Data Strategy

In today's digital age, data is at the core of every business operation. Whether you find yourself leading an enterprise business or a burgeoning startup, organizations of every size across all industries are becoming increasingly reliant on data to drive everyday business strategies. But it takes more than just mere data acquisition and analytics to surpass your goals. Enter proactive data quality into the spotlight—the foundation of any data-driven strategy that will successfully guide your company. From reducing operational costs to improving the customer experience, the benefits of prioritizing data quality from the onset are unsurprisingly abundant. In this blog post, we'll dive deeper into what proactive data truly encompasses while highlighting several of the key benefits this data offers for your business. What is proactive data? Proactive data refers to the approach of preventing data errors and issues before they occur, rather than reacting to them after they have already impacted operations. It involves implementing strategies and processes to ensure that data is accurate, complete, and reliable from the outset by emphasizing the importance of data quality. Proactive data management is crucial for businesses because it helps them avoid costly mistakes and inefficiencies. By ensuring that data is clean and accurate from the beginning, businesses can make better decisions, improve customer relationships, and enhance operational efficiency. Clean data allows businesses to trust their information, enabling them to act confidently and strategically. It also helps businesses comply with regulatory requirements, as accurate data is essential for meeting legal standards. In essence, proactive data management sets the foundation for a successful data strategy, enabling businesses to leverage their data effectively to drive growth and innovation. Proactive vs reactive data: what’s the difference? Proactive data quality focuses on preventing errors before they occur, while reactive data quality deals with correcting errors after they have impacted operations. By proactively managing your data quality, you can avoid costly mistakes and ensure that your data is always accurate and reliable. Let’s use two businesses in the retail sector as an example: “Business A” leverages email validation and phone number validation software as customers enter this information on contact forms, ensuring accuracy from the start via a proactive approach. “Business B,” in contrast, engages in reactive data management and collects information without validation, leading to issues like incorrect email addresses and customer insights. This approach requires future corrections, such as cleaning up the database via a batch cleanse and re-sending emails, and increases the likelihood of bounce rates and failed marketing outreach. Because “Business A” leverages proactive management, they’re able to save time and resources by ensuring data accuracy upfront—a feature that will lead to more effective marketing campaign, better customer relationships, and much more. Why is proactive data important for your business? Relying solely on reactive data quality measures can be risky. By proactively managing your data quality, you can improve decision-making, enhance customer relationships, reduce costs, ensure regulatory compliance, and increase operational efficiency. 5 benefits of proactive data quality Proactive data quality measures are essential for ensuring that your data is accurate, reliable, and up to date from the start. Let’s take a look at 5 key benefits your business can enjoy by adopting this approach: 1. Enhanced decision-making Inaccurate or incomplete data can lead to costly errors and misguided decisions. Proactive data quality measures ensure that organizations have access to reliable information when making critical choices. By maintaining high data quality standards, businesses can minimize risks, identify opportunities, and make informed decisions that drive success. Whether you leverage enrichment services to expand on your existing database or make use of data validation software, be sure to prioritize complete and accurate data that will empower you to make better decisions for your business. 2. Improved customer relationships Customers expect personalized and seamless experiences from the brands they engage with. Proactive data quality ensures that customer data is accurate and up to date, enabling businesses to deliver targeted marketing campaigns, personalized recommendations, and tailored services. By understanding their customers better, organizations can build stronger relationships, foster loyalty, and drive long-term growth. 3. Long-term operational cost reduction Proactive data quality measures can help reduce costs associated with data errors and inefficiencies. Proactively addressing data quality issues streamlines operations by reducing errors, eliminating redundancies, and automating workflows. This not only saves time and resources but also enhances productivity and agility, enabling businesses to adapt swiftly to changing market demands. A great first step in proactive data management that will achieve this feature is via data cleansing. By removing duplicates and ensuring data accuracy, you can avoid ineffective outreach campaigns and optimize resources, saving your business time and money. 4. Regulatory compliance With the increasing focus on data privacy and security regulations, businesses face mounting pressure to protect sensitive information and ensure compliance. Proactive data quality measures help organizations maintain compliance by ensuring data accuracy, integrity, and confidentiality. By implementing robust data governance frameworks and monitoring mechanisms, businesses can mitigate compliance risks and avoid potentially costly penalties. 5. Adaptability and competitive advantage Proactive data quality enables organizations to unlock key business insights, uncover market trends, and identify emerging opportunities. By leveraging high-quality data as a strategic asset, businesses can innovate faster, enter new markets confidently, and stay ahead of the competition. Not only does this prove your business can adapt quickly, but it also serves as a strong signal of your ability to remain ahead as business strategies evolve, too. Connect today to ensure optimal data efficiency Streamlined, proactive data processes improve operational efficiency and productivity, helping your business to make the right decisions necessary for success. Ready to learn more? Our team is here to help. Fill out the form to get started:

