Data Governance

Learn the three essential roles every high-performing data governance team needs to drive successful governance initiatives.

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:

Data governance verifies that data meets precise standards and business rules when entered into a database and used throughout an organization, giving businesses more control over their assets. Understanding and integrating data governance into your organization's data strategy is pivotal in an age where accurate data fuels daily operations across various industries. Data governance framework (system) The relationship between data governance and data quality is closely intertwined. Data quality refines your organization's data for accuracy, reliability, and fitness for purpose. Data governance allocates the right resources to data collection, cleansing, and usage.Prioritizing data quality lets your organization establish a sturdy foundation for effective data governance practices by: Mitigating risks associated with incomplete or inaccurate data Protecting against bad decision-making and resource waste Fostering confidence in monitoring, auditing, and accessing data Standardizing data guarantees validity, trustworthiness, and correct formatting. Focusing on data quality aligns the data with data governance principles, allowing your organization to maximize its potential while guarding its competitive edge in the marketplace. The objectives of data governance Data governance objectives are to build data integrity, security, and compliance within organizations. At its core, data governance for businesses works to achieve these key goals: Validating data quality: Standardized processes and automated rules allow organizations to maintain consistent and accurate data across various sources. This consistency enhances operational efficiency while fostering reliable data. Securing data: Effective data governance guards against vulnerabilities and breaches from cyber attacks. Transparent control processes and best practices for data handling protect customers' confidential information and uphold their trust. Facilitating compliance: Adhering to regulatory mandates is necessary for maintaining operational integrity and a good reputation in highly regulated industries like insurance or finance. Creating a comprehensive data governance framework guarantees compliance with industry regulations while avoiding penalties and reputational damage. Prioritizing data quality, security, and compliance allows your organization to unlock the full potential of your data, drive innovation, enhance operational efficiency, and maintain a competitive edge in the digital age. Data governance policies and procedures Implementing data governance means having a strategy with clear policies and procedures tailored to your organization. It necessitates forming policies, rules, and organizational structures within a documented governance framework. Successful data governance relies on planning and execution across three key dimensions: people, processes, and technology. When putting together your framework, focus on: Establishing a strong team: Effective data governance for businesses lies in a dedicated team overseeing data quality and governance initiatives. This team of data managers and stewards checks for alignment between governance objectives and business needs. Defining clear roles and responsibilities lets data stakeholders uphold quality standards and drive governance efforts forward. Developing clear processes: Well-defined processes governing data storage, access, security, and management are central to any data governance framework. These processes are the guiding principles for initiatives to remain compliant with regulations and maintain data integrity. Incorporating the right tools: Technology alone cannot fulfill data governance objectives, but using the right tools streamlines governance efforts. Integrating data governance tools such as data cataloging platforms, classification systems, and data quality tools makes enforcing your organization's governance policies more effective. Data governance tools There are many tools your business can use to increase the effectiveness of your data governance framework. Using data quality tools frees up valuable time in your day-to-day organizational functions by improving the quality and usability of your data. Incorporating data governance tools into your current data management strategy helps: Prevent inaccurate data from entering your database Remove duplicate information Correct errors Store and maintain data Cleanse email lists Continuously update your database with correct information Experian offers several tools handling these above services and more. From our email verification tools to real-time address verification tools, we take the hassle out of managing your data. Then, you have more room to expand your organization's data management strategy. Benefits of data governance Data governance drives organizational success and unlocks the full potential of data assets. Organizations get a multitude of benefits extending beyond regulatory compliance: Preservation of data: Maintaining data quality means data remains accurate, up-to-date, and error-free. Organizations can gain insights for decision-making that contribute to sustained growth and success. Increased data value: Implementing data governance improves data accuracy, consistency, and accessibility. It also drives operational efficiency, reduces costs, and enables faster organizational decision-making. Competitive advantage: Organizations in industries reliant on data-driven decision-making can have a competitive edge when anticipating market trends and delivering superior customer experiences. Enhanced customer experience: Data governance improves the customer experience by verifying that the data used to engage with customers is correct, reliable, and actionable. Then, organizations can personalize interactions, deliver targeted promotions, and deliver exceptional customer service. Security: Data governance promotes a culture of trust and transparency, safeguards sensitive data, protects organizational assets, and fosters stakeholder confidence. Data governance enables businesses to harness the full potential of their data assets while certifying compliance, driving innovation, and delivering value to customers. Data governance challenges Despite data governance's importance for organizations, these initiatives often come with their own challenges: Organizational alignment: Aligning stakeholders across your organization around key data assets, definitions, and formats is difficult. Opposing views on data entities can impede progress and cause conflicts. Everyone must work together and communicate to establish common data definitions and formats. Lack of support: Successful data governance programs need sponsorship from executives and individuals. Without adequate sponsorship and support, governance efforts may lack direction and fail to gain traction. Relevant data architecture and processes: Implementing an effective data governance program relies heavily on the right tools, data architecture, and processes. Adopting data catalogs and metadata management processes is essential for creating an accurate and relevant data inventory. Securing sufficient resources and skills: Organizations must allocate funding, leadership, and expertise to governance efforts. Appointing the right individuals to key roles and providing training and support are critical for program success. Governance in the cloud: As organizations increasingly use cloud-based technologies, managing data security, compliance, and privacy presents challenges concerning data residency and sovereignty issues. Governance practices must adapt to the cloud while maintaining consistency and compliance across hybrid and multi-cloud environments. Managing expectations and internal changes: Due to significant organizational changes, data governance is often slow. Setting realistic expectations and implementing change management strategies is essential for overcoming internal resistance. While data governance for businesses has substantial benefits, organizations must navigate numerous hurdles to implement governance initiatives successfully. Future trends in data governance The future of data governance indicates a significant shift in mindset and technological advancements. Looking forward, some current developments in data governance include: An all-company approach: Data governance is transitioning from confinement to specific departments towards a collective responsibility for data management. Data governance as a service (DGaaS): Third-party providers offer DGaaS to help organizations classify and map data, securely store data in the cloud, and establish governance models for better accessibility and scalability. Data lineage in generative AI: As Generative AI adoption grows, there is a bigger focus on data lineage for error detection and post-hoc analysis, driving the need for sophisticated data governance Automation of data governance: Automation is pivotal in data governance, enabling organizations to streamline governance tasks, improve data quality, and stay compliant through automated processes and AI-driven tools. Ethics and privacy concerns: Regulations like GDPR and CCPA maintain ethical data governance practices and transparency. However, as more cloud-based services become available, more regulations will need to be created to address privacy concerns regarding ethical data usage. As data governance evolves, comprehensive frameworks encompassing data, roles, processes, communications, metrics, and tools are essential for your organization. Incorporating data governance into your business Data governance is necessary for any organization's data management strategy. Taking the proper steps to govern your data means maintaining reliable data, making well-informed decisions, and complying with industry regulations. Experian's data governance tools streamline your data management processes so you can focus on using your data properly. Contact Experian today to learn more about how our data management solution can help with your data governance initiatives. Fill out the form below to learn more:

The Consumer Financial Protection Bureau's (CFPB) Regulation F transformed how debt collectors communicate with consumers by establishing clear rules for digital outreach. Today, email, text messaging, phone calls, and certain social media communications remain valuable tools for engaging consumers while maintaining compliance with the Fair Debt Collection Practices Act (FDCPA). As digital communication continues to shape customer expectations, organizations have an opportunity to improve engagement, streamline operations, and enhance the consumer experience—provided they have accurate contact data and compliant communication practices in place. What is Regulation F? Regulation F is the CFPB's implementing regulation for the FDCPA. It establishes guidelines for how debt collectors communicate with consumers across both traditional and digital channels. The regulation permits collectors to communicate through channels such as: Email Phone Text messaging Certain social media platforms At the same time, Regulation F includes safeguards designed to protect consumers from excessive or unwanted communications. How does Regulation F work? Regulation F is the CFPB's implementing regulation for the FDCPA. It establishes guidelines for how debt collectors communicate with consumers across both traditional and digital channels. The regulation permits collectors to communicate through channels such as: EmailPhoneText messagingCertain social media platforms At the same time, Regulation F includes safeguards designed to protect consumers from excessive or unwanted communicationsRegulation F outlines requirements for both the frequency and manner of communications with consumers. For telephone communications, collectors are generally limited in how often they may attempt to contact a consumer regarding a specific debt. The rule creates a presumption that a debt collector violates federal law if they place more than seven telephone calls within seven consecutive days about a particular debt, or within seven days after having a telephone conversation with the consumer regarding that debt. These telephone call limits do not apply to email, text messaging, or social media communications. However, digital communications are subject to their own compliance requirements. For example, electronic communications must provide consumers with a clear and simple way to opt out of future messages sent to that email address or phone number. Consumers may also use these channels to refuse payment or request that communications cease, consistent with the FDCPA. Organizations that incorporate digital outreach into their collections strategy should ensure their communication practices align with Regulation F while providing consumers with a positive, transparent experience. Regulation F in today's collections environment Digital communication has become an established part of collections operations. Many consumers prefer the convenience of email or text messaging over traditional phone calls, making accurate digital contact information increasingly important. As organizations expand their digital collections strategies, success depends on more than simply adding new communication channels. High-quality consumer data helps improve deliverability, reduce operational inefficiencies, and support compliance efforts by ensuring communications reach the intended recipient. How Experian supports compliant digital collections A successful digital collections strategy depends on reaching consumers through accurate, reliable contact information. Experian's email append and verification solutions help organizations improve email coverage, validate deliverable addresses, reduce bounce rates, and support compliance efforts with higher-quality consumer data. By strengthening the accuracy of customer records, organizations can communicate more confidently while improving the effectiveness of their digital outreach. Why data quality matters A successful digital collections strategy begins with trusted consumer data. Accurate email addresses help organizations: Improve consumer engagement Increase email deliverability Reduce bounce rates Support regulatory compliance Create a better overall customer experience Improve operational efficiency With more than 20 years of experience in data quality, Experian helps organizations communicate with greater confidence through industry-leading email verification and email append solutions. Looking ahead Regulation F established a framework that continues to support compliant digital communications in debt collection. As consumer preferences evolve, organizations that combine compliant outreach practices with high-quality contact data are better positioned to improve engagement while delivering a more seamless customer experience. Experian's email verification and append solutions help organizations build stronger digital collections strategies by providing accurate, actionable consumer contact information. Fill out the form to learn more:

