Data Validation

Discover how validated address data minimizes shipping costs, prevents delivery delays, and improves ecommerce operations.

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 is one of the most valuable assets a business can have—but only if it's reliable. Organizations rely on data to understand customers, make informed decisions, improve operations, and drive growth. However, inaccurate, incomplete, or outdated information can lead to missed opportunities, inefficient processes, and costly mistakes. So, what makes data reliable? And how can you tell whether the information your business relies on is trustworthy enough to support confident decision-making? Let's explore the key characteristics of reliable data and why they matter. Key characteristics of reliable data every business should know 1. Accuracy Think about the number of times you’ve moved, changed your number, or created a new email. If you’ve altered your personal data in any shape or form over the years, chances are that your customers and prospective clients have, too. While you may think that having extensive data puts you one step ahead of your competitors, you can only begin to rely on the data you have when you are confident that it is accurate and represents your users’ current information. Since data quality is directly correlated to the precision of the data your business possesses, accuracy is the foundational characteristic of reliable data that your business should prioritize when formulating a data management plan. 2. Completeness Complete data is oftentimes the most effective data. While it’s great to have a user’s phone number, address, or any other single piece of information attached to their name in your CRM, you’ll often find that the data with the highest reliability is typically fleshed out and complete. Why does complete data matter? Access to comprehensive data allows you to reach your audience across multiple channels while ensuring the validity of their identity. If you know John Doe would be a great client, but you only know his phone number, your ability to reach him successfully decreases tremendously. Additionality, it is much more challenging to verify the validity of a user’s identity when you only have a fractional identifier stored in your database, which can lead to security issues for your users, too. 3. Relevance What does your customers’ data tell you? Does it paint a picture of your users? Or does your data only provide you with a glimpse of what you truly need to know to execute necessary business strategies that lead the pathway to business growth and success? While data in and of itself may seem inherently valuable, your data should provide an array of information to your organization that shows you what you need to know about your business performance, your audience, and other key metrics. Reliable data is useful data you can lean on to provide you with the answer to questions such as “how do I reach my intended audience,” and “what is the next best strategy that will lead my company to success?” 4. Timeliness In order to thrive in your industry, you must constantly remain a step ahead of your competitors. This means engaging in marketing initiatives and reaching out to prospective customers in an efficient and timely manner. When you wait too long to market to a given audience, you risk losing their attention entirely— steering them directly in your competitors’ hands. Not only will reliable data provide you with a complete customer profile, but it will do so quickly. You want to have an idea of the key identifies that define your audience and who exactly you’re targeting so that you can grow your business efficiently and with success. Data quality management systems that effortlessly provide you with the correct consumer information in a rapid manner will play a significant role in helping you to thrive in a competitive landscape. How to ensure data reliability The characteristics above provide a framework for evaluating the quality and reliability of your data. If your data falls short in any of these areas, identifying and addressing those gaps can help improve decision-making, strengthen customer engagement, and support better business outcomes. Investing in data quality solutions—such as address verification, phone verification, and email verification—is a practical first step toward building more reliable customer data. By validating and enriching customer information, businesses can improve accuracy, increase completeness, and maintain up-to-date records. Combined with a strong data quality management strategy, these solutions help organizations build greater confidence in their data and make more informed decisions. Learn more about data reliability, management, and maturity In our most recent research study, we surveyed leading business professionals across the country to understand the impact of data maturity and investment on a company’s success. You now know what makes data reliable—now it’s time to learn how your company can leverage this information to reap the greatest business success. Ready to get started? Connect with a data quality expert today:

As organizations collect more customer data than ever before, one question continues to come up: should data validation be handled manually or automated? For small datasets, manual validation may seem like the simplest option. But as data volumes grow, relying on people to identify and correct errors becomes increasingly time-consuming, costly, and difficult to scale. When deciding between manual and automated data validation, it's important to consider both the short-term effort and the long-term impact on data quality. Manual data validation Pros: Works well for small datasets Gives teams complete control over validation rules Doesn't require new technology Cons: Time-consuming and resource-intensive Prone to human error Corrects issues after bad data enters your systems Can impact customer experiences before errors are discovered For organizations with limited data or one-time cleanup projects, manual validation can be an effective solution. However, as data volumes increase, maintaining accuracy through manual processes alone becomes increasingly difficult. Automated data validation Pros: Validates large volumes of data quickly and accurately Reduces manual effort and operational costs Applies consistent validation rules across systems Supports real-time validation at the point of data entry or scheduled batch processing Helps prevent data quality issues before they affect your business Cons Requires implementation and integration with existing systems Involves adopting new technology and processes While implementing an automated solution requires upfront planning, the long-term benefits often outweigh the initial investment. Automated data validation helps organizations maintain accurate, reliable data at scale, allowing teams to spend less time correcting errors and more time using data to drive business decisions. For many organizations, manual data validation is no longer practical. Customer data is constantly changing, and maintaining its accuracy requires more than occasional cleanup. Automated data validation helps ensure data is accurate from the moment it's collected, giving teams greater confidence in the information they use every day. Ultimately, the question isn't whether to automate data validation—it's whether your current process can keep pace with the volume, speed, and complexity of today's data. Learn how Experian helps organizations improve data quality with automated data validation solutions.

