What is a data quality audit and how can it benefit your marketing?

According to one recent study, roughly 2.5 quintillion bytes worth of new data are being generated every single day. Based on that, it shouldn’t come as a surprise that the global big data and analytics markets is currently worth about $274 billion – a trend that shows no signs of slowing down anytime soon.

From the point of view of a business, contained within that data is the insight you need to run a successful operation both now and in the future. It can help shed invaluable light into the habits and the preferences of your consumers. It helps you understand not just where the marketplace currently is, but where it is likely headed – all so that you can get there before your competitors do.

The issue is that when you’re talking about data volumes of this magnitude, simply making sense of it all can be an uphill battle. But without the proper analytics, the data itself is meaningless – you’re talking about little more than 1s and 0s sitting on a hard drive somewhere.

This, in essence, is why a data quality audit is so important. Not only do you need to be able to uncover the story hidden beneath that data, but you need to be able to trust what you find. Data auditing is a process that involves making sure that a company’s data is fit for a given purpose – in other words, making sure that the decisions you’re making are backed up by data that is of a high quality.

But what is a data quality audit in a larger sense, and what benefits can it bring to your marketing efforts in particular? The answers to these questions require you to keep a few key things in mind.

The Importance of Data Quality Auditing

When it comes to data analysis, one of the biggest pain points that a lot of organizations have to deal with comes down to volume. So much data is being created daily that even processing and storing it becomes not just time-consuming, but costly.

They also need to make sure that their data is set up in the right way to avoid issues like duplication or incomplete records. This, too, is challenging.

Thankfully, a data quality audit can address all of these problems and more in the following ways.

Database Structure

In terms of database structure, one of the major issues that a data quality audit can help with involves breaking down data silos. A data silo is essentially a pocket of information that is “trapped” in one place, unable to freely move across an enterprise. A sales team might have insight into consumers that the marketing team desperately needs, but if that insight exists solely in an application that only the sales team has, it’s not generating maximum value for the business. A data quality audit and the right cloud-based tools can make sure that anyone who needs data to effectively do their jobs always has it, no matter what.

Data Integrity

Another way that a data quality audit can help has to do with data integrity. This makes sure that the information decision makers are working with is relevant, meaningful, and always up-to-date. Examples of poor data integrity include but are certainly not limited to instances of duplicate data, situations where you may be working with incomplete data, data that is incorrect for the given task because it has been inputted into the wrong location, and data that is totally inaccurate from the start.

Regulatory Compliance

Data quality audits are also invaluable when it comes to ensuring regulatory compliance. Depending on the industry that you’re operating in, there may be strict rules that you have to follow in terms of what data can be created and stored, where it can be stored, and how long it is allowed to exist before it can be properly disposed of, among other factors. With regard to regulatory compliance, a data quality audit can help make sure that all requirements are being met to avoid potential fines and can remove data silos, which again leads to improved marketing performance.

Security

Finally, a data quality audit can help address one of the biggest issues of the modern digital era: security. The term “data breach” refers to a situation where a company’s information has been accessed without authorization. According to one recent study, the average cost of just a single data breach rose to $4.35 million in 2022. A data quality audit can improve security by identifying potential vulnerabilities when it comes to personal identity information, any credit card numbers that you may be storing, and other customer data.

How a Data Audit Works

When it comes to actually conducting a data audit, there are a number of critical steps to keep in mind. These include ones like:

• Planning your data audit. This is not a process you want to enter blindly. Everyone who will be responsible for leading and ultimately presenting the results of the data audit must be identified, and a plan should be agreed upon and documented that outlines what will happen during the audit, what steps will be taken, and other relevant information.
• Identify personal data and how it is processed. To conduct a thorough audit, you need to know what types of personal data your business is actually storing. You need to know where that data came from, what it is being used for, and how long it is being held, among other questions.
• Assess processing and compliance. This is the part of the process where you look at your business’ data practices within the context of any laws or regulations that you have to follow. You need to carefully examine things like security measures, access controls, and more. You should also carefully identify any employees or third parties who have access to that personal data and why.
• Report, recommend, and make changes. After the audit is complete, you’ll be left with a bigger picture pertaining to your data practices. You’ll know what is working, what is compliant, what is secure, and most importantly what isn’t. At that point, you need to act on those insights and make any changes that are necessary to avoid further issues with your marketing (and potential regulatory compliance concerns) in the future.

While the specifics of each data quality audit will likely vary depending on the organization, this is a general outline that all types of businesses can follow for the best results.

Who Conducts a Data Audit

Generally speaking, a data quality audit will be conducted by a data scientist or a team of other like-minded professionals. Your business will likely want to establish what is called a data advocacy panel – a team of people responsible for oversight, prioritization and budgeting when it comes to first-party consumer data.

The data scientists and other members of this team will work to establish the policies and strategies necessary as it pertains to how all data is handled across an enterprise. They will also be in a position to react quickly to issues like data breaches, shifting consumer attitudes, and more.

In terms of marketing, this is essential because it makes sure that the insights people are using to effectively advertise to people are based on the most complete information possible. Given everything going on in the world right now, the last three years have shown us that consumer preferences and buying behaviors can change rapidly and unexpectedly. A data quality audit can help anticipate those changes, putting a business in a position to learn from them and to treat them as an opportunity as opposed to a liability.

Of course, thanks to modern day tools, technology has made the process of conducting a data quality audit even easier – and the power of automation is a prime example of that.

Automated Data Auditing

Artificial intelligence-powered automation platforms have been a major benefit to data quality audits in recent years. The audit process itself is an important one, but it is also filled with a lot of time-consuming and menial tasks like verifying data sources, eliminating duplicate records, verifying database structure best practices, etc. Again, all of these tasks are critical – but they also take up a lot of time and virtually all of them are aimed at preserving the status quo as opposed to actually extracting value from the data you’re working with.

By automating as many of these processes as possible, a few things happen. First, you can be confident with the overall quality of the information you’re working with because it has been processed and thoroughly analyzed with a system built to, among other things, eliminate human error. Likewise, it all frees up the valuable time of human employees so that they can spend less time processing data and more time acting on the insight contained within it.

Tools for Auditing Marketing Data

In the end, the process of managing and auditing customer data will be far easier – and more effective – with the right tools by your side. This is a reason why many businesses employ options like data marketing platforms to that end.

Many solutions on the market employ artificial intelligence to not only shed more light on customer behaviors, but interests as well. This puts businesses in a better position to not only target their marketing to more precise audiences in a more effective way, but to do so at scale as well. You don’t have to worry about outgrowing the platform as your audience continues to expand and evolve.

In general, many of these platforms offer features like the ability to identify unknown visitors using JavaScript tags, ID resolution, etc. You can also enrich known profiles with behavioral scores and content affinity, both of which make sure that the right data is being matched to the right person for careful analysis.

Also essential is a platform’s ability to let you segment users into real-time target audiences via a graph database. Again, the more you know about someone, the easier it is to market to them – which in and of itself is the most important goal of all.