for mailing and shipping. When you use a CASS-certified address verification software, you not only qualify for mailing discounts through the USPS, but you also significantly reduce costs for returned mail or poor customer experiences by confirming the accuracy of the addresses to which you send. CASS processes provide numerous benefits to businesses for address verification: They ensure more accurate addresses for faster delivery CASS processes help keep the database organized and cleaned up You avoid returned mail, which costs money and prevents you from reaching your intended recipients These processes allow for work sharing or sorting bulk mail yourself to get a discount on the cost of postage What makes Experian CASS-certified? CASS certification requirements change as needed, and vendors like Experian must stay up to date with the latest rules. They must use software that meets minimum requirements for accuracy. This may include the following: Validation for five-digit ZIP Code Matches ZIP+4 to the five-digit codes Coding for carrier routes, which group addresses with the same ZIP code to improve efficiency LACS matches rural addresses to city addresses for 911 conversions RDI to verify if an address is a residence or commercial space DPV confirms that the address truly exists, and mail can be delivered eLOT, which puts mail in descending or ascending order to follow the order the mail will be delivered DPC, which is a unique identifier for each address How do I know if a vendor is CASS certified? Experian address verification tools are all CASS certified based on the latest requirements. You will also find tools for international address validation, so you can ship to your global customers. Connect with our data quality experts to learn more

Whether you need to enter your address online to make a purchase or provide your zip code when verifying your identity on the phone, you’re more than likely to sound off a combination of five different digits. But, if you pay attention to the address labels affixed to the miscellaneous items you receive in your mailbox on any given day, you’ll notice that your address has now transformed into a nine-digit number with an added 4-digit extension. This is referred to as your ZIP+4 code. What impact do ZIP+4 codes have on your business? In this article, we’ll cover why these additional zip code digits exist, why they matter, and how an address validation service can help you get started. What are ZIP+4 Codes and why do we have them? Let’s breakdown a 9 digit zip code and what these different numeric values mean for an address: The first 5 digits: The beginning of a zip code, or the 5 digits we are traditionally accustomed to using, refer to the specific delivery area of the country of a given address. The final 4 digits: ZIP+4 codes include an addition 4 digits that identify and tell the postal carrier the geographical segment present within a zip code delivery area. It’s important to note that the ZIP+4 codes are only appended to the original 5-digit USPS® Zip Code after the address has been validated and standardized. Why do these added 4 digits matter? These additional appended numbers help to ensure the quickest and most accurate mailing process possible. While only the first 5 digits of a zip code are required to send mail, you’ll find that your customers can enjoy expedited delivery time stemming from the specific geographical information translated by a ZIP+4 code. ZIP+4 Codes and How Experian improves address verification ZIP + 4® verification software verifies US addresses down to their USPS® 9 digit ZIP code™. When performing address verification with a ZIP + 4®, the user can pinpoint an address down to a few block radius. Although many people do not know their ZIP + 4®, Experian's address verification software adds the ZIP + 4® to every recognized address. This helps to ensure delivery, hence saving time and money. Zip + 4® verification software works both on the front-end and the back-end. On the front-end, the ZIP + 4® will be added upon entering the address. Meanwhile, with back-end address correction software, the ZIP + 4® will either be added or corrected for all existing addresses. What ZIP+4 means for your business If you’re accustomed to using solely a 5-digit zip code, you’ll miss out on the numerous benefits enjoyed by businesses who leverage a ZIP+4 system, including: 1. Quicker deliveries As noted above, the additional specified information offered by a ZIP+4 code means faster delivery for your customers. The extra information supplied traditionally reduced processing time at mailing centers, which means that a package arrives to the consumer quicker. 2. Deliveracy accuracy Whenever postal carriers have access to more information, the likelihood of a package or piece of mail landing in the proper mailbox significantly increases. The correct ZIP+4 decreases the number of times a piece of mail is handled, too. This means that misdelivery or error is significantly reduced as a result. 3. Access to updated information Since ZIP+4 codes are based on delivery routes, this information can change as frequently as once every two months. Fortunately, this is where an automated address validation software will come in handy and allow you to stay on top of updated and accurate user data. 4. Reduced costs Anytime mail gets sent to the wrong address, your business may take on the costs associated with: Initial shipment costs Product replacement Reshipment Implementing a ZIP+4 code system into your business’s system of labeling and processing packages will serve as a preventative measure that safeguards your business against lost revenue associated with incorrectly delivered mail. 5. Increased customer satisfaction rates In today’s digital-forward society, online purchases have become a part of most individuals’ everyday routine. This means that now, more than ever, your clients expect a seamless shopping experience. The expectation for fast and accurate delivery is at an all-time high. With ZIP+4 codes, your customers will enjoy faster delivery with a reduced chance of undelivered mail. For your business, this means satisfied customers who are happy to do repeat business with your company. Improve address accuracy with Experian Experian helps businesses validate and standardize addresses, including appending the correct ZIP+4® code for recognized U.S. addresses. The result is cleaner customer data, fewer delivery issues, and a better customer experience. Ready to improve your address data? Learn how Experian can help your business verify addresses and improve deliverability. Fill out the form to learn more: