A practical and pragmatic approach to the implementation of data management that delivers quick wins is one of the key challenges of any data management professional. Sooner or later, you will deal with this at one point in your career.
In the series of presentations on Practical implementation or optimization of data management with the “Orange” model, I share with you my practical experience of the past 10 years. This experience has led me to develop a new model and practical method for the implementation and optimization of data management. This method is a collection of techniques and templates that can be used for performing various tasks related to the development and optimization of data management in your company. Today we discuss how to assess data management maturity.
Using the “Orange” model for developing and performing a data management maturity assessment
I have been working on the topic of data management/governance maturity for the last two years. My key focus is how to assess data management maturity. To my biggest surprise, my articles on this topic are by far the most popular compared to other topics.
In this article, I will share my journey with data management maturity over the last two years. This journey involves understanding and developing practical approaches to assessing data management maturity. Let’s explore three questions about data management maturity, using the example of the “Orange” model of data management by Data Crossroads:
- What is data management maturity?
- Why do companies need it?
- How to assess data management maturity?
What is data management maturity?
There are a lot of different definitions of maturity put into different contexts. I have chosen one which matches our purpose: “Maturity is the ability of an organization to undertake continuous improvement in a particular discipline” (Source). I took four keywords from this general definition and converted them into a graph that you can see in Figure 1. Our task will be to adjust this definition to data management.
Figure 1. A graphical representation of the term “maturity.”
Particular discipline
The first word is “particular discipline.” In our case, it is data management and/or governance. The definitions of data management put in different contexts differ significantly. Therefore, you should specify your understanding of data management.
Data management definition will vary depending on the following factors:
1. Different organizational levels.
My series of articles about data management and governance show that definitions, content, and deliverables depend on the organizational level.
For example, at a strategic level, data management focuses on assessing the value of data for the business. It expresses data strategies and data (management) programs.
At an operational level, data management concerns the proper performance of the data lifecycle.
2. Data management models proposed in different guides.
I have discussed in several publications the conceptual differences between DAMA-DMBOK and DCAM.
First of all, let’s take a look at the list of data management building blocks. DAMA-DMBOK2 uses Knowledge Areas as key business blocks. DCAM operates with components, capabilities, and sub-capabilities. Even at first glance, you can see that the number and names of the blocks differ. DAMA has 11 Knowledge Areas, and DCAM has eight components, 31 capabilities, and 106 sub-capabilities—one of the most important differences in the relationship between Data management and IT. According to DAMA, data management is part of the IT domain. DCAM, on the other hand, considers IT as a part of the collaborative environment. There is also a misalignment between these two models’ definitions and key data governance artifacts.
The “Orange” model of data management offers the following definition: “Data management is a business capability that safeguards company’s data assets and optimizes data value chains to ensure the effective conducting of business” (Source).
The “Orange” model considers five constituent capabilities of data management: data governance, data modeling, data quality, information systems architecture, and data and information value chain, as shown in Figure 2:
Figure 2. Key data management capabilities in the “Orange” model.
Ability
The second word in the definition of maturity is “ability.” To measure maturity, you need to make “ability” measurable.
To do it, the “Orange” model uses the concept of business capability by the Open Group. Four dimensions constitute a business capability: business process, roles, data (outcomes), and tools. This approach is pragmatic, as it allows to design and implement each data management capability into practice.
Measurement
The third word in the definition of maturity is “measurement.” A detailed description of the five data management capabilities along four dimensions makes the data management capability measurable. An example of a detailed description is presented in Figure 3.
Figure 3. A detailed description of a data modeling capability.
Take, for example, developing, documenting, and maintaining information requirements. This process can either be inexistent, performed in an ad-hoc mode, in the development or implementation phase, or fully operational. Using these statuses, you can assess the current status of the process and specify the desired one. Such an analysis of each of the items of the data modeling capability is de-facto the measurement of the maturity of this capability.
Continuous improvement
The fourth word of the maturity definition is “continuous improvement.” I have shown how to assign five different maturity levels to a process. These 5 statuses can also be easily applied to analyzing deliverables, tools, and roles.
We have discussed the definition of maturity. Let’s talk about why we need to perform such an assessment.
Why do companies need to perform a maturity assessment?
The advantages of using a maturity assessment for data management functions seem quite obvious.
Each company manages data in one way or another. Some companies have already formalized the data management function. Others have not. Regardless of this, any company can still measure its data management maturity.
There are two key reasons why a company should perform a maturity assessment, as shown in Figure 4.
The first one is the need to improve the performance of data management. You will do it by assessing the ‘as is’ status. Then you will identify the ‘to be’ status. The gaps between the ‘as are’ and ‘to be’ statuses will form a foundation for developing strategies, roadmaps, and plans.
The second reason is to benchmark your results against peers in the industry. Very often, this can be difficult as the maturity assessments differ due to the reasons I have discussed above. Maturity assessment should not be a one-off event.
The key aim is to develop a tool that will assist the company in constantly measuring its performance and progress and benchmarking the results against other companies.
Figure 4. Key reasons to perform data management maturity assessment.
Now let’s move on and discuss how to perform the maturity assessment.
How to perform maturity assessment
You have two options: use already existing models or develop your own. Regardless of the chosen approach, you must perform the steps shown in Figure 5 to design and perform a data management maturity assessment.
Figure 5. Key steps to perform data management maturity assessment.
STEP 1
Specify the metamodel of data management used in your company. You need to know your definition of data management, its components, and each element’s key deliverables.
STEP 2
Align your metamodel of data management with the metamodel of the chosen maturity model. Assume you decide to develop your maturity model. Then you are safe. You can apply the business capability approach to your data management metamodel, and it is done.
But what if you will use one of the existing maturity models? How would you choose? For example, DAMA-DMBOK operates with Knowledge Areas, DCAM with capabilities and sub-capabilities, and CMMI with business processes. You should avoid comparing ‘mandarins’ with ‘pomelos.’ The name of the “Orange” model came after this study as an attempt to align metamodels of data management and data management maturity models. Suppose you are interested in diving into the differences between the existing models. In that case, you can look at a comparative analysis of the most well-known models I did a while ago HERE.
STEP 3
If you decide to proceed with your model, you should specify maturity levels and the measurement method.
For example, in the “Orange” model, I have applied the following logic in measuring maturity, as shown in Figure 6.
Figure 6. An approach to measuring data management maturity.
The measuring process starts at the level of the dimensions: process, data, tools, and roles. The level of the maturity of each sub-capability is the average of the maturity of its dimensions. The level of the maturity of data management as a whole is the average of the maturity of each underlying sub-capability.
STEP 4
By now, you should be able to measure your company’s data management maturity.
Last year (2019), I developed a short data management maturity scan that can guide you through a quick assessment of your current situation with data management. You are very welcome to perform this scan.
The results of this scan are being collected anonymously. During the first 9 months of 2019, around 70 participants performed this scan. I found that these first results have exposed some quite interesting patterns. I have published them in the Data Management assessment review.
As soon as you have measured the maturity of data management in your company and specified the desired level of maturity, you can start implementing or optimizing data management in your company.
For more insights, visit the Data Crossroads Academy site:
