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Do you know the steps to take for dealing with data duplicates? What about the deduping minefields to avoid? Follow these five steps for disaster-free duplicate identification. To learn more about data duplicates, the dangers of deduping, and custom data quality management solutions, reach out to pam.lang@xcelerated.com , or call (877) 236-9155.

The “people, process, and technology” (PPT) framework helps businesses identify the necessary components for successful change management. Data has long been lumped in with technology, but that’s changing as more companies recognize the fundamental nature of data and the role it plays in their success. Data is finally getting its due. Is your company’s data ready for the spotlight?

Your new marketing campaign just fell short, despite spending many hours designing, researching and planning. The client is unhappy, even though it was their data. This happens all the time, why? ​ Not having a clear understanding of data processing will destroy your marketing results. For a quick and easy turn around, Dirty Data Cleanup™ is our standard automated process for a "one-size-fits-most" data quality solution.

Disparate data quality management processes contribute to confusion and in some cases, complete chaos. A single vendor can leverage data quality expertise to manage your business data in a consolidated, end-to-end process that accounts for its source, purpose, and any potential problems. A single source data quality solution increases the accuracy and utility of your company’s data.

How do you know you need help with your data? What if you have a unique or special data project? Would you know where to go? Aaron Sevigny, President and CEO of Acadia Wealth Management, shares his Xcelerated Data experience.

What’s the most dangerous word in data quality management? Deduping. Before you start dumping records, consider what data duplicate records can provide, and whether you’re deleting essential information. To learn more about data duplicates, the dangers of deduping, and custom data quality management solutions, reach out to pam.lang@xcelerated.com , or call (877) 236-9155.

The terms “data hygiene” and “data quality” are both well-known and oft used in Big Data circles, but how interchangeable are they? What is the difference between hygiene and quality as applied to stored information? Understanding the language of data maintenance is essential for ensuring your company’s most valuable asset — your business data — is accurate, reliable, and complete. Data hygiene Data hygiene refers to the disparate steps your company employs to keep data accurate and up to date. This is typically accomplished through a collection of rigid processes for standardizing data sets, removing unnecessary or outdated data, and organizing it for use throughout your business. While data hygiene can help to optimize productivity and informed decision-making, it can easily go wrong — with disastrous results up to and including the loss of critical data. Data hygiene processes must be done with careful attention to best practices. Despite the potential risks, data hygiene can be helpful for avoiding redundancy, improving data accuracy, and updating data sets. But it’s only a subset of data quality management for accurate — and useful — data. Data hygiene is a good start, but where do you go from there? Data quality solutions Gartner defines data quality solutions as “the processes and technologies for identifying, understanding and correcting flaws in data that support effective data and analytics governance across operational business processes and decision making.” Data quality solutions are complete, end-to-end, customized processes for solving a variety of data problems in one go. Ensuring data quality is more comprehensive than simply cleaning up redundant data and keeping what you need updated. Data quality solutions encompass the full range of data management processes in one neat package from one vendor partner. Instead of using different, laborious processes for solving data problems, these solutions are customized to fix quality issues and ensure ongoing data accuracy and consistency. Custom solutions are based on data sources, industry priorities, and individual company goals, and their interconnectivity allows you to unify information across different teams and divisions of your business to ensure decision-makers at every level are working from the same — accurate and reliable — data. Data quality solutions simplify day-to-day processing and the business operations that incorporate data. Why it matters Data hygiene is a subset of data quality, but pursuing it without considering the potential consequences of disparate processes invites problems. Disparate processes can miss data issues — and even create a few new ones. Redundant processes waste time and money and frequently fall short of your intended goals — not to mention the problems that can arise from a lack of proper management. Data quality solutions cover more than data hygiene. They are complete solutions to your company’s data challenges, and they continue to safeguard your data against new issues that arise over time. This comprehensive approach to data management improves your valuable data and establishes a custom framework for ongoing, consistent, and efficient processing. In short, data hygiene processes are only a part of improving your data. They are useful on their own when managed carefully, but data quality solutions improve data — quickly and efficiently — for better business decision-making. For a comprehensive answer to your company’s data challenges, data quality solutions beat standalone data hygiene techniques every time. Contact the data experts at Xcelerated Data today to talk about data quality solutions for your business, or sign up to take the database challenge for free. We will find problems in your data you didn't even know existed.

