In a previous article we discussed the importance of the data quality checking process with regards to tag management. We described the differences between pre- and post-processing data quality checks and solutions for each method. Higher data quality can be perceived to increase the value of it. Now we want to discuss how data governance, including data quality, can play a significant role when used together with tag management.

Before we begin, we would like to point out that there is no exclusivity between data governance and tag management. Both work independently of each other, although a solid foundation can function as a means to a proper tag management setup and implementation.

What is data governance?

Let us not re-invent the wheel here. Data governance has been around for a long time. It is unavoidable not to act cliché, so here is Wikipedia’s definition of data governance:

Data governance is a data management concept concerning the capability that enables an organization to ensure that high data quality exists throughout the complete lifecycle of the data. The key focus areas of data governance include availability, usability, consistency, data integrity and data security and includes establishing processes to ensure effective data management throughout the enterprise such as accountability for the adverse effects of poor data quality and ensuring that the data which an enterprise has can be used by the entire organization.

Although we agree with the definition, Tagticians feels that there is an element missing. The human element. Although the goal revolves around effective corporate data management, there are many technological and human influences on data collection that are difficult to identify when establishing a data governance model for the first time.

“To err is human”, said every person working with data at least once

We will be the first to admit that making mistakes is part of the process when defining a tag implementation and tag plan, two core elements of data governance. Someone should own tag management, but there is only so much that can be tested and determined when deciding on the most effective data collection strategy. In the end, technology (websites, apps, servers) can be unpredictable.

Technical Errors

During a Tealium webinar on data quality 7 excellent examples of pre-processing technology errors were given that describe the hidden data quality pitfalls perfectly. Those examples were:

  1. Browser bugs result in issues
  2. HTML Stuctural changes
  3. Order of code changed on page
  4. Removal of code dependencies
  5. Duplication of tags/code
  6. Javascript library updates
  7. New javascript code conflicts with a global variable

Procedural Errors

As an additional example, post-processing and -implementation errors can also occur due to inadequately designed communication processes. The lack of communication processes in data governance can lead to data collection errors remaining undetected and unresolved for prolonged periods of time.

For instance, changes in local law, such as Europe’s GDPR law which came into effect in May 2018, had an effect on data governance. The GDPR law required changes in pre-collection and -processing. For many companies these changes needed the optimization of existing tag management implementations. If required changes are not communicated properly it can lead to post-processing privacy issues if not acted on swiftly and adequately.

Encourage Data Governance

We need to change the data governance definition ever so slightly to include the need to not shame failing, but to encourage a process of monitoring, identifying, fixing, communicating and learning. Discouraging a continuous improvement process will only harm your company. It can seriously effect trust in and use of potentially valuable data and, in effect, your bottom line. Integrity is key, not only in your data tools, but also your human data resources.

The data governance endgame

There are many reasons your company should embrace data governance. From monetary motivations to data scientist sanity. The simple truth is, every aspect of data governance is integrally linked to the other, for instance:

  • Company wide data collection guideline implementation will allow for easy monitoring.
  • Data collection monitoring enables swift response to errors and increases the quality of your data.
  • Increased data quality will make your data more trustworthy.
  • Increased data trust will improve the value of your data.
  • Increased data value will lead to better decisions.

Like we pointed out, and this is always a good thing to re-iterate, data governance has a definite monetary value. A proper and well maintained data governance has significant impact on your company’s bottom line by:

  • reducing the costs of data cleaning and increase the productivity of your data scientists.
  • reducing the spend on marketing due caused by incorrect attributions.
  • preventing decision making processes caused by inaccurate insights.

Tagticians 6 E’s of Data Governance Success

To sum it all up, we came up with the 6 E’s of data governance success. We hope that these 6 points will paint a simple and clear picture of the steps involved to make data governance a success in your company.

  1. Ensure high-level support for it.
  2. Educate employees on its value.
  3. Enforce liasons with Legal and Security representatives.
  4. Enable it across all websites, apps and digital domains.
  5. Encourage the reporting of data errors as early as possible.
  6. Embrace internal ambassadors of the data governance.

If you are interested in learning more about how Tagticians can help you implement and support data governance in your company, please do not hesitate to contact us. Also, if you are interested in any of Segment’s solutions, Tagticians is a Segment certified partner and can help you get started.