Data Management

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

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:

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

Learn why accurate employee data is essential for HR and how address verification helps keep employee records current and compliant.

Insurance companies face new challenges as they compete in the digital age. Consumers can research rates, contact employees and even sign up for service without meeting agents face-to-face or calling the company's phone number. This can be a blessing because it provides consumers with the convenience to sign up through their preferred channels when they find the best deals. However, in a competitive market, insurance companies must be able to reach new consumers and gain enough information to meet their needs or risk falling short. With a comprehensive data management strategy and effective data collection tools, insurance companies position themselves to better meet customer needs and demonstrate a strong commitment to developing customer trust. Understanding how your company can leverage consumer data can set you apart from the competition What data do insurance companies collect? Insurance companies collect a variety of data from various sources to better serve customers and respond to their needs. Like other industries, insurance companies collect contact information during customer interactions for the sake of being able to stay in touch with customers and provide them with valuable information and updates. However, insurance companies also have unique, industry-specific data collection methods. Data collection has become especially useful at the scene of an accident or incident. Many companies use mobile apps that allow consumers to report the details of an incident. These reports can collect information like contact details, time and date of the incident and location details. This data is valuable for recognizing trends, understanding individual incidents and properly addressing claims. The importance of data in insurance When insurance companies focus on their data collection tactics, they can be certain they aren't losing any prospects in the pipeline and are providing their customers with the best product options. Data collection for insurance companies provides the ability to gain a single customer view, a complete data profile of a customer and their interactions with your company. This profile, when developed with quality data, provides a useful overview that can inform future interactions and potential ways to reach the customer. However, companies that want to leverage these capabilities must be certain to use data collection tools like address management to perform email verification at the point of entry. Improved data collection processes and better analysis tools allow companies to enhance the quality and utility of their data to better serve customers. With reliable data, companies can focus on using it to find new potential clients, promote new products and even improve the customer service experience for existing customers, demonstrating a commitment to valuing customers on a personal level. Establishing a more structured method for data collection and handling can also improve information security and help insurance companies avoid circumstances such as those that recently affected the Investment Industry Regulation Organization of Canada (IIROC), where the organization announced that an employee lost a personal device that stored 52,000 customers' personal information. Because insurance companies deal with such valuable and personal customer information, a secure data management system with security tools is necessary. Maintain high-quality data While insurance providers once relied on staff members to type in customer data, companies are now saving time and money by supporting self-service, the source adds. Unfortunately, this can also lead to a higher error rate if the proper tools are not installed to flag inaccurate or incomplete fields before they are saved and processed. Issues like typos, human error and improper formatting are prevalent during the data-entry process. Insurance data collection tools are highly valuable for maintaining high-quality data, flagging data quality issues and preventing incorrect or incomplete data from entering the database. Incorporating the correct measures at the point of entry is one way to eliminate mistakes when larger volumes of data are being processed. Another strategy involves deploying a system that regularly cleanses information in its flow throughout departments. Data cleansing tools like email address verification are helpful at the point of entry, while tools like data monitoring regularly check the quality of stored data. These tools prevent incorrect information from entering databases and correct mistakes when it does. Verification methods are particularly important when firms are supporting online channels, which are very common sources of data entry errors. Improve your insurance data quality Effective data collection and data quality practices play a critical role in helping insurance companies meet customer expectations, improve operational efficiency and remain competitive in a digital marketplace. By implementing tools that validate, standardize and monitor customer data throughout its lifecycle, insurers can reduce errors, strengthen security and create more personalized customer experiences. Solutions such as address verification, email verification and ongoing data quality monitoring help ensure customer information remains accurate, complete and reliable. Investing in a strong data quality strategy enables insurers to make better decisions, streamline claims and underwriting processes, and build lasting customer trust. Fill out the form below to get started:

Learn how email authentication protects your business from phishing, safeguards sensitive data, and strengthens customer trust.

Explore the business impact of data quality and why accurate, reliable data is critical for informed decisions and competitive success.

