how to select a data governance maturity model

Is this you? You understand the benefits of using a Data Governance Maturity Model, you want to use one, but you don't know which one to go for. Understandably so, because there are plenty out there. 

Out of the more notable ones, here are the ones we've covered so far:

And this is not an exhaustive list. So how do you select a Data Governance Maturity Model to use? Here are the things that you should take into account to help you choose the one that's the best fit for your organization.

Business drivers

The most important question to address is "What are your business drivers?". What is the goal that you're trying to meet that you need a model for? What is the organization hoping to accomplish by going through a maturity model? 

The answer can't just be "in order to get to level 5". That is just a means to an end and that end is your business goal. So you need to know what your business drivers are. 

This is important to know because you'll then look at the properties and offering of each Data Governance Maturity Model through that lens. I'll give you an example:

If your business driver is ensuring GDPR compliance, then the maturity model you select should have a data confidentiality, a data classification, or a data privacy subject area.

It doesn't need to, but it might be helpful if it does since it will help you identify the areas that should be tackled first and the deliverables for each level.

Why am I mentioning that it doesn't need to have that specific subject area even if that's what you are looking for? Well, because no maturity model out there will fully meet your needs. Most likely you will need to adapt it to your own environment, adapt it to your own industry. In that case, the model should be somewhat flexible. The good news is that most models are easily scalable and can be tailored to meet those specific needs. In the end, it's always easier to select one that doesn't require a lot of changes.

Resources

We cannot not talk about this one as that's at times the deciding factor when choosing a model. When it comes to budget, there are 2 considerations:

1. The cost to purchase the model

2. The cost/ resources required to implement the model

Most of the models cost money. Sometimes you can just purchase a document outlining and describing each characteristic for each maturity level. You would use that as a guide to assess your current level and the steps to take in order to get to the next.

Other times, probably the most common, the data governance maturity model comes as a package when you hire a third party to help you with your data governance program. In this case, you're getting much more than the model per say, but it also costs considerably more. This second option can also be provided by a vendor that would sell you your data governance and data management tools to help you progress on the technical/automation aspect of the model.

Besides the budget constraints that you might have to purchase the maturity model in the first place, you should also try and evaluate how expensive it would be to implement that model. Think of it this way, if you want to create a logo, you might want to watch a video on how you could do that best. As with data governance maturity models, there are plenty of videos out there that will show you how you could do that yourself, but here are 2 of them:

  • Video A: will show you how to create a 3D logo worthy of any major brand. Based on the info learned from this video, you would invest in some courses on color theory, hire an artist designer, a 3D modeler, a drawing tablet, some licenses for the required software, etc.
  • Video B: will show you how to create a 3D logo that might not be the same level of quality as the one from Video A, but one that still meets your needs and scope. In this case you will be using your pen and paper, scan your drawing, learn how to digitize it further using a dedicated software, and maybe hire a 3D modeler for the last step. 

The two videos provide a similar outcome, but with different ways of getting there, incurring different costs and requiring different resources. Yes, their final quality might differ as well, but that's why you need to understand the business drivers and see what you're actually trying to accomplish.

Framework

If you have a data governance or a data management framework in place, then this needs to be taken into account. Especially if it's one that's going well in your organization. If so, you might not want to change it, but then you also need a maturity model that resonates with it.

There's of course the possibility that you need to do some big changes in your framework. The model could provide you with some clues as to what those changes could be and what to implement first.

Industry

My last advice is to take a look at the peers in your own industry. Reach out to your peer groups to see which maturity models industry partners and associates use successfully. 

It's best to identify those peers that are ahead of you in the area that you want to improve and find out if they are using a maturity model that you could emulate. Of course, you have to be realistic and know that certain factors, such as the size of the company, its market share, its profit, and even its location would play a role in the chance that you could follow the same model or not. 

So start with your peer groups. If your company is running a small ecommerce store, you can look at Amazon as your competition and as a goal that you might want to also achieve, but don't expect that you can follow the same maturity model at the same way that they are. 

Conclusion

Based on the criteria outlined so far, you would have narrowed down your selection. It's important to present your top 2-3 picks to the sponsor(s) of your data governance program. If you don't have sponsors, make sure that you get an executive-level buy-in. Yes, it's important to get the resources for your program and maturity model, but it's equally important for executive management to understand why the maturity model is important to your organization.

Explain to them how the maturity model will help:

  • ensure the organization is spending resources correctly and appropriately on data governance on data management,
  • get data to be treated as an asset and transformed into meaningful information that fuels the business goals.

By sponsoring the effort, providing adequate resources, and accepting the final results, executive management plays a critical role in going through the data governance maturity model. In turn, you need to listen to executive management to know their priorities, issues, and resource constraints and tie that back to those business drivers.

Check out the Data Governance Maturity Models online course to learn more about all of the above, plus:

  • A thorough overview of several models (Gartner, CMMI, IBM, TDWI, Oracle, etc.);
  • Learning about the characteristics of a model;
  • Understanding the reasons to leverage one in order to build your business case;
  • Selecting a model;
  • Performing the assessment;
  • Analyzing the results;
  • Improving the maturity level;
  • Learning about their shortcomings and how to mediate them;
  • and so much more 

Enroll today and become a more knowledgeable professional.

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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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