In the previous articles of this series, we have discussed how to build a company-specific data management maturity assessment and the way to benchmark the results. Now it is time for data governance maturity.
In this article, I will share an in-depth approach for measuring and benchmarking data management framework maturity (data governance maturity) sub-capability. Benchmark results used in this article have been based on ‘Data Management Maturity Review 2019’.
We will cover the following four topics:
- Definition of the ‘data management framework’ sub-capability and its dimensions
- Specification of indicators (KPIs) for measuring the performance
- Benchmarking results based on a set of indicators
- Development tips
Data management framework sub-capability and its dimensions
The data management framework is one of the five sub-capabilities of the ‘Orange’ model of data management that is explained in Data Management Maturity 101: What is a data management maturity assessment and why does a company need it? The overview of the model is shown in Figure 1.
The data management framework is a business capability that delivers the structure in which all other data management sub-capabilities operate. Rules (strategy, policy, process, etc.) and roles are the core components of the framework.
The following dimensions enable a (sub) capability: role, process, data (input and output), and tools.
In Figure 2, each dimension of the data management framework sub-capability is described in detail.
In our context, ‘data’ stands for formal deliverables/artifacts of the data management sub-capability. The key deliverables of this sub-capability are related to the rules and roles that ensure the operation of the data management function.
To make a data management initiative feasible, a company should limit it by specifying one or two key business drivers. A company does not set up data management ‘just for fun.’ It does so after experiencing a strong necessity to do it for reasons such as compliance with regulations, improvement of decision-making, improvement of customer experience, etc. Each such business driver will specify the required set of deliverables limited by a related set of data. Data (management) principles specify how a company will deal with data to produce the required deliverables.
The data management function will require a specification of its place in the organizational structure and corresponding operating model. To know how to achieve the specified deliverables, a company needs to estimate the current and desired status of data management.
Maturity assessment is one of the means to do so. Such an assessment will lead to the development of strategy, roadmaps, and plans. The operating model will be implemented based on a set of data management–related policies, standards, processes, procedures, and tasks. Data management-related roles will perform these tasks.
‘Process’ signifies a data management-related business process at different levels of abstraction.
There are key high-level processes to be developed and implemented. These processes will focus on making the data management framework operational. Data management initiatives should be planned and the progress of these initiatives should be followed. Processes will be shaped according to the project management approach of the company. Design, approval, and implementation of data management rules and roles will also require dedicated processes. The resolution of the data-related issues should be a separate process.
‘Role’ describes the participation of people in business operations. It can represent business units, functional jobs, a set of data management-related accountabilities and responsibilities (in RACI context), etc.
Usually, all data management roles represent a set of data management-related accountabilities. These accountabilities and responsibilities are spread between data management/IT professionals and subject matter experts from business departments. Even if the industry reference guide DAMA-DMBOK2 offers some solutions regarding roles, still each company usually creates a set of roles that fits the company culture, understanding of data management, etc. I shared my vision on data-management roles and their design in one of my previous articles.
‘Tools’ include information technology systems and applications as well as resources required for performing the data management function, e.g. budget.
Almost all departments within a company are involved in data management-related activities. Therefore, it is very important to share and maintain centrally related processes and artifacts.
Processes could be documented in business process management (BPM) applications. MS PowerPoint and Excel are also tools suitable for this purpose. Maintenance of the roles’ descriptions could form a part of HR documentation. Of course, it is highly advisable to use a centralized data management/governance tool where all artifacts can be mapped with each other.
Specification of indicators (KPIs) to measure the performance
Each of the sub-capability dimensions described above can serve as a specific indicator (KPI) to measure performance.
By assigning maturity levels to chosen indicators each company can create its maturity assessment.
I will demonstrate four indicators as examples. These indicators assist in measuring data governance maturity. They have been used as the foundation of our Data Management Maturity Scan:
Indicator 1 (Data): ‘an information/data policy and processes in place’
Data policy and processes are one of the key deliverables of a data management framework that form the basis for data management operations.
Indicator 2 (Process): ‘presence of the formal data management function’
All processes mentioned earlier can be performed only in case of the existence of a formal data management function.
Indicator 3 (Role): ‘awareness and support of the top management to data management initiatives’
Top management is one of the key data stakeholders. Their support of data management is one of the key success factors in any data management initiative. It is not always the case. Therefore, data management professionals should work to gain active involvement of the company’s top management.
Indicator 4 (Tools): ‘a dedicated budget for data management initiatives’
Data management will require monetary and staff resources. Therefore, a dedicated budget is also a success factor of any data management initiative.
Benchmarking information is available for each of these indicators.
Below you will find the benchmarking results for the four above-mentioned indicators (KPIs). You can use these four indicators to quickly benchmark the situation in your company against.
Each of the indicators has been evaluated at one of five maturity levels, that demonstrate the level of development.
Figure 3. Benchmarking results for the maturity of ‘data management framework’ (data governance) sub-capability
The results presented in Figure 3, led us to the following conclusions:
- Up to 38% of respondents still do not have a formal data management framework in place. You can see it based on the maturity levels of Indicators 1 and 2.
- 33 % of respondents are still in development. And only about 30% of respondents are in the process or have already established an operational framework.
- Only 40% of respondents got the support of top management for their data management initiatives.
- The situation with the budget looks slightly better than the situation with the support of management. The majority of respondents seem to have received (or are in the process of receiving) the required budget.
To improve the situation with the data management framework (data governance) companies should:
…put the effort in creating awareness of the necessity of proper management of data among all data stakeholders, with the main focus on top management.
…concentrate on the design and implementation of feasible data management strategies, policies, standards, processes, procedures, tasks, and corresponding deliverables.
…create an effective set of data management-related roles and map the accountabilities connected to these roles to data management processes, tasks, and deliverables.
In the next article, the same analysis will be provided for Data Modeling capability.