Every decision you make relies on some form of data, and the appropriate level of data quality is fundamental to sound business decisions. What can you do to ensure the accuracy of your company’s data in support of well-informed business decisions? Begin with a basic understanding of the significance of data quality. The importance of data quality Data is essential to every business in every industry. Data drives your most expensive activities and your most critical business decisions – from launching a new project to altering your business model. Quality data increases the accuracy of your predictions to allow for better market forecasting and budget planning — and a more positive customer experience. What impact does low-quality data have on your company’s: Reputation? Employee Satisfaction? Project Success? Financial Results? Decision-making, company reputation, and your customers’ experience can all suffer from poor data quality. Measuring data quality Data quality has several metrics by which it can be measured and maintained. These include: Consistency. Are your data sets consistent with one another? Is all your data current, measured objectively, coherent, and reasonable? Imagine losing a $125 million dollar space probe because of something as basic as feet versus meters. If it can happen at NASA, it can happen to you. Accuracy. Is your data correct and representative of the object or reference information? For example, what happens to the reputation of a charity that sends five identical mail pieces to a long-time donor? Inaccurate data can cause anything from a minor inconvenience to a catastrophic failure depending on the context, so ensuring data accuracy is critical. Completeness. Are all your records complete across data sets? Are any records missing relevant information, such as a previous surname or current contact information? While there are other important metrics to consider, these three basics are easy to check in an initial examination of data quality. Problems in any one of these areas can have a significant impact on your business. Improving data quality According to a poll of data professionals , 75% of respondents named data quality as one of their top priorities. So, the next question is: How do you improve your company’s data quality? Recognizing the existence of a problem is the first — and often most important — step to data quality improvement, but without understanding the underlying causes and implementing action to correct the problem, the potential consequences of low-quality data remain. There are specific steps you can take to manage your data quality. Establish data governance. Develop an understanding of your data and how it is used, establish rules for managing how data is collected and/or input, and implement formal, regular data quality maintenance procedures. Perform frequent data checks. Changes in names, addresses, titles, positions, management requirements, or local, state, or federal regulations can all lead to outdated, inaccurate, and incomplete data. Using external validation processes and running appropriate reports, for example, will allow you to catch issues before they grow into serious problems. Incorporate a total data quality management solution. Complex data quality issues require more than a few tweaks. Frequent or persistent problems require a complete solution to resolve current issues and ensure data quality going forward. Implement continuous improvement strategies. It’s unlikely you’ll have data that never changes. With fluctuations in customer information, market trends, and other changes associated with running a business, maintaining data quality is a constant process. When your company’s data quality is low, it impedes your ability to make sound business decisions. Maintaining quality data requires consistent, ongoing, and well-defined processes — a total data quality management solution. Data drives your most expensive activities and your most critical business decisions. Learn more about custom and complete data quality management solutions for your business at xcelerated.com .