In today's data-driven world, companies across industries recognize data as a powerful asset for decision-making. But what constitutes reliable data? How can you assess whether the data your business relies on is mature and dependable enough to instill confidence in your team and drive strategic decisions? Understanding the 10 characteristics of data quality makes it easier to evaluate and enhance the quality of your data, which will serve as a solid foundation for your organization's growth and success. What are the 10 characteristics of data quality? The criteria for good data quality comes down to 10 data characteristics, which are: Accuracy Accessibility Completeness Consistency Validity (Integrity) Uniqueness Currency Reliability Relevancy Timeliness These elements of data quality, in one way or another, build the foundation for an organization to make the best decisions for its business and better communicate and engage with customers. 1. Accuracy The first piece of criteria for good data quality to consider is accuracy, which verifies an organization's information is correct. The measured value should match the actual, true value, and the data must be error-free. Error-free data should be up-to-date, not redundant, and have no typos. Prioritizing data accuracy means organizations can make more informed decisions, foster stakeholder trust, and achieve better business outcomes. When developing data management strategies, remember the actual value of data lies in its accuracy and ability to represent the real world accurately. The overarching goal should be to increase data accuracy, especially as datasets grow continually. 2. Accessibility Accessible data is obtainable at the time it is needed and by those who need it. Having data readily obtainable with traceable changes allows organizations to enhance transparency, accountability, and efficiency. This data quality characteristic is essential for timely decision-making and effective business operations. Data accessibility is not just available when needed but is also usable. The ability to quickly access data allows businesses to respond promptly to market changes, internal demands, and customer needs. If data is inaccessible, its value diminishes, as delays in obtaining the necessary information can lead to missed opportunities, inefficiencies, and poor decision-making. 3. Completeness Completeness refers to whether all the necessary data is available. For instance, while a customer's first and last name might be crucial for communication, their middle initial might be optional. Comprehensive data lets businesses engage effectively with customers, make informed decisions, and operate efficiently. Completeness is one of the critical data quality characteristics because incomplete information can render data unusable. Imagine sending out mail without the recipient's last name. The mail might not reach the intended address, wasting resources, time, and money, ultimately leading to missed opportunities to connect with customers. Incomplete data can impact business operations, decision-making, and customer engagement. 4. Consistency Consistency is another one of the necessary characteristics of data quality. It means the same information is stored and used across multiple instances to match each other. Consistent data is the percentage of matched values across various records, making accurate and meaningful data analytics essential. Data consistency prevents organizations from using contradictory information that undermines their operations and decision-making processes. Consistent data supports accurate analytics and effective business operations and fosters trust in the information used across the enterprise. 5. Validity (Integrity) Validity is one of the data quality dimensions verifying values align with specific domain requirements and formats. It signifies that data attributes are appropriate and accurate for their intended use, which any organization needs for maintaining data integrity and usability. This fundamental characteristic of data quality makes sure data is fit for its intended purpose. By maintaining high data validity, organizations can enhance the accuracy and reliability of their data for better decision-making and operational efficiency. 6. Uniqueness Uniqueness verifies that each data point is recorded only once in a dataset, eliminating duplication and overlaps. This data quality characteristic maintains the integrity and reliability of data, supporting accurate analysis and effective decision-making. Prioritizing uniqueness improves an organization's data accuracy and reliability, leading to more trustworthy analytics. Implementing thorough data cleansing and deduplication processes allows for keeping unique records, further building upon data governance and compliance efforts. As a result, fostering data uniqueness is key to effective data management and organizational success. 