Several well-known data management (DM) frameworks exist. However, many data management professionals worldwide still have similar questions regarding the following:
the scope of data management that fits a company’s needs and resources
the relevant framework that meets a company’s needs
the integrated approach to implementing multiple data management capabilities
In this article, I will:
demonstrate trends in applying DM frameworks
summarize the experiences of multiple data management professionals worldwide
Trends in applying data management frameworks
Several years in the raw, I published the same poll question on Linked. This poll has aimed to answer the important question: “Which guidelines have companies worldwide used to implement data management?” This poll offered four answers:
The underlying assumption is similar to the previous one.
DAMA-DMBOK2 or DCAM adjusted
This choice means a company adjusted one of these frameworks to their needs.
This option assumes that a company has developed its framework without using industry guidelines.
Figure 1 demonstrates the poll’s results and trends in using different frameworks.
Figure 1: Trends in using data management frameworks.
So, let me briefly summarize the results and trends:
DAMA-DMBOK2 remains the leading industry guide companies use worldwide in their practices.
When I saw these results, I interpreted them straightforwardly: a company applies the whole framework without changes. However, later, discussing the answers with respondents, I realized the situation was more complex. Firstly, they used this guideline for a limited number of capabilities. Secondly, they still adapted the guideline to their needs.
The trends demonstrate that the number of companies using DAMA-DMBOK2 in their practices has decreased over the last few years.
On average, only 8% of respondents used DCAM as a data management framework. The trend remains relatively stable.
More companies tend to develop their framework. In polls from 2021 and 2022, I combined “adjusted” and “self-developed” in one answer option.
When poll 2023 was closed, I approached the respondents with three questions. Below, I will share and summarize the answers received. They demonstrate the experiences of the poll’s participants in using various data management frameworks.
Summary of practical experience
I asked participants to share their experiences about the following:
The list of implemented capabilities
I will summarize the answers using these three categories.
The list of implemented capabilities
Figure 2 demonstrates the key implemented data management capabilities.
Figure 2: Implemented Data Management Capabilities.
One of the challenges with this analysis is that DAMA-DMBOK2 and DCAM define constituent components of data management quite differently. So, I had to make some adjustments. For example, the Data Strategy capability from DCAM belongs to Data Governance.
As you can see, the most often implemented data management capabilities are data governance, data quality, metadata and master data management, and data architecture. The choice of a framework does not influence the set of capabilities.
I want to share four of the most significant observations regarding the adjustment made:
Respondents indicated that both guidelines lack an integrated implementation approach for multiple capabilities. That is why companies developed the implementation approach themselves.
Company culture and practices
Because both guidelines provide a common approach, companies must adjust them to their cultures.
External consultants support
Companies invited external consultants to adjust a framework or develop their own one.
DAMA-DMBOK2 as a reference point
Companies used DAMA-DMBOK2 to adjust the existing frameworks or to develop their own. One of the participants shared that DAMA-DMBOK2 was used as a complimentary source to fill in the DCAM gaps.
Challenges associated with a framework implementation were similar for all approaches.
I’ve split challenges into several categories, shown in Figure 3:
Figure 3: The classification of challenges with implementation.
These challenges are associated with various factors:
A company and its culture
A company’s staff
Organization of the initiative
Let’s briefly consider each factor one by one.
Challenges associated with a company
Multiple participants indicated that their initiatives faced the following challenges:
Lack of leadership, vision, and adequate strategy and plan
Low business priorities for data management
Lack of resources
Structural and organizational insufficiencies
Challenges associated with people
Unwillingness and resistance to cultural changes
Lack of data literacy
Lack of support from knowledgeable staff
Unwillingness to take accountabilities
Challenges associated with the frameworks
Definitions of governance operating structure, including roles and their accountabilities, processes
Absence of guidance on the integrated implementation approach for multiple capabilities
Both frameworks are not operational
Measuring data management maturity
Difficulties with the definitions of topics
Challenges associated with the organization of the initiative
Absence of supported technology
Unclear location in the organizational structure for data governance function
Finding use cases to demonstrate data management advantages
A data management initiative is considered to be a project
Businesspeople do not recognize the importance of data management
Difficulties in distributing roles and accountabilities between functional teams
Rapidly developing technologies
We can conclude that, independently of the chosen framework, companies have a lot in common:
They implement similar common data management capabilities
Experience similar challenges
It would be great if you could share your experience with us.
For more information on this subject, visit Data Crossroads Online Academy: //academy.datacrossroads.nl
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