Do you know where to find specific data in your business or who to contact for that data? Modern businesses know that making data-driven decisions is critical to improving their bottom line. A strong data governance strategy saves time and money by improving data quality and making it easy for teams to get the information they need. By following data governance best practices, you can reap the benefits of implementing the best data governance framework for your business. What is data governance? Data governance is the set of processes that make sure data meets business standards and rules as it’s entered into a system. It lets businesses control the management of data assets. Data governance involves three aspects that make sure data is usable, accessible, and fits its intended use: People Process Technology A data governance plan is important for different types of organizations and industries. It’s especially important for those that have regulatory compliance, such as finance and insurance. To meet compliance regulations, organizations must have formal data management processes in place to govern their data throughout its lifecycle. While data governance is a key focus for many organizations, not all strategies return the anticipated results. Best practices for data governance Following strong data governance practices means your business gets the most from a data governance strategy. We know what methods work best when building a data governance policy. When developing your data strategy, one of the best data governance tips is to first focus on people, then refine processes, and finally integrate technology. 1. Get business stakeholders on board To begin creating the best data governance strategies, you need executive buy-in, but getting a green light is just the start. You also want to inspire stakeholders to take action so your governance plan is adopted across your organization. The best way to get executives excited about your plan is to build a business case for your data governance practices. By building a business case, you show leadership the specific benefits they can expect from a data governance strategy. 2. Data stewardship Data governance practices are only successful when roles, responsibilities, and ownership structure are clearly defined. Defining the data governance roles of team members across your organization creates the framework on which a governance plan runs. The goal of data governance is to improve data quality and collaboration between teams. It needs input and data ownership from all parts of your organization. While each organization’s data governance structure will look slightly different, there are a few key players to use in your framework: Data governance council or board: This team oversees the entire governance strategy. They help guide the data governance plan through strategic suggestions, typically prioritize aspects of the strategy, and approve new policies. Tactical team members: Members of the tactical data governance team develop data governance strategies based on the council’s suggestions. They create the data processes and policies that are then approved by the data governance council.Owners: Data owners are the team members responsible for specific data. They are the person of contact when someone needs a certain type of data. For example, if you need last month’s sales information, you contact the sales data owner.Data users: Team members actively inputting and using data as part of their regular work responsibilities. Putting the right people in the right roles is one of the best practices for data management, especially when creating a data governance framework.  3. Data quality management Your business needs data quality management to create the best data governance strategies. It enables you to harness the full potential of your data assets for informed decision-making, competitive positioning, and customer satisfaction. By guaranteeing consistent, secure, and credible data, your organization can unlock numerous benefits contributing to your success in today's dynamic marketplace. Data governance is pivotal in unlocking better data quality by creating systems promoting actionable, accurate, and complete data. It empowers your business to adapt swiftly to evolving market trends and customer preferences. Your business can enhance the customer experience by delivering personalized communications, promotions, and customer service interactions. High-quality data is needed for informed decision-making, and having it means you can confidently navigate any challenges your business faces and achieve your objectives. 4. Use metrics to measure progress As with any change, it is critical to measure progress and display the success of your data governance program. Once you have executive sponsorship from your business case, you need data to support each step you take. Plan on defining your metrics before you start introducing data policies. This lets you set a starting point based on your current data management practices. Track your progress using the original metrics regularly. Not only does this show how much progress has been made, but it serves as a checkpoint to make sure your data governance best practices are effective in practice and not just in theory. A plan that’s flawless on paper may not transfer to real life as well as you hoped. It is important to keep a close eye on your governance strategy and be flexible regarding adjustments and improvements. 5. Data security and privacy When creating your data strategy, it is crucial to make sure your business’s data is safe and private. Data security practices protect data against unauthorized access, alteration, or destruction. Encryption techniques, access controls, and authentication mechanisms are commonly employed to mitigate risks associated with data breaches. Additionally, organizations often implement firewalls, intrusion detection systems, and data loss prevention solutions to bolster their defense mechanisms against external threats. Data privacy concerns the ethical and legal obligations surrounding the collection, use, and disclosure of personal data. With more data-driven technologies available and the increasing volume of personal information collected by organizations, customer concern about privacy continues to grow. Organizations must adhere to established privacy regulations such as the GDPR (General Data Protection Regulation) in Europe or the CCPA (California Consumer Privacy Act) in the United States. Complying with these regulations means implementing privacy policies, data anonymization techniques, and consent management procedures to protect customer’s privacy. The best data governance strategies establish clear guidelines for data usage and dissemination for accountability purposes and to foster responsible data stewardship. The best way to handle security and privacy in a data governance plan is through transparent communication regarding data practices, along with regular audits and assessments.  6. Encourage frequent communication Whether you’re just starting a data governance program or have been practicing one for years, communicating early and often is essential. Consistent and effective communication helps show the impact of the strategy—from highlighting successes to reorganizing after a setback. An executive team member, such as the Chief Information Officer (CIO) or Chief Data Officer (CDO), should become the communications leader of the data governance program. These leaders are the central contact for the current status of governance practices across the organization. Team leaders and data owners can give regular updates to the executive. The executive team member then brings the most important updates to the rest of the leadership team and the organization as a whole. 7. Data architecture and metadata management Another one of the best practices for data management when creating your data strategy is using the right tools, data architecture, and processes. Data architects are critical in developing appropriate models and architectures to integrate and manage data across disparate systems. Central to a data governance framework are well-defined processes governing data storage, access, security, and management.  Metadata management processes allow your organization to maintain an inventory of data assets and verify their relevance and accuracy. Well-defined data governance processes govern data storage, access, security, and management, ensuring compliance with regulatory requirements and maintaining data integrity throughout its lifecycle. All of these processes are tailored to your organization's objectives and structure, serving as guiding principles for data governance initiatives.  8. Data governance is a practice, not a project Creating a data governance program often feels like tackling a new project. You might be tempted to assemble a team to take on the project while the rest of the organization waits for it to be done. This is where many businesses see their data governance strategies slow down. A data governance strategy isn’t a one-time project. There is no set end date or conclusion. Instead, it’s an ongoing practice that’s introduced as a regular policy. Much like dress codes or policies for requesting time off, the best data governance strategies become part of everyday life at your organization. Ready to put data governance practices to work? Implementing the best data governance framework for your business helps it work more efficiently, but you’ll need to build a business case for stakeholders before getting started. Find out how to get the green light for your data governance strategy by connecting with our team today!

