Data Governance: The Fundamental Tool for Data Management

Daniel Mannino
Feb 13 · 4 min read
Photo by Barn Images on Unsplash

The goal of this article is to clarify what data governance is and how to apply it.

I decided to write it because I saw a lot of articles and blog posts where data governance is pictured as an enemy whose only goal is to slow down any data related activity like analytics. Nothing could be further from the truth.

Data governance is your foundamental tool for any data related activities! This what I will demonstrate in this article.

This article contains:

  • a definition of data governance and its goal, and
  • a data governance framework

I took inspiration for the article from my 20 years of experience in data management, the DAMA book, numerous blogs/articles that I read, and the excellent book “Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program” by John Ladley.

Definition and purpose of data governance

Data Governance (DG) is defined as “the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets to ensure that those assets are managed properly”.

The previous sentence contains 80% of what you need to know about data governance. You might take a bit of time and read it one more time and slowly, very slowly.

The sentence introduces an important separation between two concepts: data management (DM) and data governance (DG).

DM is a set of activities like:

  • database management
  • data quality management
  • analytics

DG encompasses all the activities that are needed to ensure that DM is done properly. Properly for me means that data is managed:

  • efficiently,
  • effectively,
  • lawfully, and
  • according to the best practices of risk management

You might not knowing it but we are all already applying DG in your daily life since we all manage data and we do it every day. We do it when we store files on our laptop or when we tag an email in Gmail.

Do you have a standard to tag your emails in Gmail? I do. I tag my email with the following labels: “Urgent”, “One day”, or “Awaiting reply”. All other emails end up in the bin after I read them. When I have time, I go through my inbox starting with the email with the label “Urgent” and I perform whatever action the email requires. I delete the email as soon as the action is done.

I start applying this email “hygiene” a long time ago to avoid missing important emails.

My “hygiene” is nothing more than my way of applying data management to my emails. The fact that I planned to manage my inbox with labels, the labels that I use, and my dedication to manage my inbox that way are all components of the data governance of my inbox.

Periodically, I review my “hygiene” and I ask myself: does it work? How many important emails am I missing? How can I improve it? Monitoring the efficacy and improving continuously are important aspects of data governance. Unfortunately, they are often neglected.

Managing my inbox is easy. Ensuring that data is managed properly at the enterprise level requires a robust framework. The next chapter describes such a framework.

The DG framework

The exercise of authority and control over the management of data assets at the enterprise level requires a robust framework. The framework includes a set of components that work altogether to ensure that DG is implemented correctly and is sustainable.

DG framework

I would like to focus your attention on three points.

First, setting up a “data governance council” that meets every month is not DG. I saw countless initiatives starting with that council and dying slowly and painfully. DG requires all the components of the framework above.

Second, the component “metrics” is often neglected but it is of paramount importance. Good metrics make DG sustainable because they tell if the DG is working -that is, if data is managed properly or if corrective actions are necessary.

Third, start always with a problem in mind! Do not try to put together “the data governance initiative”. It won’t work. Start with a data management problem that you are facing and you want to solve. I can be, for example, data quality for products in an ERP system, or the automation of the deployment of the ETL code of your analytical system. Put together the DG required to fix that problem, showcase your success, and tackle the next problem. You will be successful!

Conclusion

I hope that I was able to shed some light on what DG is.

Feedbacks are welcome.

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Daniel Mannino

Written by

I am a cloud-native analytics architect and my goal is to bring companies from drowning in data to swimming in innovation

The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +787K followers.

Daniel Mannino

Written by

I am a cloud-native analytics architect and my goal is to bring companies from drowning in data to swimming in innovation

The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +787K followers.

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