7. Currency Currency directly impacts the relevance and accuracy of the information used for decision-making. Currency refers to how recently the data was collected or updated to reflect the most up-to-date and contemporary reality. Data currency checks that data remains relevant and accurate over time. Focusing on the recency of data collection and updates allows organizations to have high data quality for more accurate insights and better decision-making. Maintaining current data is considered part of the criteria for good data quality, as it drives organizational success in a rapidly changing environment. 8. Reliability Reliability encompasses the degree to which data is factual across various sources and systems. Reliable data means the information used for analysis and decision-making falls within the acceptable values for the specific task or question. Reliable data increases trust, improves operational efficiency, and mitigates risks associated with inaccurate data. Implementing validation rules and maintaining audit trails builds data reliability. Ultimately, reliable data provides a solid foundation of accurate and trustworthy information. 9. Relevancy Relevance is one of the elements of data quality determining whether collected data serves a meaningful purpose. It pertains to whether the data collected is necessary and helpful in achieving specific business goals and objectives. Without relevancy, data collection efforts can become wasteful and unproductive. Relevant data collection efforts are purposeful and align with business goals. Data relevancy allows organizations to use resources more efficiently, extract valuable insights, and conduct focused analyses. Implementing practices to define goals, evaluate the necessity of data, and align data with applications creates relevant data. 10. Timeliness Timeliness refers to how current and up-to-date the information is. In an era where information constantly evolves, timeliness can be considered one of the mandatory data quality dimensions. Organizations can gain a competitive edge by designing timely data collection, storage, and updates and making informed decisions based on the most recent information available. Data timeliness contributes to organizational success by enacting data quality management systems supporting rapid data processing and frequent updates. Maintaining data quality High-quality data is essential for a business's success, and incorporating these characteristics of data quality in your data management plan will save you time, money, and resources in the long term. Developing the criteria for good data quality in your organization helps you avoid relying on invalid or incomplete information. Experian’s data quality management tools can help you with your data quality platform. Contact us today to get started with cleansing and maintaining your data. Connect with a data quality expert today:

Data-driven decision making has become a necessity for the best businesses to stay on top of their daily operations and respond to areas for improvement. However, the quality of this data will greatly affect how useful it will be. Maintaining a high quality of data allows you to make decisions that improve customer relations and increase revenue. Poor quality data informs poor decisions and restricts access to customers while also using up time and resources to correct. Because of its capabilities, data should play a vital role in informing your business decisions. As a result, your business should have a clear and well-thought-out plan for how data will be collected, maintained and analyzed. Improving your data quality through careful data collection and maintenance should be a key part of your business operations and will set your business apart from the others. Benefits of improved data quality Improved data quality gives you a complete, accurate picture of your business and the customers you serve, allowing you to make more informed decisions that save time and money. High-quality data can be used to inform marketing campaigns that increase customer engagement, develop more trust in customer relationships and receive a higher return on investment in sales and marketing strategies. Access to accurate data takes the guesswork out of key business decisions, increasing efficiency and saving costs. By following just a few steps, you can set your business up for success with a clear plan for collecting more valuable data and using it to inform critical business decisions. Steps to improve data quality Many organizations manage data quality through separate departmental strategies, which can lead to inconsistent standards, duplicated efforts and less effective outcomes. Assigning a single data owner, such as a chief data officer, creates clear accountability for the quality, governance and standards of your data assets. Centralized ownership can also help align data quality initiatives with broader business goals and improve collaboration across departments. This central data owner should be supported by a team of data professionals—such as data stewards, data service officers, analytics professionals and data scientists—who can help enforce policies and promote the use of data to drive insights across the business. Implement proactive processes Improving your data quality requires careful and constant consideration to help your business maintain a strong and effective data strategy. Your business should develop a clear plan for using and analyzing data as well as keeping that data up-to-date and accurate. To improve and sustain your business’s data quality, you should carry out the following steps: 1. Develop your objectives Data quality means something different across different organizations. For some, it’s ensuring that customer contact data is accurate so that