5 most common data quality issues (and how to solve them)

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From mistakes at the time of collection to old, out-of-date information, there are several common issues that can affect the quality of your data.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.