Data quality is a business priority. Every day your business gathers data, including confidential and sensitive information regarding customers, target markets, internal finances, and more. With continually evolving privacy and compliance regulations, such as CCPA (California Consumer Privacy Act), CPRA (California Privacy Rights Act) and GDPR (General Data Protection Regulation), maintaining regulatory standards is necessary for avoiding fines, but it also helps you protect your business and retain your customer trust. Is your business prepared to prevent potentially devastating complications that could wipe out significant revenue? Low-quality data is a disaster waiting to happen, and the best time to determine your plan is before disaster strikes. Consequences of low-quality data The consequences of low-quality data include: Diminished reputation. Fines for inaccurate or late reporting to regulatory bodies. Inferior decision-making. Loss of revenue. And that’s not all. It costs employee time to investigate, identify, and correct problems, and in the worst cases, even equipment and inventory can be casualties. Regulatory compliance The most obvious consequence of noncompliance is the regulatory fines your company can incur. Data quality is required to meet standards for privacy, security, accuracy, reporting, and shareability. Did you know, for example, that GDPR requires businesses to correct inaccurate or incomplete personal data? But there are benefits to complying with data quality rules and regulations besides avoiding fines. Adhering to regulations helps you ensure overall data quality, which leads to higher efficiency, a streamlined customer experience, and increased revenue over time. Following regulations establishes the minimum baseline for your data quality. From there, you can improve upon your data quality until it reflects your desired management criteria. Getting ahead of regulatory compliance, rather than letting it catch you off guard, can be the difference between floundering and flourishing. Data quality management criteria Analyzing your data quality needs, and the risks associated with neglecting them, allows you to create the best management criteria for maintaining and optimizing your company’s data quality. General management criteria include: Accuracy. One of the primary factors for data quality management is data accuracy. Data that doesn’t accurately reflect its source is useless at best and catastrophic at worst. Inaccurate data takes time and effort to track and correct. So, protocols for regularly updating data and verifying its accuracy are an essential piece of your management criteria puzzle. Consistency. When gathering data, it should be internally consistent regarding what is recorded and how. Standardize forms, field rules, and use consistent formats and formulas for dates, measurements, and other information. You should also ensure data is consistent with reality. If a record shows an employee’s birth year occurring after the year they were hired, for instance, it is inconsistent with reality. Duplication. D uplicate records occur when data is collected from multiple sources. Duplicates get a bad rap, but it’s important to identify and evaluate them carefully to determine and optimize their value. Completeness. Empty fields in data sets and/or missing data sets are examples of completion issues that should be addressed in your company’s data quality management criteria. Formal, standardized data quality management is necessary for regulatory compliance regarding data privacy and security. Data quality management solution Unaddressed data quality issues can quickly get expensive, whether in regulatory fines, inefficient business activity, poor business decisions, or all of the above. Data management criteria and compliance regulations head off potential data issues in your business. But a total data quality management solution — designed to meet the unique needs of your company — organizes and optimizes your data to correct existing quality issues and avoid future problems. Learn more about total data quality management solutions for your business at xcelerated.com .

Third-party cookies have long been the go-to resource for consumer data collection, particularly for targeted marketing and advertising campaigns. But privacy and cybersecurity concerns, along with increasing governmental regulation, have major internet and technology companies phasing out third-party cookies. It’s time to adapt, and first-party data is the way of the future. Who’s taking your cookies? For the uninitiated, third-party cookies are data tracking codes created and managed by an outside service to collect data for your company’s benefit. Cookies collect user data across websites, pages, and devices, primarily for the purpose of targeted advertising. Here’s how it works. When you own a business, you want to be sure your ads reach the right people. If your company rents vacation homes, you subscribe to an ad network to target consumers researching travel, vacations, and private getaways. If, on the other hand, you own an obedience training school, you would subscribe to an ad network for consumers researching dog breeds, crate training, and the best dog food for puppies. Using third-party cookies to collect data was, until recently, the smartest choice for gathering information on potential customers. The data is more reliable, and it allows for personalized, targeted ads, which generate more revenue than broad, general advertising campaigns. But as internet users grow increasingly privacy-conscious, it’s more common for people to clear their cookies or use tracking blockers, and many have complained about cookies as an invasion of their privacy. These complaints aren’t unwarranted. Cookies track a user’s internet activity across many sites — no matter how unrelated they may be to the original website. Others worry about data breaches and other possible security risks. As a result of the ongoing outcry, major web browsers are phasing out the use of third-party cookies in favor of less controversial first-party data collection. What is first-party data? First-party data is collected directly by the company that intends to use it. Rather than operating through an outside service, first-party data is collected from websites and other sources owned by the company. This means the data belongs to the company, as opposed to a third-party service. First-party data doesn’t present the same privacy concerns as third-party cookies because it’s collected with the consent of the individual to which it pertains through sources directly connected to your company. This can be data requested via the company website in exchange for discounts or additional content, or it might be gathered via consumer surveys and other methods of market research. Your company collects the data for its own use according to a stated privacy policy. Consumers have less worry that confidential data from one site will be viewed and collected by every other web page they’ve visited since they last cleared their cookies. A world without cookies Ready or not, third-party cookies are going away. As you prepare for a world without cookies, enhance your first-party data collection approach with these essential steps: Provide the compelling products, services, and content consumers want. You can no longer rely on completely customized content because the data you collect will not be as specific and comprehensive. Use the data you can collect to create products, services, and content to appeal to a wider audience. Establish systems to collect data on company channels. This can mean first-party data, email lists, or even Google’s Privacy Sandbox. Research data collection tools and channel options to find those that work best for your company’s circumstances and marketing strategy. Prepare for contextual advertising. Now that specific, customized ads are less of an option, consider setting up advertisements tied to website content that adds value for your target market. Because these ads relate to content the consumer opts in to view, they’re almost guaranteed to reach an interested audience. Camping equipment companies can’t target customers researching sleeping bags anymore, so their future efforts should focus on websites promoting the joys of outdoor living. Set up personalized email newsletters. Reach out to the consumer. Email newsletters aren’t just an efficient way to share company information. They can also be used to collect consumer data. Send out surveys and questionnaires to get consumers interested in sharing their opinions. People value their privacy and the security of their personal information. Third-party cookies are increasingly perceived as a threat to information security. Phase out targeted advertising efforts that rely on third-party data, and turn your attention to first-party data collection and contextual advertising. By preparing in advance, your company can avoid the fallout when cookies are gone for good. For more on first-party data collection and total data quality management solutions for your company, visit xcelerated.com .

Bad data can have disastrous — even fatal — consequences, but some people still struggle to understand the potential gravity of data quality problems. The longer a business survives without a data quality disaster, the more complacent they tend to become with assessing and managing their data. Check and recheck your math — then check it again Something as simple as a misplaced decimal point can cost you thousands, millions, or even billions of dollars. Just look at the $2.7 billion Spanish S-80 submarine program . One misplaced decimal point led to submarines 70 tons heavier than planned, and engineers feared they would be incapable of resurfacing once submerged. Spain had to spend $14 million to have their weight reduced. Misplaced decimals are bad enough, but what about calculating with the wrong units? In 2013, the Amsterdam city council used software to calculate housing benefits, and accidentally used cents instead of euros. The mistake resulted in the distribution of 188 million euros in benefits rather than the expected 1.8 million euros, and the city council was forced to ask for the money back. While most of it was returned, many already struggling citizens ended up in extreme debt because of the error. NASA isn’t immune to unit errors, either. In 1999, a $125 million Mars orbiter was lost because NASA used the metric system, and the engineering team at Lockheed Martin used the imperial system. Finally, something as simple as a conversion error can be life-threatening. While converting between metric and imperial systems is routine, a small error in the conversion process forced the crash landing of an Air Canada flight in 1983. The math error left the plane with only half the fuel it needed to complete the flight. The plane crash-landed on a racetrack, and its nose was destroyed, but thankfully, no one was killed. Of the 69 people aboard, two suffered minor injuries. Outdated data Making business decisions based on consumer data is a good idea. Relying on outdated consumer data? Not so much. Take, for example, the spectacular disaster that was Crystal Pepsi . At the time of its development, PepsiCo was chasing a health food craze, and artificial colors were increasingly unpopular with people pursuing healthier diets. So, Pepsi created a clear soda. Unfortunately, new data indicated the taste of the drink was too far from that of the original formula, and consumers wouldn’t like it. The company chose to ignore the new information, and Crystal Pepsi flopped. Even game shows have issues with outdated data. Just last year, Jeopardy! relayed inaccurate data about a medical condition known as postural orthostatic tachycardia syndrome (POTS). The prompt used an outdated, offensive nickname for the condition — Grinch syndrome — based on a disproven theory that the hearts of POTS sufferers are smaller than average. The show was forced to make a public apology. Human error Humans are imperfect creatures. That’s why we check, double-check, and have someone else triple-check our work on important projects. Sometimes even that isn’t enough — especially if pride gets in the way. Human error is one of the most common causes of data quality disasters. Some examples include: Challenger . While the dangers of O-ring failure in the space shuttle were known, they weren’t considered a serious issue. Prelaunch safety checks missed that the ring had cracked due to cold temperatures. We don’t need to tell you what happened next. Enron . Enron Corp. was an energy giant until it went out of business in 2001 because of spreadsheet errors. The accidental nature of these “mistakes” remains unclear, but 24% of the company’s spreadsheets contained serious errors. Add untrue claims of profit, and several other ethical issues, and the company didn’t stand a chance. Olympic synchronized swimming. The 2012 London Olympics accidentally sold twice as many tickets as there were seats available for the synchronized swimming event. Why? A typo. Someone entered a “2” instead of a “1,” and the Olympic committee was forced to issue a public apology and convince 10,000 fans to accept tickets to other events. Suffice it to say, human error can lead to serious losses when left unchecked. Data quality is an essential consideration in any field. This is just a small selection of the significant disasters created by data quality issues. From Mars rovers to game shows, bad data leads to loss of revenue, public embarrassment, or even death—proving the necessity for comprehensive data quality management. To learn more about total data quality management solutions for your business, visit xcelerated.com .

