Poorly managed data puts organizations at risk. Conversely, high data quality maximizes the value and perceived value of it. Combining the right approach to data quality, data governance, and tag management make this possible.
This post will explore the common errors linked to pre and post-processing data, how to improve data governance, and the importance of integrating a tag management strategy.
Common pre-processing data errors
Technical errors with your data can creep in, even at the pre-processing stage. The most common of these are:
1. Browser Bugs: Interactions with different browsers, and bugs within the browsers themselves, can cause sites and apps to function differently in different environments.
2. Code Dependency Problems: Issues with code dependency can make it possible to remove modules the program depends on unintentionally. Small changes in the dependent code can quickly break an application.
3. Duplication: Whether duplicate tags or code, the fundamental problem with duplication is that it is wasteful. However, worse, it can cause inaccurate data, skew metrics, lead to poor reporting, and reduce data value. Combined, these issues can also lead to a poor sender reputation.
5. Structural Concerns: Sometimes, errors in HTML and CSS code can be hard to find, leading to wider structural concerns.
How to resolve pre-processing data errors
In all of the examples listed above, the errors are found to occur before data processing. If you just allow these errors to flow through into your analytical resources, time will be wasted cleaning data and bugs rather than discovering the actual insights in your data. As such, it is a good idea to use software that enables you to check the quality of data at the source of collection.
You should also clearly define the data you want to include in your tag management implementation plan. It means thinking about what the data is, when you collect it, where from, and in what format. Indeed, when it comes to tag management and mature tag management systems, this is fundamental to the implementation plan.
Common post-processing errors
Whilst rooting out errors at the source before they enter the production environment is important, you still need to plan for post-processing errors. Here, we outline some of the most common post-processing errors:
1. No Plan: Tag Management Systems (TMS) make it easy for almost anyone to create, edit, removed, and deploy tags. However, this can soon lead to duplicate tags, inefficiency, and waste. Good tag management governance always requires a plan for why data is being collected and who can produce and publish tags. If you can think of no reason for a tag to be doing what it’s doing, always let it go.
2. Data Security Issues: Tag management requires you to think carefully about data security. Just because something is technically possible doesn’t mean it is the right way to do things. Customer data like emails should never find their way into tags if you are to avoid data breaches.
3. Untested changes: Even if things are well planned, unexpected errors can crop up when changes go live. Failing to test in a pre-production environment is usually a big mistake.
4. Lack of documentation: Having a clear plan when you start out is important. However, it would be best if you also tracked changes over time. Workflow diagrams, and a clear quality assurance (QA) process, should be well documented to ensure changes are always recorded.
5. Failure to audit: Even if you have planned, tested, documented, and QA’d everything, data governance and tag management can fall down when you fail to audit the final outcome.
How to resolve post-processing data errors
When it comes to post-processing data errors, it is vital to use the software if you are going to bring a poorly governed system of tag management under control. They won’t necessarily help you to capture issues with data collection.
However, if you are using pre-processing software to capture these, fewer data issues will be being passed through in this way. The key way post-processing software will help is in the ability to trawl through millions of bits of data and all of your tags. Some can even fix tag management systems in real-time when they pick up these errors. This helps you deal with errors that can drip into the system over time.
Where do data governance and tag management fit in?
It’s important to remember that even with good data governance, hidden errors can always find a way in. In conjunction with good tag management, the key thing data governance offers is the ability to prevent errors going unnoticed. Combined with the use of pre- and post-processing software to improve inputs and outputs, data governance and tag management ensure you can see things holistically.
Data governance and tag management are all about continuous improvement. However, errors and the need for change should not be shamed. Indeed, these are vital steps along the way to learning from the monitoring and adaptation required in data governance and tag management. Moreover, since all aspects of data governance are interrelated, a holistic approach ensures the oversite of overall data quality. This facilitates:
- Quality data collection ensuring efficiency and improved monitoring
- Fast responses to error to prevent overspill
- Increased data quality throughout its lifetime, promoting trust
- Added value to your data built on trust
- Improved decisions based on better data
Ensuring data governance success
An integrated tag management strategy requires good data governance throughout the pre- and post-production process. High-level support for improving governance is vital, followed by good education to employees about its importance. Security and legality also need to be robustly checked, and the strategy should cover all digital domains.
Reporting data errors should be encouraged, and their occurrence should not be considered a failing or failure. Finally, clear quality processes and governance documentation with internal ambassadors leading the way will facilitate continuous improvement.