Would you like to know more about data governance maturity 2020 trends?
Data Crossroads has published the Data Management Maturity Assessment Review for the second year in a row. In the previous article of this series, we have explained the methodology for measuring maturity. Then, we have demonstrated the general trends in data management. In this article, we focus on trends in data governance maturity 2020.
In this article, we will:
Explain the structure of this capability
Demonstrate the general trends in data governance maturity 2020
Investigate changes in four key performance indicators
Data governance/data management framework
In the “Orange” model of data management, I substitute the term “data governance” with “data management framework.” The key reason for that is that the data management community does not have an unambiguous definition of “data governance.” In a different context, it has quite a different meaning. I follow the definition provided by DAMA-DMBOK2. In this context, data governance is a set of rules and roles.
The data management framework is a business capability that delivers the structure in which all other data management sub-capabilities operate. The framework consists of rules and roles. Strategy, policy, process are examples of rules. Different types of data stewards and governance bodies represent roles.
Four components enable this capability. These are process, role, data/deliverable, and tool. Each component consists of a set of items. The full list of items per component, you can see in Figure 1.
Figure 1: Data management framework components in detail.
The logic behind this model is simple. Data governance roles are a deliverable of this capability. A process to design and implement roles produces this outcome. Particular data management professionals participate in this process. A tool such as a data governance application can assist in the design and maintenance of the roles’ description.
Now let us take a look at the general trends in data management framework maturity.
General trends in data governance maturity 2020
In the previous article, we have seen the general changes in data management maturity. The maturity of the data management framework has remained at the same level, as shown in Figure 2.
Figure 2: The comparison of the maturity levels per DM sub-capability.
The results in Figure 3 demonstrate some shifts between the levels of maturity.
Figure 3: The changes per maturity level.
The number of respondents at the two lowest levels of maturity has decreased. At the same time, the number of participants with the statuses “in development” and “capable” have grown. Both trends indicate the positive dynamic for this capability.
The “Orange” maturity scan uses four performance indicators to assess the maturity of this capability. Let us take an in-depth look at each of them.
Indicator 1: “data management function in place”
A formal data management function is one of the key deliverables of a data management framework. The formal data management function embeds data management into daily operations. In Figure 4, you see positive trends for this indicator.
Figure 4: Trends for the formal data management function.
In 2020, fewer companies did not have the formal function or have it in the “ad-hoc” status. Concurrently, the number of companies that have (been establishing) established such a formal function has grown.
To be operational, a data management function should develop policies and processes. Indicator 2 specifies the maturity level for that.
Indicator 2: “data (management) policies and processes in place”
For successful operation, data management should establish rules. Policies and processes are examples of rules. The positive trends are also valid for this indicator, as shown in Figure 5.
Figure 5: Trends for the data management policies and processes.
The number of respondents that don’t have policies and processes has significantly reduced. The number of companies that develop these artifacts remained the same. At the same time, more companies have reached the “capable” and “effective” levels.
It is impossible to establish data management without having a budget. Indicator 3 demonstrates developments in this area.
Indicator 3: “budget for data management initiative”
The situation with budgets also improved in 2020 compared to 2019. Figure 6 presents the results.
Figure 6: Trends for budgets for data management initiatives.
The percentage of companies that did not have a budget has dropped from 15% to 3%. Companies that have started the budgeting process have increased significantly from 18% in 2019 to 41% in 2020.
The availability of budget has connections to the support from top management. The next indicator demonstrates the trends for that topic.
Indicator 4: “the awareness and support from top management.”
The situation with the development of top management supports looks less optimistic compared to other indicators. The results in Figure 7 support this conclusion.
Figure 7. Trends for the involvement of top management.
In other words, C-suite in the companies that performed the test in 2020 is less interested in data management. The results in each maturity group confirm this conclusion. These results are not surprising. Several months ago, I published a poll on LinkedIn. 43% of respondents indicated that middle-management had initiated data management.
The demonstrated results have brought us to the following conclusions.
The general level of data governance maturity remained on the same level in 2020 compared to 2019
The changes in data governance maturity 2020have positive trends:
More companies have established a formal data management function.
The number of companies that have paid attention to the development of policies and processes has increased.
More companies have got budgets for data management initiatives.
The support of top management to data management initiative worsened in 2020 compared to 2019
To strengthen the data management framework, companies should focus on:
Expanding awareness of the benefits to implement data management among key business stakeholders and decision-makers
Creating sustainable and fit-for-purpose resources for the set of data management roles, policies, and processes
Embedding data management practices in “business as usual” operations
Improving the collaboration between all data stakeholders
Making data management artifacts available throughout all data management stakeholders by implementing repositories and data management tools.
In the following article, you will read about the changes in data modeling.
Irina is a data management practitioner with more than 10 years of experience. The key areas of her professional expertise are the implementation of data management frameworks and data lineage.
Throughout the years, she has worked for global institutions as well as large- and medium-sized organizations in different sectors, including but not limited to financial institutions, professional services, and IT companies.
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