shipments are received in a timely manner. For others, it could be complete prospect profiles that help with marketing segmentation efforts. At its heart, data quality is about being fit for a desired purpose. In order to collect meaningful data, your business should have clear objectives in place for how this data will be used to improve decision-making and daily operations. When developing your objectives, you should consider the following factors: Your business goals How you measure progress towards goals What data you need from customers and clients How this data will be used and analyzed 2. Assess your current data Before implementing any data quality improvement plan, you need to understand where you stand today. Using the data quality sophistication curve, you can determine where you are related to data management efforts and what your next steps should be. There are four stages of sophistication your organization can fall into: Almost half of organizations today fall into the reactive or unaware stage, meaning there is a lot of room for data quality improvement. Data assessment should be a continuous process. The most successful businesses are constantly assessing the quality of their data and the effectiveness of their current data management plan. A constant reassessment of data allows a business to react to areas of concern and make improvements when necessary. 3. Cleanse your existing data Inaccuracies or inconsistencies in your data will harm your data quality and prevent you from using it for effective decision-making. Over time, data sets become riddled with out-of-date or incorrect information as customer data changes or information is improperly added to your database. Data cleansing is the process of correcting incomplete or inaccurate information, fixing formatting issues and more. Issues in your data set that require data cleansing include: Typos in data entries Incomplete data fields Inconsistent formatting of addresses or contact information Out-of-date contact information Duplicate data entries Addressing incorrect information in your database provides you with more confidence that your data provides an accurate picture of your customers and allows you to make appropriate business decisions as a result. There are a variety of tools available to help you cleanse your data. These tools include email address list cleansing, real-time address verification and more to help you cleanse your database of errors and also prevent incorrect information from entering at all. 4. Establish a data collection and management plan Because data collection issues are nearly inevitable, you should have a carefully considered plan in place to prevent these issues from arising and addressing them if they do. Being proactive with your data collection allows you to build a database of accurate and informative data instead of a database that needs to be fixed later. This will allow you to spend more time focusing on making decisions than fixing your data. Considerations for an effective data collection and management plan include preventing poor quality data from entering your database and consistently assessing the quality of the data you already have. Methods to include in an effective plan include: Using tools like real-time email verification to ensure accuracy upon entry Creating required fields on forms to avoid incomplete information Regular, periodic data cleansing to catch errors or outdated information Providing appropriate training to all staff involved in data assessment and decision-making Having a clear data collection and management plan will save your business valuable time and money correcting mistakes and allow you to be confident in using your data for strategic purposes. 5. Develop a plan for maintenance and improvement In order to maintain strong data quality and business growth, your business should have a plan in place to regularly assess the quality of data and its use in decision-making. Data-driven decision-making is a constantly evolving strategy. Effective plans for maintenance and improvement require consistent buy-in from everyone in the business. A data-driven business should foster a healthy culture of improvement where everyone is comfortable acknowledging when change is necessary. The ability to acknowledge when issues like flawed data collection methods or inconsistent data monitoring arise is monumental for business growth. Recognizing these issues and developing an appropriate response helps preserve your investments and will ultimately show greater returns than a business that is resistant to change. How to improve data quality for your business Implementation of data quality technology solutions across a company can streamline and unify data improvement efforts. This will result in the enforcement of consistent data standards across the organization, which means your data can be more easily used for advanced, comprehensive decision-making. To learn more about how to improve your data quality, download our paper on creating a strong and calculated data quality strategy. We also encourage you to try our email verification and data validation tools to strengthen your dataset. Contact Experian today to see how our tools and services can give you the help you need to collect, maintain and analyze accurate and meaningful data. Get help creating a data quality improvement plan

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:

Stop losing revenue to bad data. Discover a 6-step framework to define, validate, and scale your lead generation data quality for faster sales outreach.

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.

Prepare for the holiday rush with data quality strategies that improve customer experiences, streamline operations, and drive retail success.

