A data audit helps you assess the accuracy and quality of your organization’s data. For many organizations, data is the most valuable asset because it can be deployed in so many ways. Organizations can use their data to improve existing processes or services, make important business decisions, or even predict future revenue. And of course, it’s of great value for the marketing team.
However, when your organization doesn’t adhere to standards or processes related to data accumulation and storage, you might end up with poor-quality data. By regularly conducting a data quality audit, you make sure the quality of your data stays high. Even if the quality decreases at some point, you can take immediate action to fix or improve problematic processes.
This article will help you understand how to get started with a data quality audit. First, let’s discuss the importance of a data quality audit.
Why Do You Need to Perform a Data Quality Audit?
You may want to conduct a data quality audit for many reasons, such as:
- Validate a business case. For example, usage statistics might show that a particular service is popular. Based on this data, your company might decide to invest further funds to expand the functionality of this service. Wise use of data allows a business to make informed decisions. However, in order to make such decisions, you need high-quality data.
- Reduce the number of data faults. Often, data errors pop up after you use a new tool for a while. These errors can include missing fields, incorrect formats, or just incorrect data. Performing a data quality audit can allow you to be an effective gatekeeper of the quality of your data. It’s easy to fall into a data inconsistency spiral where the quality of the data continues to decrease. In the end, you might end up with a useless data set. You might think this won’t happen, but it’s actually very common.
- Comply with data regulations, such as GDPR. A data audit helps you discover unused data or data that you no longer need. Therefore, it makes sense to regularly perform a data audit to identify and remove unused data. In addition, your data quality audit should focus on how your organization stores data. Be sure to properly secure sensitive data, including personally identifying information, to comply with data regulations.
- Discover new data. Let’s say your organization introduced a new process that requires an employee to store personal details about each customer. However, the employee has been recording this data in an Excel sheet. A data quality audit helps you discover such new data sources. Also, you can define rules for how to store this data so that it’s protected and used effectively.
Now that you know why a data quality audit matters, what’s the best way to perform one? Let’s take a look at the steps you’ll need to conduct your data quality audit.
4 Steps to Perform a Data Quality Audit
A data quality audit has four steps. Generally, you start by gathering information about processes and people’s day-by-day operations. With the help of this data, you can start identifying data and prioritize it accordingly. Obviously, you’ll also want to gauge data accuracy and quality. Let’s take a look at those four steps.
#1: Gather Information About Processes.
Most likely, you’ll start gathering information by speaking with managers and leads. These are a good first point of contact to understand the processes in each department and determine what data each group uses.
For example, a marketing lead knows a lot about the data that his or her department handles. These initial conversations help you map out data usage. They also help you identify new processes that generate uncaptured data or improperly stored data.
After talking with managers and team leads, try talking with employees on a deeper level. For example, consult a sales assistant to understand his or her day-to-day operations and find opportunities to capture new data or missing data.
As you learn more about the type of information your company collects, you may want to establish a policy on data governance.
Next, find out where departments store this data.
#2: Where Is Data Stored?
You might find that a particular department tracks customer information in a separate Excel sheet. On top of that, the department doesn’t share this information with other departments. This is valuable information that other departments can benefit from. Therefore, identify how your organization stores data, and merge all data in the proper domains.
For example, store all client-related data in one database containing only customer data. Accordingly, store all customer emails, and link them to customer entities in the customer database. This helps your organization build a strong data profile about its customers—one that all departments can access.
Sometimes, an employee or department might not realize they’re holding valuable data. Let’s say a support engineer talks with customers who complain about a certain product. It can be valuable to track the length of conversations between support engineers and customers. In addition, a support engineer should keep track of the most frequent issues or the number of issues per service call. All this information helps improve the efficiency of the support team.
#3: Identify Valuable Data.
The identification step lets you filter out less important data. For instance, let’s say your organization captures the physical address of each customer. However, your company sells only digital products. As the company won’t send physical letters to its customers, it doesn’t make sense to store physical addresses. To reduce the risk for your company, it makes sense to remove any unnecessary information. It’s best to hold only the information you really need.
For a digital company, an email address is of great value. Therefore, it makes sense to store email addresses in the customer database.
Also, identify valuable data based on business requirements. Data helps you validate business cases. For example, you may want to verify if it’s worth spending extra development time on a newly developed service. To validate this business case, you can track the usage statistics of this service to make a more informed decision.
In short, it’s often useful to prioritize data based on business requirements.
#4: Evaluate the Data’s Accuracy.
This step is the most important one in a data quality audit. It allows you to determine the quality of your data by measuring accuracy and consistency.
These problems define poor quality data:
- Missing data or null fields
- Incorrectly formatted data, such as wrong date notation
- Data generated by bots (for instance, through a contact form on your website)
- Wrong data through human error, such as keying errors, misspellings, or omitted words
Ideally, you shouldn’t find many of the above problems in your dataset. If you have few of these errors, then your data is likely to be of high quality.
But what if your data turns out to be of low quality? Then it’s time to evaluate existing processes.
Analyze your processes, and see where mistakes happen or how you could improve things. For example, you might discover that the name field isn’t required for the contact form. This might be why you have multiple missing fields in your database. The goal is to find problems with existing processes and improve them so you can improve data quality.
In addition, consider investing in a tool that helps you capture and format data. A data management platform is a great solution that helps you with both of these requirements.
Tip: Don’t Track All Data!
Many companies want to track all data. This is a mistake. Why? It goes against the principles of a data quality audit.
Data should either be meaningful for an organization or help with validating a business case, or both. Tracking data just to have the data is meaningless. It doesn’t add value, it eats up the valuable time of your employees, and it exposes your organization to more data risks.
The main goal of a data quality audit is to store only valuable information. Besides, storing large amounts of data isn’t cheap, and it makes your data harder to discover.
In short, you can’t perform a data audit without understanding the existing processes. Therefore, it’s crucial to gather information about them. Once you understand them, you’ll be able to perform a successful and useful data quality audit. Good luck!
This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!