Why data quality is now a regulatory control

Data quality errors have several consequences for a business, affecting decision-making, harming customer relationships, delegitimizing marketing campaigns and more. By addressing flaws and inconsistencies in your business’s data, you increase your ability to analyze information and make informed business decisions. Data quality issues can also lead to stressful situations. “Our critical real-time e-commerce site crashed last night! What happened?!” Well, upon further analysis, it seems that the website was expecting alphabetic characters in the comment field, which unfortunately had an unreadable “TAB” character in it that caused a cascading system failure. And even though it’s boring for the average person to think about “TAB” characters being significant enough to crash a website, these are the types of issues that data quality people get a kick out of, and that business leaders and senior managers should be terrified are going to impact their businesses. At this point, we’ve determined that a minor data quality issue has the capacity to take down a business, at least for a period of time. Now the question is, “how do these data quality issues occur in the first place?” Let’s take a look at some typical data quality problems. Why do I have data quality issues? A dataset can develop a large variety of issues over time. Unfortunately, poor quality data is somewhat inevitable to a large extent. A significant portion of issues that affect data quality occur during the data collection and entry process. This can be due to problems with your data collection system or the person entering the data. Other issues can develop over time as formatting requirements change or customer information changes, affecting your current database. However, with a data entry and management plan and the right tools, your business can easily address and correct the issues that do arise. Most common data quality issues From mistakes at the time of collection to old, out-of-date information, there are several common issues that can affect the quality of your data. Data quality issues are almost inevitable, but they are preventable, making it all the more important to keep an eye out for these issues and develop systems to address them. When collecting data and maintaining a database for your business, the following are the most common data quality issues that arise. 1. Incomplete data fields During the data entry process, it can be easy to rush through a form, overlook a few questions or simply choose not to answer some. Incomplete data leads to incomplete reports and prevents your business from gaining a complete picture of your customer information and drawing accurate conclusions from the information.Fortunately, this issue is pretty easy to address by using software that allows you to set required fields. With this software, a form cannot be submitted unless all of the data is complete. This issue can also be remedied by adding rules to forms and questions. These rules include excluding special characters, only allowing digits and using fields specifically designed for currency or dates, all depending on the question. These methods provide a great example of taking proactive steps to improve data quality before it even enters the database. 2. Duplicate data Duplicate data is one of the most prevalent data quality issues that affect businesses. For many businesses, duplicate data is unavoidable, especially when they use multiple data collection systems and methods. With a high influx of data from in-person interactions, phone calls and online forms, duplicate data is bound to happen, which makes it important to have a system in place that constantly checks for duplicate data in a database. Duplicate data also often happens when existing customer information changes. For example, it is common for a customer to provide information such as an email address to locate their account. If their email address has changed since the last time they logged in and it is not recognized by the system, then they may end up creating an entirely new account instead of changing the email address on file. To address duplicate data, your business should invest in a tool that cleanses and combines duplicated records. With the high intake of data that your business experiences, this issue is nearly impossible to fix manually and would take an unrealistic amount of time. 3. Inconsistent formatting Dates, addresses, and numbers all lead to formatting issues that can render large amounts of data useless and unhelpful. If the date is entered manually (like a request for date of birth), it can be input in any number of formats: two-digit months and days, one-digit months and days, two-digit years, four-digit years, and a mixture of each, sometimes separated by spaces, or hyphens, or slashes. And what about when someone uses an “O” instead of a zero or an “I” instead of a one? People may even spell out the date in total, like “January 1st, 2017”, which is ripe for misspellings and non-conformity. Numbers are not quite as complicated as dates but still fall into some of the same traps. The most common issue is letters representing numbers (the aforementioned “I” for “1” and “O” for “0” and the occasional heavy-metal data entry person using an “E” for a “3”). But you also have spaces being used in numeric fields, people entering “seven” instead of the digit “7.” Addresses are also affected, as some entries may place the zip code in different areas of the address. Inconsistent formatting affects your ability to run reports, analyze data and effectively compare data entries. With the number of formatting issues that can arise, it is crucial to regularly assess and cleans data. Fortunately, data cleansing tools, like address validation tools, target and correct problems with formatting to allow for consistency and better analysis. 4. Human error People filling out forms is one of the most common causes of data quality issues. It is not necessarily anyone’s fault, as human error is a natural component of the data entry process, but it is a crucial issue. Technology is helpful in reducing the impact of human error, but individuals still play a key role in the process. Common errors include typos and entering information into the wrong field, like putting a name in the address field. Other errors include willingly entering incorrect information in a field to bypass the required fields and submit the form. Although these errors are likely to happen, there are still measures to take to reduce and correct them. This makes training an important element of any data collection plan. If the person in charge of entering data is not entirely comfortable and proficient with your data management system, errors are far more likely. Well-trained data entry personnel will still make mistakes, which is why proper data validation and cleansing technology is helpful in catching and flagging errors that do occur. Having the right tools for the data entry process will help prevent poor quality data from ever entering the database. 5. Different languages and units of measurement Globalization has largely affected how we treat and work with data. It requires a more careful entry process. For businesses with customers and data entry specialists in multiple countries, the potential for entering a different language or measurement unit raises greatly, making it crucial that each system has clearly defined measurement units and a way to flag potential errors. A lack of attention to detail can particularly affect inventory ordering. A mistake in units can lead to a disastrous situation of not enough or too much of an ingredient or product. Altogether, businesses must set consistent data quality standards that account for weights, lengths, distances and currencies. How to fix data quality issues Data quality issues are guaranteed to arise at some point, especially when your business frequently gathers new data about customers or maintains a database for an extended period of time. Fortunately, there are plenty of resources to assist you with your data collection and management. Whether you are looking to avoid errors during the data entry process or cleanse data from already existing lists, Experian Data Quality can help. To learn more about how to resolve your data quality issues, contact Experian Data Quality today. We have a variety of tools, from our phone verification tools to our address validation tools, to help your business target and correct poor-quality data. Try our tools today to instantly improve your data collection and management strategies so that you can avoid data quality issues and advance decision-making on important business practices.