In today’s digital-forward society, you can’t avoid the growing importance of data—especially in the realm of business. Regardless of an organization’s size, industry, or business model, you can’t plan for optimal growth and success without also discussing data. A mature approach to data can help businesses build greater trust in their information, make more informed decisions, and use data more effectively across the organization. But what exactly does it mean to achieve data maturity, and how can you determine whether your business needs to optimize its approach to data? Below, we’ll delve into what data maturity truly entails while helping you discover where your business lands on this scale. What is data maturity? In the business world, data refers to any information an organization collects surrounding its customers, workers, marketing plans, and organization at large. But if data is the information collected and processed, what exactly is data maturity? Put simply, data maturity refers to the extent to which your business can collect valuable data, derive meaning from it, and leverage this information in the decision-making process. Your business’s data maturity is determined by how your organization’s data is: Processed Analyzed Utilized It’s important to note that the quantity of data does not directly correlate to the maturity of the data in question. The goal is to strive for quality data collection that helps an organization successfully make the best marketing and operational decisions possible. At Experian, we track three key components that make up data maturity: Strategy: We look at the processes behind data management, from implementing data quality control measures to sharing insights with a wider range of roles. People and skills: We take note of data ownership, considering whether there is a chief data officer in place and who manages insights across the business. We believe that data skills are becoming increasingly important, whether an organization has a data literacy program or is hiring for data skills. Technology: We look at an organization’s tech stack, data quality management functionality, and how user-friendly the software is. Depending on the organization’s data skill level and resources, technology can make or break the data experience. The three elements live across the four levels of data maturity, which we discuss in the next section. The 4 levels of data maturity—how data mature is your business? Below, we’ll delve into the four stages we use to assess data maturity that can serve as a benchmark for how your business is performing from a data quality standpoint. 1. Under-developed If you’ve never really put much thought into your business’s data and find that data at large has never been a topic of discussion in your organization, you’re likely to find yourself starting from square one. At the base level, organizations that lack an understanding of data quality and its implications fall into an under-developed category. If you notice your organization confronting data quality issues, manually fixing data problems as they arise, and having an inconsistent understanding of data across the business, you may fall into this category. For organizations at the under-developed stage, establishing a more intentional approach to data management can create a stronger foundation for future progress. Identifying existing data quality challenges, defining ownership, and introducing consistent processes can help your organization begin moving toward greater data maturity. Reaching out to a data management expert can also be a helpful place to start. 2. Reactive Maybe your organization has started to think about data and its importance within your business, but it hasn’t laid the foundation for the proper metrics or processes necessary to utilize this information in a meaningful way just yet. As the name indicates, a “reactive” business reacts to data when issues arise and need to be fixed. However, discussions surrounding data aren’t evenly spread throughout the business. While tackling data quality issues in silos is a start, it’s crucial to expand the data experience throughout your organization. This means that if only certain departments have the tools and knowledge necessary to leverage data insights, it’s important to expand data access and training to those outside of traditional IT roles, too. 3. Proactive Moving along the data maturity scale, businesses are likely to achieve a proactive state once they’ve established clear data quality success metrics that help them make better use of their data across the board. Within this level, there’s a more universal knowledge of data across the organization, which helps team members use data in meaningful ways when performing their daily tasks. There’s also an increased emphasis on data discovery, which places the organization a tier above “reactive” businesses that focus more heavily on root cause analysis. If you think your business identifies with a proactive level, you may still confront issues surrounding data trustworthiness and optimal data governance. Investing in a trusted data management program could be the next step in your journey toward higher data maturity. 4. Optimized At the highest level, businesses with the greatest data maturity have established an advanced data extraction and interpretation strategy to reap more business value from their data. An organization in this category has successfully: Established advanced data quality measuresCreated confidence in the quality of its dataInvested in the right data professionals to oversee company-wide data assetsIncorporated data quality monitoring into everyday operations While it may seem like achieving a high level of data maturity is the endpoint, it’s important to note that data management is an ongoing process. Businesses at an optimized level can still benefit from improving their data management programs over time to reduce costs and automate more of their daily processes. If you’re still not sure where your organization lands on the data maturity scale, we’ve put together a free assessment that can help you get a better idea of where you stand so that you can begin taking the steps necessary to strengthen your approach to data. My business isn't as data-mature as I thought: What now? Regardless of where your business lands on the data maturity scale, your business could still benefit from enhancing your data’s trustworthiness and developing new data quality strategies as time progresses. Data isn’t stagnant, so your data strategy shouldn’t be, either. In the modern world of business, data has become a competitive asset, and what you do with the information you collect matters. At Experian, data is top of mind for every decision we make. When you reach out to a team member, one of our data quality experts can connect you with the resources you need to kickstart your data maturity journey. Unlock the importance of data in your business Building greater data maturity can help your organization get more value from its data and create a stronger foundation for informed decision-making. Data quality and governance should remain important considerations for your business—regardless of your organization’s industry, size, or overarching goals. As you plan for the future, evaluating your current data management practices can help uncover opportunities to strengthen how data is managed, trusted, and used across the organization. Explore how strengthening data quality and data management can help your organization advance its data maturity journey and make data a more valuable part of future business planning. Fill out the form to connect with an expert: