What Is a Data Quality Audit? Ensuring Your Data Integrity

Companies maintain huge volumes of data. This data may contain codes, test data, and financial information as well as a customer database. However, maintaining this data can be costly. This is because for storing huge data you have to get the services of a data center. The worst case being you may have many pieces of unused data like former customer information, for example. You may also have archived audit reports, which are no longer of any use. But how do you check what kind of data is useful and what isn’t? The answer is by carrying out a data quality audit.

In this post, we’ll discuss what a data quality audit is. We’ll also explore the process and importance of a data quality audit. Additionally, we’ll learn about the tools you can use for this job and how the process will ensure your data integrity. So, let’s get started.

What Is a Data Quality Audit?

Before discussing what a data quality audit is, we should know what auditing is. Generally, auditing refers to inspecting an item or a process. The aim is to find out whether the properties of that item fulfill the required criteria of the customer. For instance, suppose you’re executing a data center security audit. During the audit, the team checks if the security protocols of the data center match the processes written in the contract. Let’s move on to data quality audits.

As I said earlier, companies have a huge volume of data. Before you use any such data, auditing is a must-have to ensure that the data set fulfills your goal. In layman’s terms, a data quality audit involves checking some key metrics. The goal is to find out whether the data set has the required quality. Once the audit team gives the green light, only then can you use the data for your task. All clear with the definition? Let’s move on to how a data quality audit works.

Process Behind a Data Quality Audit

While auditing data, the audit team reviews the format and process of data creation. Thus, they can check the data’s utility and find out its value. Not only that, auditing can conclude whether the data is consistent. Also, auditing checks whether the data will be able to maintain its integrity throughout its life cycle. Let’s discuss in detail how a data quality audit works.

Are you auditing the data in-house? Or hiring a company to carry out the audit? You must know these six mandatory steps that auditors follow.

Find the Data

Until and unless you identify the data, you can’t make the data listen to your commands. All kinds of apps, servers, software, and programs can have data assets. Your first task is to make a list of the different data types. The list may contain categories like the following:

  • Shopping cart data
  • A server having customer information in XML
  • Marketing contacts in email
  • Followers on social media

Also, if you have vendors working for you, consider the data that they use as well. In short, you have to consider any data used by anyone working for your company.

Find the Data’s Source

All clear on what kinds of data you have? What if you missed something? Let’s find out the data’s source and mode of access.

Suppose your team is using Salesforce to store customer information. Even if the team members are digitally entering data, you would still have quite a lot of unorganized data—the worst case being some pieces of data that you missed in the previous step. To find out that missing data, look in the following places:

  • Check with your sales team. Find out if they have the customer’s information in their Twitter or Facebook account or in their personal mail.
  • Did you just upgrade to Outlook from your old email? You might have some customer information that you missed while migrating from the old email program.
  • There may be some customers who purchased products from you but don’t have their information in your Salesforce database.

So, the aim behind this is to find out where you store the data. Once it’s done, you can decide where you ideally should store the data.

Set Up the Base Rules

Got all your data-related information and storage locations? It’s time to set up some rules. How do you do that? Let’s look at an example. Let’s suppose you have a folder that only has information of customers belonging to a specific geolocation. Check if all the data tables in that folder have the same type of information (i.e., customers belonging to the location defined in your base rules). Once you compare the data according to your customized rules, the next step will be much easier.

Organize Data Based on Priority

Now, it’s time to sort data based on priority. The sorting rules depend on the company type. For instance, if your company is web-based, email IDs of customers are of more value than physical addresses. Another example is, if your company focuses on direct marketing, physical addresses are of more value. This is because your salesperson has to visit the customer’s office for marketing purposes. Once data sorting is done, we’ll move on to examining data and judging its quality.

Audit the Data’s Quality

There are two predefined rules that are applied during a data quality audit.


The DM SME, or data management subject matter expert, rules check and note the known rules that can impact a data set. Once the rules are documented, the metadata team reviews the rules. They check how many of these rules they can implement while auditing your data. The data quality team then checks your data reports. After that, they find out if your data’s quality matches the quality stated in the predefined rules.


The UC SME, or user community subject matter expert, adds some additional rules that the team thinks are important to check your data’s quality. Once the audit team gets input from the user group, they enter a few more rules and recalculate the confidence report.

You can customize the rules that come under UC SME. For instance, you can check if a database table has duplicate primary keys or if the table has incorrect date time format. The audit team applies customized as well as predefined rules to check the data quality.

Finally, the audit team generates another round of reports. The data quality team reviews the final report based on both UC and DM SME rules.

Why Is the Data Quality Audit Important to Ensure Data Integrity?

What is data integrity? It’s a metric that checks if your data is accurate and consistent. This metric also checks if your data is safe and secure and complies with data privacy regulation policies. But how does a data quality audit ensure data integrity?

In the previous section, we discussed the DM SME and UC SME rules that the audit team implement during data quality audits. The audit reports find out silos and issues that make your data unsafe and irrelevant. Besides, the reports also point out the areas of improvement. Suppose you’re not using the latest encryption technique. Or, what if you’re not aware of the latest GDPR rules? The audit reports find that out and allow you to correct your data set and storage rules—thus improving the overall data quality and ensuring data integrity.

Tools Used for a Data Quality Audit

Companies nowadays have huge volumes of data. If your goal is to improve the data quality, manually analyzing the data is next to impossible. The only solution is to deploy a tool that can check the data and analyze its quality. Let’s discuss what to look for when choosing a data quality audit tool.

How to Judge a Good Data Quality Audit Tool

So, we know why companies need a data quality audit tool. Now, let’s discuss how to judge whether a tool is useful for your company. Apart from finding issues related to data quality, a data quality audit tool must help in the following:

  • Analyze the company’s data
  • Access data management and reporting
  • Help in checking the system’s ability to report and collect data
  • Verify data frequently and evaluate data quality
  • Check whether proper data quality management systems and tools are applied in the system

Before choosing a data quality audit tool, check if the tool is able to perform the above-mentioned tasks.

Popular Data Quality Audit Tools

Now, let’s discuss some popular data auditing tools.

If your goal is to audit the database, IBM’s db2audit tool is quite useful. This tool generates audit logs whenever you perform a database operation.

MySQL Enterprise Audit is also quite popular. This is because the tool helps in policy-based auditing by providing a user-friendly interface.

Another popular database audit tool is Oracle Database 12c. The product is a combination of the Database Firewall and Audit Vault. So, how does the tool work? It provides a precise logging configuration that captures the reported audit data in a reinforced manner.

Let’s Wrap It Up

Ultimately, it’s important to carry out data quality audits in a timely manner. This will help you identify the painful areas of your database, thereby helping you in managing your company’s data. By using a data quality audit tool, you can resolve the weak zones of your data. Also, you’ll be able to get rid of redundant or archived data that you cannot reuse. Finally, you’ll soon find out that maintaining your business and making plans to boost it get much easier.

This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.