Data is one of the most valuable assets a business has. Organizations rely on accurate, trustworthy information to understand customers, drive operational efficiency, support compliance efforts, and make informed decisions. When data is incomplete, outdated, duplicated, or inaccurate, it can create obstacles across the business—from missed customer opportunities to inefficient processes and flawed decision-making. By understanding the causes and consequences of poor data quality, organizations can take proactive steps to strengthen their data foundations and support long-term growth. What does it mean to have poor data quality? Data may be defined differently across organizations and industries. One source defines bad data first, saying, “We define bad data as those acquired through erroneous or sufficiently low-quality collection methods, study designs, or sampling techniques, such that their use to address a particular scientific question is scientifically unjustifiable”. Examples of poor data quality in business include outdated customer contact information, improperly formatted address data, and customer data with typos. The impact of poor data quality extends to all departments within a business. For example, if you have bad customer data such as duplicate records or inaccurate records, it can affect the finance team for billing purposes, the renewals team for identifying who their customers are, and operations for processing and reporting on accurate products that the business has sold into. What causes poor data quality? Unfortunately, no business is immune from poor data quality. In fact, without a consistent data strategy, it is virtually inevitable. By knowing what to look for, you can target the precursors for poor data quality before they become deeper issues. 1. Inconsistent data collection methods If your business has not spent ample time ensuring that the inputs coming into your CRM or invoicing system are uniform, then you risk inaccurate information through these non-uniformed inputs. Establishing standard processes across all data-entry points is a great place to start; this will ensure that the data coming in is trusted, consistently formatted, and accurate, saving time and resources for your employees. 2. Ineffective data management The lack of best practices and policies could negatively impact your data consumption and management. If there are various ways to define, view, and manage data across your business—with no standard in place—you could be creating a roadblock when it comes to sharing insights across departments and overall decision-making. When it comes time for processes like data migration and integration, these standards only become foggier, making data less reliable with inconsistent formatting and quality. Your team should have a clear understanding of what data management looks like and how to work with data to meet business standards and goals. Sixty-two percent of businesses believe that a lack of basic data literacy skills impacts the value they get from their investment and technology, according to our latest study. After you standardize your records, we highly recommend that your business is trained across all departments on how to read, write, and argue with data. 3. Outdated data Understanding how to use data does not matter if the data is no longer useful. Data should be refreshed and viewed on a regular cadence to aptly take actions or make pivots as a business as needed. If your business is only collecting data and not reviewing it, then emails and addresses are becoming outdated and useless, leading to missed communication with customers and flawed decision-making down the line. Consequences of poor quality data Collecting data is not just for your convenience. Data collection should have a direct impact on business decisions and facilitate customer interaction. As a result, data quality issues can have debilitating effects, like the following consequences. Reduced efficiency Poor data quality can negatively impact the timeliness of your data consumption and decision-making. In fact, poor data quality may cost the US economy as much as $3 trillion in GDP. The best way to leverage data in a timely manner is to utilize tools alongside your process to create efficiencies with your time and resources, which will allow the expansion of timely strategy and tactics throughout your fiscal year. Without them, you are wasting time and energy that could be spent making decisions that support business growth on managing data quality issues and correcting avoidable mistakes. Missed opportunities Data quality issues affect communication between your business and the customer. If you do not have accurate contact information for your customers, then you cannot reach them to facilitate conversions. Poor data quality also causes you to miss opportunities to gain customer trust. Improper data leads to inefficiency in customer service interactions, impersonal emails, and ultimately unsatisfied customers. Reduced revenue Poor data quality can lead directly to flawed analysis and lost revenue, which is not uncommon. For example, marketing campaigns or analysis based on faulty data means not reaching potential customers and missing out on conversions. If you are investing money in failed mailing campaigns, you are also directly losing revenue. Not to mention, ineffective data management processes can also lead to a failure to follow important regulatory requirements and result in direct fines. How to improve your data quality The benefits of good-quality data can be felt across all departments. Standardized data, processes, and tools give your people the confidence and trust in the data and insight they need to make the best timely decisions for the business. To improve your data quality, you need to invest in and maintain systems that support these procedures. Follow consistent collection methods The worst way to maintain high-quality data is to start off with poor-quality data. Therefore, you need systems in place at the point of collection to prevent faulty data from ever entering your database to begin with. Resources like real-time email verification ensure that customer data is valid and accurate as soon as you receive it, preventing you from having to correct mistakes down the line. Use effective data quality tools Many tools on the market can help establish efficient processes to ensure high-quality data is captured and managed. Data validation and data management solutions are two options to ensure that both sides of the coin, data collection and ongoing maintenance, are reflected within your business. Whether you are looking for data migration, data cleansing and standardization, or enrichment with additional consumer attributes, Experian’s data management solutions could provide the tools you need to maintain high data quality and reach your goals. Take charge of your data quality with Experian The impact of poor data quality can be seen throughout multiple daily business operations, from unsatisfied customers to poor decision-making. In a data-driven world where returns matter, these are risks your business cannot afford to take. Fortunately, with an entire suite of data quality management tools, Experian can help you determine which ones can support your individualized business needs. For more information on Experian’s data management solutions, contact a consultant today!

We’re finding great value in AI as are other businesses. Here is how Experian Data Quality is using AI in our product suite today, so you can continue to innovate and automate your business. AI is a term that’s grown in importance since generative AI solutions like ChatGPT, Bard, and others, became available earlier this year. Their success at demonstrating the capabilities of large language models to understand written and spoken prompts, their ability to search and summarize volumes of content, and their proficiency at providing well written, holistic answers as well as a range of novel and predictive capabilities, has brought widespread attention. So, it’s natural to ask, “how will artificial intelligence impact the field of data quality?” What’s the difference between AI, generative-AI, and machine learning (ML)? First some definition of terms. I asked (Google’s AI chat bot) Bard to answer this question. This is what Bard said: These three terms are all related to the field of computer science, but they have distinct meanings: Artificial Intelligence (AI): A broad term encompassing all efforts to create intelligent machines capable of performing tasks typically requiring human intelligence. Focus: Replicating human-like intelligence and behavior in machines. Examples: Robotics, natural language processing, computer vision, decision-making systems. Generative AI (Gen-AI): A subset of AI Focus: Creating AI systems that can generate new content, such as text, images, music, or code. Methods: Often uses machine learning techniques, especially deep learning, and large language models (LLMs). Examples: Text generation tools, image editors, music composers, code generators. Machine Learning (ML): A subset of AI Refers to the techniques used to enable machines to learn from data and improve their performance on a specific task without being explicitly programmed.Methods: Algorithms that identify patterns and trends in data, then make predictions or decisions based on those patterns.Examples: Image recognition, spam filtering, recommendation systems, self-driving cars.” Here’s a useful example: AI: Imagine a solution that can evaluate a dataset and fix any data quality issues for entered values without human intervention. Gen-AI: Imagine a solution that can analyze the data and predict values for entries that are missing or incomplete. Machine Learning: Imagine a solution that analyzes the outputs from the above processes, accepts input as to the validity or otherwise of the choices made and learns how to improve on them to make the data ever more accurate and complete over time. That’s the kind of prospect that AI holds for data quality tools in the future. And apart from data quality tools, it’s also very important that organizations create a foundation of quality data to inform, train, and feed the wide range of AI tools that will likely be used across their own businesses going forward. How does Experian Data Quality use AI today? Our AI-powered data quality platform, Aperture Data Studio, automates and operationalizes data quality for businesses. Experian’s Aperture Data Studio (also known as Data Studio) solution uses AI to provide a self-service data quality and enrichment platform that enables organizations to efficiently manage data quality and create an accurate, trusted, and holistic view of their information. This AI-powered platform provides a range of features such as data profiling, data cleansing, data matching, data enrichment, and data monitoring. The platform also offers real-time data validation and address verification. Over the last few years, we have put a great deal of automation into Data Studio and have received positive feedback from the analyst community indicating that Experian has some of the most advanced uses of automation on the market. Leveraging AI and ML, automation is being built into Data Studio in nearly every area: data onboarding, data discovery, issue discovery and resolution, rule creation, matching, and data observability. This automation makes Data Studio far easier to use and helps our clients reach value faster with fewer resources. Take rule creation, for example. Data analysts need to discover, document, execute, and maintain complex sets of rules across different datasets and domains to be able to keep their data fit for purpose. Data Studio incorporates machine learning algorithms for automatic data tagging that support the easy discovery and deployment of such rules, enabling them to be stored, shared, and executed, all via a business-friendly interface. Automation is