9 keys to data governance success

Unfortunately a lot of data governance programs fail and there are many reasons why. The silver lining is that  there are great lessons from these failures that we can learn from and make sure that we will avoid them in our data governance program.  

Here are the 9 keys to data governance success:

1. Treat data governance as a program

So many times people refer to data governance as a project. What's wrong with this? What's wrong is that projects have end dates. Data governance, if you want it to be successful and serve your data well, it needs to be a program.

You can't put an end date to it, because it needs to be ongoing. That is if you want to be a data-driven company, if you want to derive information and knowledge out of your data, then data governance can't have an end date.

Indeed, a program is comprised of many different projects, one of the first ones, for example, would be to establish a data governance framework, another is to do the as-is assessment, another to determine the to-be state and the plan on how to get there. But data governance does not stop.


2. Overcome political challenges

Spoiler alert if you're new in data governance: there will be political challenges. Usually plenty. There will be a lot of people singing "I want it that way" - cue in the Backstreet Boys :).

Data governance usually brings in some changes in order for there to be clarity:

  •  into what the data means
  • how it should be acquired, produced, maintained, used
  • what resources should be dedicated to it
  • what priorities should be addressed and so on

 And there will be a lot of opinions on how any of the above should best be done. Of course, some will be conflicting one another and some will not in the best interest of the company.

If you don't want a heart ache, you need to overcome these political challenges. And that's where good sponsorship, strong executive support, communication and change management will come to help.


3. Don't boil the ocean

There are a lot of areas to tackle in a data governance program. It can be overwhelming. When I got my first role in data governance it felt like being told "just relax and boil the ocean".

You need to learn to identify the low hanging fruit and tackle those, but at the same time keep an eye out for that strategic, long-term goals. That's why a data governance maturity model can be a good tool because it shows you what should you start tackling first in order to progress and advance to a higher maturity level.


4. Build iteratively

To build on the previous key to data governance success, build iteratively. I recommend cycles of a 3 month duration at the end of which you are done with a deliverable. Of course some would require 6 or even 12 months.

All in all, it's good to be consistent and to set that expectation to your stakeholders that after a set number of months, there will be a larger deliverable. This deliverable could be:

Just keep building and progressing from one iteration to the other. And again you can look at a data governance maturity model for some guidance on that. 


5. Focus on data stewardship and metadata management

In my opinion, data governance can't stand on its own. It's like a tripod that can't stand freely on its leg. You also need a strong data stewardship program in place as well as a metadata management program. Then everything else, other data management areas, data science, AI/ML and so on can be built easier on this strong foundation. 


6. Measure success

Maybe a no brainer, but this is happening too many times not to mention it to you. People forget to measure the success of data governance. Especially in the early stages of the program.

It's probably because they are keen on getting something off the ground, to show some improvement, but then they forget about the "show" part of "showing some improvement". It's hard to show improvement if you don't take a baseline measurement. That's what a lot forget to do. 

Make sure you take a baseline measure and also develop metrics that you can track progress against. I do cover all of this and more in my online courses on data governance, by the way.

The second aspect to this is to tie these metrics to the company goals. Because it's not enough to just say that:

  • we've added 100 business terms to our business glossary, or 
  • we've trained 10 data stewards, or
  • developed data quality standards for these 2 data domains, etc. 

That's not why we're doing data governance. We're doing data governance to get better insight and hindsight and foresight out of our data, to determine efficiencies, to make our customers happier and so on.


7. Focus on data domains

A lot of data governance programs don't start at the enterprise level. A lot of them start at the department level. That's fine, but if it doesn't go enterprise-wide it will be siloed in its implementation and it will ultimately fail. As a result there will be undesired consequences, such as:

  •  unable to achieve a 360 view of the customer
  • inability to build a robust AI/ML program
  • no reduction of data quality errors
  • data illiteracy, etc.

Other data governance programs are focused on a particular system, usually a CRM or an ERP. This is also wrong because it's missing important stakeholders that might not be using these systems, but are using and creating related data.

So the best way to focus your data governance is on data domains, such as customer, product, location, etc. This way it will ensure that the relevant departments and systems are part of the scope as well.


8. Don't put it under IT

This is another mistake that I see often, the fact that data governance gets slotted under an IT function. Why is that a problem? Because then it gets associated with IT, there are assumptions being made that IT needs to lead this, that they need to own this, that they need to provide all the answers.

Yes, IT is an important partner to have and a key stakeholder, but the business must be continuously involved in the data governance program. Believe it or not, it would be the business that owns it, really, not IT.

Without the business providing that ownership, without the business providing that stewardship, without the business providing their requirements, knowledge, guidance, support then data governance will most likely fail.


9. Communicate and manage change

Communication is your best friend. At a conference a few years ago, the IDC was mentioning that data governance is 90% communication. Maybe that's a bit much, but still, successful data governance requires a lot of communication.

As I mentioned in an earlier key to data governance success, data governance usually brings quite a bit of change and that needs to be managed through:

  • communication,
  • stakeholder engagement,
  • strong sponsorship,
  • executive support,
  • training, and so on. 

Don't underestimate their importance and that's why I think this is probably the most important out of the 9 keys to data governance success. 


Conclusion

Data governance programs are not easy to implement and some fail because they do not account for one or more of the above points. That's why I encourage anyone to follow and adhere to these keys of data governance success. This will ensure an enterprise-wide data governance program that follows best practices and meets its requirements.

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About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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