In 2021, 75% of companies who made moves to improve the quality of their data exceeded their annual business goals . Data quality is clearly fundamental to the well-being of your business. If your data is wrong, you can’t understand your customers, establish competitive pricing models, or strategize for growth. If you’re ready to improve your company’s data quality, start with a series of clear, measurable goals. Breaking big changes down into smaller steps makes them easier to implement, and data quality management is no exception. If you’re not sure where to start, we’ve got you covered with data quality improvement steps for the next 30 days. Step 1: Identify the problem(s) As with most problem/solution scenarios, the first step to resolving data quality issues is identifying the “right” problem. There are several possible reasons for low-quality data, including ill-managed duplicates, human error, inconsistent or incomplete records, and inefficient data quality management. Once you know what’s wrong, you can begin narrowing your focus to finding the source and strategizing solutions. Step 2: Find the source Once you’ve identified the right problem(s), you need to figure out what’s causing them. This is where the dimensions of data quality come into play. Once you understand these dimensions, you can identify where your problems lie and how they occur. Quality data is: Complete. What percentage of your company’s data is complete? Do you have guidelines in place for qualifying a record as complete? Qualifications for completion typically vary from one business to the next. Unique. What percentage of your business data is unique? Duplicate records can cause problems when they aren’t managed well. Your data should not include unknown or unnecessary redundant records. Timely. When was your data recorded? Old records may be outdated or inaccurate. Company data is typically less accurate and relevant over time. When was the last time your records were updated? Valid. What percentage of your business data is valid? Your data only has value if you can use it to enhance customer interaction and other business activity. Valid data is usable data. Accurate. What percentage of your business data is accurate according to your company’s established rules? Errors contradict the real-world information your data is meant to represent. Consistent. What percentage of your company data is consistent? Are record formats and completion requirements established and regularly met? Is your business data consistent between sets? Review these dimensions of data quality in the context of the problem you’ve identified. A consistency issue can result from a lack of unified understanding regarding what data should be input in what format. Duplicate data may be caused by human error, a bug in your data management system, or both. Whether it’s an issue of policy, mechanics, or simple human error, once you’ve found the source, you can begin to address it. Step 3: Strategize solutions You’ve identified the right problem and verified the source. Now, you must determine the resources necessary to solve it. Do you have the necessary data quality management expertise in house? A small issue with your data collection process (e.g., a repeated field within a record) is simple enough to fix, but a total data quality management overhaul is complex and unique to your organization’s business — and business data — needs. It takes time and technical knowledge, and it’s difficult to conduct business as usual and manage a project of this scope. Some data quality issues can be addressed with policy changes to establish a defined set of rules for recording crucial business data. Automation is also a possibility for verifying data quality and ensuring consistency. Staying on top of potential issues — before they become major problems — also helps avoid a longer, more arduous, or expensive data quality repair process later. Step 4: Expert help Data quality drives your company’s business and future growth, but resolving data quality management issues can be overwhelming when you don’t know where to start. Ignoring problems will only make them bigger and harder to solve down the road. Follow these steps in your next 30 days, and make solving your data quality problems easier with expert assistance. Visit xcelerated.com to learn more about custom total data quality management solutions for your business.