also present in Data Studio’s smart profiling capability, allowing users to automatically find data issues and receive suggestions on how to resolve inaccuracies. Leveraging auto-tagging and smart profiling, the Suggest Transformation option analyzes values in the data and recommends functions to improve data consistency, clearly explaining what each transformation will do to the data to preserve data integrity. Examples are Trim and Compact which remove unnecessary space characters or convert null to zero for numeric columns containing both. Also Hash, which obfuscates sensitive data so that it can be safely saved and shared. Once accepted, transformations are easily deployed in just a couple of clicks. Other areas where machine-learning is used within Data Studio include: Powerful outlier analysis to proactively detect and inform users of unknown and known anomalies within the data. Observability features provide automatic data monitoring to detect interesting or unexpected changes to the data. Tuned matching rules for optimized accuracy when comparing records from different sources. Smarter merge suggestions when configuring how best to deduplicate records with duplicated data. The Aperture Data Studio Roadmap indicates that further investment in AI is already under investigation. The goal is to determine how can Gen-AI natural language processing (NLP) models be used to increase user efficiency and improve collaboration through personalized experiences and AI-driven intelligent suggestions. A robust data governance and data quality strategy is the prerequisite to AI business success The early adopters of AI, ML, and Gen-AI were primarily organizations with robust data and analytics strategies. Now, as the hype continues, more organizations without that foundation are keen to take advantage of the new innovations. Many analysts advise them that they can’t get started without building a strong data strategy. One big challenge is the breadth of information used to inform publicly available Gen-AI solutions. For example, today’s open GPT-based solutions such as Bard, Bing365, and OpenAI are trained on a broad spectrum of internet and social media data. Any frequent user will know that this often results in “hallucinations” where, to collaborate and simply provide an answer, the solution will misinterpret the data and present a totally incorrect result as the truth. Without human intervention and understanding, such “hallucinations” can cause significant misdirection and even harm. The answer for businesses interested in using Gen-AI in their own products and decision-making is to narrow the input data to information that is relevant to the purpose and to make sure that the data is as accurate as possible. Without accuracy, the models can still produce hallucinations. Without trust, the resultant decisions will not be acted upon or acted upon slowly, after the wisdom of the decision has been thoroughly vetted. The latter course, eliminating much of the business value assumed for the AI solution. Success is going to require a strong blend of data quality, data governance, and data security. Data quality ensures that the “training” data is accurate, complete, and comprehensive. Data governance manages the data quality and accessibility, determines ownership, and carefully catalogs and defines the information available so that decisions can be made about the best data to use. Information security will be needed to protect the data from being shared inappropriately or being purposely corrupted to impact competitiveness or reputation. A key example where governance, quality, and security could make an impact is in the call center. One use of Gen-AI is in call center applications where bots use customer data to efficiently respond to personalized customer questions. The benefits of improved customer satisfaction and efficiency should be significant, but if the customer data gets corrupted or is simply wrong, the opposite effects will likely occur. That is, poor satisfaction and less efficiency as the firm tries to do damage control. The challenge for many firms is that the traditional, top-down approach to data governance is too expensive and unwieldy. It can take years for a business to mature enough to adopt a data governance program—and data quality often takes a backseat due to lack of ownership. Now, more agile firms are taking a bottom-up approach and seeing success. Agile firms taking a bottom-up approach are building their data governance and quality programs one step and one issue at a time. Perhaps, leaders bring governance practices to the data analytics department first then expand involvement to other departments as issues arise and are solved. Over time, those with a vested interest in solving their department’s problems will become involved and take ownership, broadening the organic adoption of governance and quality across the business. Aperture Data Studio and data governance Experian has partnered with leading data governance vendors, such as Alation, to provide bi-directional interfaces to applications. Such integrated interfaces allow the governance solutions to take advantage of profiling, monitoring, and other data quality capabilities while providing Data Studio access to a wide range of metadata to increase its operational effectiveness. The net result for Experian and Alation joint customers is a far more robust data quality and data governance capability. Experian continues to invest in data governance for Aperture Data Studio customers by expanding partnerships and integrations with companies like IntoZetta, who is a UK-based software company that specializes in data governance, quality, and migrations for specific industry sectors. By further participating in the data governance market, Experian is focused on providing our customers with a well-rounded tool set that helps businesses innovate their use of artificial intelligence with a strong foundation of data quality, governance, and security.