You need to make sure that all of the data fields fits somewhere on the maturity model… if it is questionable you may not have defined your levels clearly enough. If it falls right between two levels, you may need to define a new level to account for the difference, or incorporate the characteristics into one of your existing levels.


If everything is good to go, the final step is to tag all of your data with its maturity model level. I always work with the data modelers to do this, as the elements need to be tagged within the data model.

I’m not going to spend too much time explaining it because it should be pretty clear to you what to do. Your model may have more or less levels depending on what you are doing, but I’ve found 5-7 levels should be sufficient. Any less than 5 and you may end up with data that falls between levels, such as a 2.5 – you don’t want to do this. Any more than 7 and it may become ambiguous as to what level a data element is really on because the levels will generally be very similar to each other.

The first step as you fill in your document should be pretty easy. Take the lowest maturity model level that we developed on Day 1 and plug it into the first row of the template. Next, take the highest maturity model level that we developed on Day 3 and put it into the last row of the template.

The next step is going to take a little more thinking and working. Review your notes from Day 2 on looking for a natural maturity model already appearing in your data. See if you can easily fill these thoughts into the levels in the middle of your highest and lowest. If you can, great! If not, try to think logically about the next maturity level up from the lowest level. If you had data that fell into the lowest maturity model level, what action would you first take to bring it up a step? The result of that action should be the next level. (Example, if your lowest level is ‘unmodeled’, your first action may be to add it to your model. Thus, level 2 would be ‘modeled’.) Continue on and build towards the highest level.


I didn’t say that this part would be easy, but it should not be too hard. Please feel free to email me questions and definitely enter your email address in the box on the right if you haven’t done so already. That ensures that you’ll stay up to date with the posts on this site (no, we won’t spam nor sell your email address).

That was an easy step, huh? The exercise of determining the lowest data maturity model level and the highest maturity model level is now complete. At this point you should start to see it coming together. You see the highest point, the lowest point, and you also have an idea of how your data maturity naturally occurs.


Tomorrow I’ll provide you with a template to organize this information in so that anyone can look at it and quickly understand it. It will also help us finalize the remaining pieces, the middle maturity levels, and put them into place.

As your first task, I posed the question, “What is the lowest maturity level you currently have for your in-scope data?” (see Day 1’s post on the Data Maturity Model here) In this post we’ll talk about what to do with that, as well as review a term I call the ‘natural data maturity model progression’.


Yesterday we looked at individual elements to identify the lowest level on our maturity scale. We are going to stay at this low level approach again today to see if we can identify any trends. What we are looking for here is if there are any natural progressions you can see in the data as it stands today. Starting from your lowest level, what is the next step-up in maturity that you already see? If your first level was “Unmodeled, No metadata, no idea what it is”, the next step you see in your data could be, “Its in our datamodel but we have no supporting information on it”.

So, as you see, there is a natural positioning already in place. One of your data elements isn’t even in your data model, another is. Right there you have two elements with two different positions in a maturity model. Now, what do you see after that level? It could be, “Its in our model, we have metadata that needs to be updated and verified”. At this point, we roughly see three levels in the maturity model that already exist, we’re just giving them names. Don’t worry about making your descriptions pretty yet, we’ll get to that because its an important step.

How many different maturity levels do you see in your data? That is your task today. Rather than creating a data maturity model and forcing your data to fit into it, we are letting the various stages the data is already in define the maturity path.


Tomorrow we’ll talk about defining the best case scenario and how many levels you should have!

So, over the next five days I’m going give you steps that you can follow to build a maturity model that works for your organization and is very powerful in tracking the progress of your Data Governance Program. You’ll remember from my post on data governance metrics that it is extremely important to know where you’ve been, where you are, and where you are going.

Now, onto the steps for a maturity model…

The first thing to do is identify what data you are going to apply your maturity model to. Have you defined the scope of your data governance program? If so, what data is that? If you haven’t yet defined the scope of your program, you really need to do that first.

Now that you have identified your in-scope data elements you can begin working on your maturity Model.

Answer this question – what is the lowest maturity level of data in your dataset? Your answer could be something along the lines of, “Unreviewed, unmodeled, no metadata, have no idea what it is”, “It is in our corporate data model but no information other than field name, not in metadata”, or “Its in our model, we have some old definition that we can no longer consider reliable”.


This may take some time to do, but it is important to establish this baseline. The main things to capture are:

1. Is it in your datamodel?
2. Do you have metadata for it?
3. Do you trust the information in the metadata, if it is in there?

Come back tomorrow for the next Data Governance Maturity Model article. We’ll talk about how to use that baseline that you’ve esablished as well as how to identify and use the ‘natural data maturity model progression’ that you probably already have.