High-quality data provides an innumerable amount of benefits for businesses, but there is one hangup that often gets in the way. By addressing the challenges of duplicate or inaccurate records head-on and developing a strategy for success, businesses are able to create what is called a golden record, one of the biggest achievements in the world of data-driven businesses. In achieving a golden record, businesses can build stronger relationships with customers and enjoy all-around greater success. All it takes is a strategic approach and the proper commitment. What is a golden record? A golden record is a guiding light in the world of Master Data Management (MDM), an achievement that every business wants to accomplish for each entry in their database. A golden record is a single data point that provides all of the important information about a customer, client, or resource with total accuracy. For example, a golden record would provide correct address information, an email, a phone number, and more, all in a single data entry. In MDM solutions, a business is responsible for connecting data from multiple areas and systems and is tasked with combining that information into a single record. Businesses that collect data from multiple sources or store data using multiple databases usually have to put extra thought into creating a golden dataset. Businesses that achieve a golden record are better able to understand their customers and clients, build relationships, and take actions that generate more overall success. Why is a golden record important? Although it is always the goal to create a comprehensive and accurate profile for each individual customer, it is often easier said than done. There are many issues that can occur during data collection methods that harm your chances for a golden record. The golden record provides a single customer view that allows businesses to better understand each customer. By achieving a golden record for as many customers as possible, your business is more informed and able to use data insights to strategize on moves for success. In the process, you will also be able to maximize your ROI on marketing tactics. Despite the awareness of the benefits of a golden record, many businesses fail to achieve it and end up with several cases of the following scenario: Last NameFirst NameEmailPhoneAddressSmithRobertrsmith@email.com(555) 200-1234418 Maple St. Port Claire, RI 76068SmithRobrsmith@email.com(555) 200-1234418 Maple St. Port Claire, RI 76068 The information here is largely the same, except for the name and a typo in the ZIP code. One small difference can create an entirely separate data entry, treating the entries as two separate people. This difference causes ambiguity and confusion, preventing your business from having an accurate picture of the customer. Even worse, if you base your marketing or communications on the wrong entry, your business might not reach this customer at all and waste valuable time and resources in the failed attempt. Altogether, this confusion makes it difficult to reach customers and requires time and energy for your team to find and correct duplicate or inaccurate records. Unfortunately, this issue is all too common in the world of data-driven businesses. On the bright side, businesses are far from powerless in their ability to take steps to reduce duplicate entries and achieve a golden dataset. How to implement Master Data Management solutions Achieving a golden record in Master Data Management requires a comprehensive approach to data management with thought-out systems for collecting, verifying, and managing data. There are three key ways that you can begin implementing an effective MDM solution. Merge and match records Duplicate records are the biggest culprit for preventing businesses from achieving a golden record. When your database contains many instances of duplicate records, the best approach is to merge and match. MDM solutions are valuable in matching records based on the information that is provided. Given the above example, a system would identify that there is significant crossover between the two entries. Since the two entries have different information in the same field, the system will prioritize the field with the most reliable information and combine the records to create a single entry. In cases with multiple areas that are different, the system has to review the source for each data field. You can also specify whether the system should prioritize a single field. For example, if the first source is more accurate for the address and the second is more accurate for the first name, you can control which the system uses to complete the record. By combining and consolidating the data into one entry, the system creates a single golden record. Develop a cohesive data management strategy Of course, you can correct duplicate records after the fact, but why take that approach when you can prevent them from occurring at all? Developing a strong strategy for Master Data Management allows you to be proactive and keep duplicate or inaccurate records out of your database before they even enter. With hundreds of thousands of entries in your database, it is easy for the entries to get out of hand if not properly managed. As a result, you need to be forward-thinking and consider how you will collect and maintain quality data in a consistent manner. Your strategy will depend on your business’s specific needs and strengths. In developing your data management strategy, you should consider several questions: What information is necessary to create a golden record? How many data entry sources do you use? How are all data sources connected and integrated into the database? How often can your business utilize automatic merges? Who will review records for accuracy during merges? When can you use tools? And who will be responsible for manual intervention? A comprehensive strategy should include consistent standards for how data is collected, stored, and reviewed. It should also highlight who is accountable for reviewing data accuracy and how often. Use the proper tools One of the most vital components of a good strategy is data management tools. Data management tools will help you maintain high data quality by ensuring only quality and checking your data accuracy over time. Some excellent examples of useful tools include the data matching tools mentioned above, batch cleansing tools, data record apps, and real-time verification toolsBatch cleansing can be used to comb through a database and highlight duplicate records, either on a consistent schedule or on demand. Meanwhile, real-time verification tools help to keep your database free of inaccurate email, address, or phone number records, as they prevent you from collecting inaccurate data at all. By preventing errant information from entering your database, you reduce the chance of duplicate entries that harm your database.Even better, data management tools help you save time and resources by doing the work for you. As a result, your staff can focus on other ways to boost your business operations. Achieve the golden record in Master Data Management A golden record is much more than just an achievement–it is a precious resource for businesses to leverage. With a golden record, your ability to reach your customers skyrockets as you gain better information through a complete golden dataset. However, you still need an effective, proper strategy. To learn more about how to achieve a golden record in Master Data Management, contact us at Experian Data Quality. Our comprehensive suite of data quality and data management tools will help your business maintain a high-quality database free of duplicate entries. Explore our tools for yourself and discover how you can get started with achieving golden records in your database. Connect with a rep to learn more:

In today’s economy, data fuels every strategic decision. Poor data quality can lead to costly mistakes, inefficient operations, and missed opportunities — which can be a liability. Experian empowers you to trust your data at every stage. We help businesses manage, cleanse, and connect their information with confidence. The result? Smarter decisions, stronger customer experiences, and measurable growth. Here’s what makes Experian different. 1. We know data Experian is a trusted leader in data quality, analytics, and decision intelligence. For years, leading organizations have relied on Experian to help them turn raw data into meaningful insights that drive smarter decisions and stronger customer relationships. Our approach is built on a deep understanding of how data flows through your business today and into the future — from customer onboarding and marketing to compliance and analytics. 2. We simplify complexity Data lives in many places — from customer relationship management (CRM) to marketing tools and internal databases. Experian’s suite of data management solutions brings all of this information together. Whether it’s: Cleansing to remove inaccuracies Matching to eliminate duplicates Profiling to understand data quality Enriching to add valuable insights Aperture Data Studio – Experian’s unified data quality solution – curates accurate customer data views into a single platform for data management. 3. We delivery accuracy Our data management solutions are built on powerful technology, proven methodologies, and high-quality reference data that work together to keep your information complete and dependable. Through advanced validation, enrichment, and continuous monitoring, Experian helps organizations maintain data they can trust — to drive improved customer experiences and smarter business decisions. 4. We scale with you Whether you’re managing millions of customer records or integrating data across multiple systems, Experian provides the flexibility and performance needed to keep information accurate and accessible at scale. Our cloud-based platforms and powerful API integrations make implementation seamless, so teams can focus on driving outcomes — not maintaining systems. 5. We drive results Take IKEA as a concrete example. By using Experian’s real-time address validation and enrichment solutions, IKEA increased their match rate from 83% (with a competitor) to 95%, enabling better segmentation and more effective marketing. That uplift translated into more confident targeting, improved campaign ROI, and a better customer experience. This is the kind of result we aim for with every engagement—across retail, finance, healthcare, and beyond. Your data journey starts here Data management isn’t a one-time project — it’s an ongoing journey. Experian is more than a solution provider; we’re a long-term partner in your success. With dedicated support, expert consultation, and continuous innovation, Experian helps your organization evolve with data — not fall behind it. The bottom line: Experian turns your data into a competitive advantage In a world where data drives everything, Experian gives you the clarity and confidence to move forward. From data accuracy and enrichment to governance and compliance, Experian provides the tools and expertise you need to make data your most valuable asset.

As a marketing or data professional, you may be familiar with the term “data profiling”. It is the process of examining a dataset to glean information about its contents. This can include statistics about the distribution of values, patterns within the data, unusual values or outliers, and in-depth analysis of its accuracy, or quality. Data profiling can be used to get a better understanding of your data, identify potential issues, and make sure that your data is clean and ready for analysis, or use. In this article, we’ll take a closer look at data profiling and examine some of the common use cases where it is most useful. Why profile your data? Before we explore some of the common use cases for data profiling, we’re going to look at the reasons why it can be so beneficial: Understand your data assets – it is extremely common for large and complex organizations to not fully understand their data landscape. Multiple territories and varying legacy systems that store data lead to data living in silos with varying levels of quality. A data quality report card helps you understand what data your organization holds and how it could be handled going forward. Boost accuracy and quality – by the very nature of the processes you undertake when profiling your data, you will spot inaccuracies and inconsistencies very quickly. Putting plans in place to fix these errors and long-term plans to prevent the same issue in the future will help you protect the value of your data assets. Improve decision-making – in the same way that data profiling can help you spot data inaccuracies, it can also ensure that your core data assets provide more reliable results when used for forecasting or planning. The old saying of “rubbish in / rubbish out” still holds very true and you want to give your organization the best chance to make the right decisions the first time round. Common use cases for data profiling Below, we discuss some of the use cases where data profiling offers the most value: Data governance is the key to sustainably unlocking the full value of the data you own – so that it can underpin your data strategy, not just today, but on a long-term basis. Customer segmentation is an excellent use case for data profiling and something that every organization should undertake. By segmenting your customer data effectively and building a single customer view you unlock a wealth of insights, such as, who is most likely to purchase your products, where you should put physical store locations, and what marketing channels will be most effective to grow your sales. Data migration is a complicated and time-consuming process, but one that is essential for large organizations to be proficient at. A common mistake is for organizations to rush the investigatory phase of their migration, shifting to data movement before a true understanding of the data is present. Data profiling is a crucial stepping stone in the migration process so that you know with supreme clarity what data you have, what will be moving, and where to. Talk to Experian about profiling your data Experian has a range of tools and services to help you profile your data. Our intelligent, self-service data quality and enrichment platform,  Aperture Data Studio, can provide you with all the tools you need to understand and utilize your data assets. Get in touch today:

See why data quality is critical for holiday success across retail, e-commerce, logistics, customer service, and financial services.