There’s no question that shopping has transformed into an effortless act. Long gone are the days of rolling out of bed, starting up your car, and mentally preparing to stand around waiting in long checkout lines. So, it should come as no surprise that a large percentage of Americans now engage in the convenience of buying their goods online. Yet given the rise in virtual shopping, many eCommerce businesses still confront the roadblock of getting their customers to finalize their transactions. If you’re like most business leaders, this leaves you with one very important question—how do I optimize the checkout process for my clients? In this article, we’ll delve deeper into shopping cart abandonment and help you identify quick and easy strategies to weave into your business plan to maximize online sales by perfecting the shopping experience. Why do customers abandon at checkout? If a client lands on your page, browses your online products, and adds them to a virtual cart, all signs point to them being interested in your company and what it has to offer. So why, then, do they go through this effort just to abandon your website at checkout? The truth is, there are dozens of factors at play that may cause prospective customers to leave your website prematurely. While you can’t be certain without an exit intent survey, here are a few common problems: Complicated or confusing checkout process: If your customers have to fill out contact information without an autofill option, they may find the checkout process to be too inconvenient—especially when your competitors may have a simplified checkout process on their site. Concerns surrounding data security: Does your website seem spammy or unsecured? If so, users may be hesitant to pass over valuable personal information, including their name, phone number, and address—key data components that are often required at checkout. Technical issues: When users shop online, they anticipate a quick and effortless experience. If your website often presents slow-loading pages that fail to let the user checkout in a reasonable amount of time, it may be the reason behind your higher shopping cart abandonment rates. Optimizing the eCommerce checkout process Despite the common issues we outlined above, there are a few strategic ways to finalize virtual sales and help your business nail its performance KPIs. Here are several key points to keep in mind when mapping out your eCommerce strategy: Data management equals happier customers Data quality automation and investment can have a far-reaching impact on the customer experience. Businesses that prioritize accurate, well-managed data are better positioned to reduce friction, minimize errors, and build stronger relationships with their customers. In simpler terms, investing in data management is a fundamental step in optimizing the checkout process. But how? With an organized and streamlined data management plan, you can segment your audiences more effectively, maintain secure and up-to-date information, and reduce errors that negatively affect fulfillment and communication. Streamlined checkout simplifies the shopping experience Shopping should be a stress-free act. When users check out on your site, do they have an effortless experience with autofill options and intelligent prompts? If not, investing in real-time data solutions can significantly streamline the process while helping you capture accurate information at the point of entry. Consider leveraging address verification to reduce the number of keystrokes required from the user, allowing for a simplified checkout process. Once integrated into your system, address verification provides users with a drop-down selection of validated addresses as they begin typing. Not only does this reduce data entry time, but it also helps ensure proper spelling and formatting. Accurate contact data provides peace of mind for delivery Optimizing what happens after a user checks out is just as important as what happens during the checkout process. To maximize ROI and reduce unnecessary risk, you want to increase the likelihood of successful delivery while keeping customers informed along the way. Help ensure your business captures accurate data at the point of entry with tools such as real-time address verification, phone verification, and email verification. With validation processes in place, you can capture valid, correctly formatted, and legitimate customer data from the start. Human error can prevent users from receiving order confirmation emails, text updates, or even their packages entirely. If a prospective customer mistypes their contact information, real-time error messaging allows them to correct the issue immediately—reducing downstream complications. Data management and shopping cart success go hand in hand While eCommerce continues to grow, not every online shopping experience will result in a sale. To decrease shopping cart abandonment rates, implementing a strong data management strategy is essential. Accurate contact data allows you to precisely reach your audience, fulfill orders successfully, and provide a seamless checkout process. When customers experience ease, accuracy, and reliability, they’re more likely to complete their purchase—and return again. Interested in getting started? Reach out to a data quality specialist to explore your options and create a customized plan suited to your company’s needs.