Organizations rely on data to make everything from day-to-day operational decisions to long-term strategic investments. But simply collecting data isn't enough. To get value from it, you need both high-quality data and a strong data governance framework. Although the terms data quality and data governance are often used interchangeably, they serve different purposes. Data quality focuses on the condition of your data, while data governance establishes the policies, processes, and accountability needed to manage it throughout its lifecycle. Together, they help organizations build trust in their data, reduce risk, improve compliance, and make better business decisions. What is data quality? Data quality refers to how well your data meets the needs of your business. High-quality data is: Accurate Complete Consistent Timely Valid Fit for its intended purpose Poor data quality can lead to duplicate customer records, failed deliveries, inaccurate reporting, compliance issues, and poor customer experiences. Data quality is maintained through ongoing processes such as: Data validation Data standardization Data cleansing Data matching and deduplication Data enrichment Data profiling and monitoring Rather than treating data quality as a one-time cleanup project, organizations should continuously monitor and improve data as it's collected and maintained over time. What is data governance? Data governance is the framework that defines how data is managed across an organization. It establishes the people, policies, standards, and technologies that ensure data is secure, reliable, and used consistently. A successful data governance strategy helps answer questions like: Who owns this data? Who can access it? How should it be maintained? What quality standards should it meet? How do we ensure compliance with internal policies and external regulations? While governance sets the rules, it also creates accountability for maintaining trusted data across the organization. Data quality vs. data governance: Key differences Although closely related, data quality and data governance address different aspects of data management. Data qualityData governance Focuses on improving the condition of dataFocuses on managing data throughout its lifecycleMeasures accuracy, completeness, consistency, and validityDefines policies, ownership, standards, and accountabilityUses validation, cleansing, matching, and enrichmentUses governance frameworks, roles, policies, and controlsImproves trust in business dataEnsures data is managed consistently across the organization Think of it this way: data governance defines the rules, while data quality ensures the data meets those rules. Why data quality and data governance work together Neither initiative is fully effective without the other. Strong governance establishes the standards your data should meet, while data quality processes continuously measure and improve data against those standards. For example, a governance policy may require every customer record to contain a valid mailing address. Address verification and validation tools help enforce that policy by identifying incomplete, incorrect, or undeliverable addresses before they enter your systems. Together, governance and quality help organizations: Build confidence in business decisions Improve reporting and analytics Increase operational efficiency Strengthen regulatory compliance Reduce duplicate and inaccurate records Deliver better customer experiences Build a stronger data management strategy Data quality and data governance aren't competing initiatives—they're complementary parts of an effective data management strategy. Governance provides the framework, while data quality ensures the information within that framework remains accurate, complete, and reliable. With Experian's data quality solutions, organizations can validate, standardize, enrich, and monitor their data to support governance initiatives and improve confidence in every business decision. Explore Experian's data quality solutions to learn how you can improve data accuracy, strengthen governance efforts, and build greater trust in your data.

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!

