Bad Data Quality or Bad Data Governance
07 Jun 2026 » Opinion
I have spent the last five years of my career at Adobe, working with the Adobe Experience Platform (AEP). I cannot say “I have seen it all” (I wish I could), but I have seen many different implementations. It is thanks to this experience that I can share with you the content of this blog. While the majority of my customers have had great data to start with, from time to time I have heard the concern that their data is of bad quality and they need to clean it up before sending it to AEP.
Bad Quality
Let’s start by defining what we mean by bad data quality. IBM defines it as follows:
Poor-quality data occurs when datasets fail to meet the requirements of a specific business operation. Even data that appears accurate and complete can function as “bad data” if it isn’t fit for purpose, meaning it doesn’t support the use case […]
I very much like the inclusion of “business operation” and “use case” in the definition. Data is data is data. Zeroes and ones are not wrong, unless there is data corruption. The quality of the data has to be evaluated against the desired outcomes of the usage of that data. In our case of the digital marketing sphere, data is good only if a marketer can trust it to make decisions and run successful campaigns.
The consequences of having poor data quality are multiple and varied. It would require a long post, which nobody would read, and it would still be incomplete. Instead, let me share a couple of examples in AEP:
- Segmentation. You want to be sure that the audience receiving the marketing message is the right one. Sending a message that does not resonate with a recipient is a waste of resources and can damage reputation.
- Personalization. In this world of hyper-personalization, generic or incorrect messages ruin campaigns.
Data Patching
Once the bad data is in the system and is needed immediately, we have no other option than to patch it. However, this is just a band-aid, as the next batch will contain errors again. I want to emphasize that this is a short-term solution. It just means that we are doing what we can to keep the system working, but not fixing the root problem.
I will not try to enumerate all options to clean up the data temporarily, just share some ideas with AEP in mind:
- Use data prep functions upon ingestion, even though we strongly recommend against using them.
- Use data distiller to:
- Modify profiles with issues that can easily be fixed.
- Mark profiles that should not be used, or put them in an exclusion audience.
- Calculate a data quality index per profile, and create an exclusion audience based on a data quality index below X.
- In Adobe Journey Optimizer:
- Use conditions to exclude profiles that do not meet the minimum standards for a journey.
- Use an update profile activity to increase the quality of the profile.
Bad Governance
In the past, when I heard my customers complain about their low data quality, I just accepted it. It is not my job to judge how they run their business.
However, recently, someone mentioned that the problem is not with data quality, but with data governance. It was one of those lightbulb moments 💡. I told myself: How could I not have thought of it? It makes a lot of sense!
Poor data quality is the evidence of the problem, not the problem itself. The problem lies in allowing the wrong data to enter the system in the first place. Why did it happen?
Data Governance for Data Quality
Many years ago, I had a customer with a problem in the data they received from third parties. These third parties were using the email address as the identity, which, as you all know, is a bad, bad idea. However, my customer wanted AEP to fix the problem. Before AEP, they had a complex solution based on heuristic rules to choose which profile an event should be assigned to. It took me a lot of effort convincing them that a CDP is not an MDM. Finally, they realized that the solution was to force all customers to have a unique email address.
In order to ensure that any data entering the system is of sufficient quality, you have to make sure that humans enter correct data. In other words, it is a human responsibility, as, ultimately, all data comes from human sources.
Therefore, data governance is the only solution that addresses the root of the problem: the human. I am not saying that humans are stupid, but we know that we all make mistakes. These mistakes can be as simple as a typo or as important as not submitting the data. You will have noticed that I have used the word “solution” here, whereas I used the word “patching” earlier. I have done it on purpose.
While researching for this topic, I found the Wikipedia page on Data Governance very useful. I want to highlight the following comments:
Data governance is the set of principles, policies, and processes that guide the effective and responsible use of data within an organization. It creates a framework for decision making, accountability, and oversight across the data lifecycle, from creation and storage to sharing and disposal.
Data governance initiatives improve the quality of data by assigning a team responsible for data’s accuracy, completeness, consistency, timeliness, validity, and uniqueness.
Leaders of successful data governance programs declared at the Data Governance Conference […] that data governance is about 80 to 95 percent communication.
As you can see, all these points apply to humans: processes, teams, communication… There is nothing technical in data fundamentals. Sure, technology should help us avoid bad data quality, but technology on its own cannot be at the core.
I am totally aware that this solution is a tall ask. Companies are like oil tankers, which require miles to change course or stop. Nobody likes to be told that they have to work differently, and there is a natural resistance. Unfortunately, I cannot think of any other option. On the other hand, I have worked with many companies that have taken good care of their data governance and they are happily using their data to achieve their goals.