Anyone can set a goal of saving money, but it is impossible to reach that goal if you don’t know when to put money away, don’t monitor your spending, or never review your bank statements. Any time you set a goal, monitoring your path to success is crucial to achieving it, and achieving effective data governance is no different. Establishing metrics and performance indicators allows you to measure progress and adjust your data governance strategy as necessary to maximize success. Once you establish proper data governance metrics and KPIs based on your business goals, you can take your data governance strategy and success to the next level. What are data governance metrics and KPIs? Data governance is the process used to ensure data follows strict rules and regulations as it enters a database and is used throughout an organization. Data governance metrics are indicators put in place in order to measure the effectiveness of an organization’s data governance processes. Key performance indicators (KPIs) provide additional ways to measure progress.Data governance centers around three key areas—people, process, and technology. Because data governance is a comprehensive process and is often more difficult to measure, organizations need to establish metrics in each of the three areas to experience success.Effective data governance strategies are crucial for organizations to stay in compliance with regulations, protect consumer data, and ensure that data is reliable for gaining insights and informing future business strategy. By using data governance metrics, organizations and businesses can ensure they remain in compliance and maintain quality, valuable data. Why are data governance metrics important? Data governance metrics and key performance indicators (KPIs) are vital for measuring success and monitoring progress. Measuring progress in data governance is important for determining whether or not your current data governance practices are effective and how to improve them if necessary.When you focus on KPIs for governance and compliance, you are able to determine how effective your processes are and how they may need to be adapted over time. After all, setting a strategy and hoping for the best is not a recipe for success, as you can never address issues that you are not aware of.Data governance metrics are powerful tools for any data-driven business, as they allow businesses to:  Highlight the effectiveness of a data governance strategy on areas like data quality and accuracy Demonstrate the success of data governance initiatives to stakeholders Uncover areas of improvement in your data governance and data management strategies Identify the need for a change of approach or priorities in your data governance initiatives Without focusing on data governance metrics, it can be easy for your best processes and strategies to fall by the wayside. Above all else, metrics and key performance indicators help provide accountability and ensure that your business maintains strong standards in its data governance procedures.  How to measure success in data governance One of the first steps in measuring success in data governance is determining what success looks like for your business. To do so, you must consider your business objectives, what operations data governance will improve, the desired data governance outcomes, and what stakeholders stand to benefit. Generally, to reach success in data governance, you should focus on four key areas to measure your business’s effectiveness in getting there. Data quality Data quality is a crucial part of any data governance strategy, and fortunately, it is not difficult to measure. Strong data quality is a direct consequence of effective data governance and data management. Some measurable aspects of data quality include: Data accuracy Data completeness Data validity Data integrity The easiest way to track your data quality is through the use of a data quality platform, which can provide a breakdown of your data’s accuracy and completeness through a dashboard and highlight areas of improvement. You can also improve data quality by using tools like email, address, and phone number verification. Each of these tools can be used in real-time or through batch cleansing methods to prevent errant data from entering your database and remove the errors that sneak in over time. People People are one of the driving forces of proper data governance in your organization. Your organization should consider who is accountable for managing data and what resources they need to succeed. For many businesses, this involves establishing set roles and teams. Some of the measurable components in this area include: How many hours of training stakeholders receive The percentage of employees trained on data literacy The percentage of employees trained on data governance strategies The number of stakeholders trained in a given time period The percentage of employees that engage with data management meetings and events Establishing these metrics allows your organization to ensure that data management teams are adequately prepared to handle tasks surrounding data governance. Process The processes within data governance help describe how your organization approaches the strategy. Processes reflect how data is collected, moved, stored, accessed, and secured. Establishing effective processes is vital for ensuring that data remains accurate and valuable throughout its life in an organization.  Measurable metrics and KPIs for your business include: The number of passed data audits carried out The number or percentage of areas and standards where your business is compliant The number of employee logins to a data insights tool or dashboard Technology Once your organization has the right systems and a strong team in place, technology is what really seals the deal. Data governance technology helps to support the process in several ways, saving time and energy for the teams responsible for managing and maintaining its quality. Data governance tools can: Assist with data profiling Identify and correct errors in data entries Assess the validity of data that enters a database Securely store and maintain data Seamlessly collect and transfer data throughout the organization Given the potential functions of data governance technology, some measurable metrics in this area can include: How often you run batch cleansing processes The number of attributes profiled The number of data entries corrected by a tool The percentage of accurate data in a database Using data governance metrics to monitor and assess the effectiveness of your technology makes it easier to determine whether your tools are achieving the desired outcomes. If the metrics are not providing the returns you would hope, then you can consider if there are better ways to use the tools or if you simply need better tools. Develop your data governance metrics for sustained success It's one thing to take strides in your data governance processes, but it is another thing to make sure that these strides are actually effective. Data governance metrics and KPIs play an important role for any data-driven business by ensuring that a data governance program is effective and achieving the desired outcomes. Establishing a strong data governance strategy is even easier with the right tools, and Experian can help. Our data quality solutions are geared toward helping you get the most out of your data, from ensuring its accuracy and completeness to gaining valuable insights and analysis from it. Contact us to learn more about how to take your data governance strategies to the next level.