Understanding your organization’s data management maturity level is critical to achieving business success in today’s data-driven world. As businesses increasingly rely on data to make decisions, it’s important to evaluate the maturity of your data management capabilities to ensure they are effective, efficient, and up to date. Whether you’re just starting to build your data management framework or looking to improve an existing one, a data management maturity review can help you achieve your data management goals and drive business success.
Data Crossroads has issued the Data Management Maturity (DMM) Review for the fourth year in a row. You can download this Review HERE.
In this article, I will:
- Provide the background information about this DMM Review
- Demonstrate key trends in the developing core data management capabilities for the last four years
- Show the easiest way to measure and benchmark your company’s maturity
DMM Review Background
The DMM Review is based on the “Orange” data management framework developed by Data Crossroads. This practice-based framework assists in designing, implementing, and measuring the maturity and performance of core data management capabilities. This framework consists of the following components:
- A data management capability model and corresponding maturity model
- Metadata, data lineage, and knowledge graph metamodels
- An integrated implementation method for six core capabilities
- Multiple templates for data management artifacts, policies, standards, roles, and plans
You can find more about this framework in the book, “The ‘Orange’ Data Management Framework,” and the series of articles.
This model has formed a basis for a simplified anonymous Data Management Maturity Scan offered at the Data Crossroads site. The DMM Review is based on the results of this scan completed by hundreds of respondents globally.
The goals of the DMM Review are the following:
- Demonstrate the status of data management among companies worldwide
- Assist companies in comparing their performance with that of their peers
- Provide a comparison between data management maturity levels in 2019, 2020, 2021, and 2022
The maturity assessment is based on the following principles:
- The DMM Scan measures the maturity level of the following:
- Overall data management maturity
- Maturity of 5 core data management sub-capabilities: data governance, modeling, quality, information systems architecture, and data chain management
- The components of each sub-capability (role, process, tool, and data)
- The “Orange” DMF recognizes five maturity levels (1st is the lowest).
- The scan measures the maturity of DM capability components. It integrates the results to the upper levels of the data management sub-capabilities and the whole DM capability.
Key Trends in Developing Data Management
Figure 1 demonstrates the general trends in the maturity levels of the DMM scan respondents.
Figure 1: Trends in the maturity of the data management capability.
From 2019 to 2021, the maturity level of the overall data management capability demonstrated has steadily grown. However, the results of 2022 have broken this dynamic. The 2022 audience seems less mature compared to the previous years:
- More companies have maturity Level 3.
- The number of respondents with maturity Levels 4 and 5 has decreased.
Key Trends in Developing Data Management Sub-Capabilities
Figure 2 illustrates trends in the maturity levels for each of the five data management (DM) sub-capabilities: data chain management, data management framework, data modeling, information systems architecture, and data quality.
Figure 2: Trends in the maturity of the data management sub-capabilities.
The maturity levels of each sub-capabilities mentioned above in 2022 have remained similar compared to previous years.
Data chain management
Figure 3 illustrates some improvements in the results of 2022 compared to those of 2021:
- The number of participants at the two lowest levels has decreased.
- The number of participants in the “capable” and “effective” levels has increased.
Figure 3: The comparison of the maturity levels of the data chain management.
Data Management Framework
Figure 4 illustrates the dynamic deterioration of the “data management framework” sub-capability.
The results demonstrate a visible decrease in the maturity of the data management framework compared to the 2021 results:
- The percentage of respondents with the status “uncontrolled” and “ad-hoc” has remained on the same level.
- The percentage of respondents with the status “in development,” “capable,” and “effective” has slightly decreased.
Figure 4: The comparison of the maturity levels of the data management framework
Data Modeling Capability
You can also see trends of worsening in the maturity levels of data modeling sub-capability, as demonstrated in Figure 5. These trends mirror the trends we saw in the “data management framework” capability:
- The percentage of respondents with the status “ad-hoc” and “in development” has increased.
- The percentage of respondents with the status “capable” and “effective” has decreased.
- The number of respondents at the “uncontrolled” level has remained in 2022 compared to 2021.
Figure 5: The comparison of the maturity levels of the data modeling capability.
Information Systems Architecture
The dynamic of the maturity of this capability is similar to that of the above-discussed capabilities: the level of maturity has worsened compared to 2021. The percentage of respondents at the lowest levels has increased, and the percentage of respondents at the two highest levels has decreased slightly, as shown in Figure 6.
Figure 6: The comparison of the maturity levels of the “information systems architecture” capability.
Data Quality
The trend of maturity levels worsening can also be seen in the “data quality” capability, illustrated in Figure 7. The number of respondents at the “uncontrolled” and “ad-hoc” has increased, and the “capable” and “effective” levels remain the same compared to 2020 and 2021.
Figure 7: The comparison of the maturity levels of the “data quality” capability.
Benchmarking Your Company’s Results
You can use the Data Management Maturity Scan to benchmark the results of your company.
By conducting this scan, you can better understand your organization’s data capabilities and develop a roadmap for enhancing your data management processes to meet business needs.
Understanding your organization’s data management maturity level is critical to achieving business success in today’s data-driven world. As businesses increasingly rely on data to make decisions, it’s important to evaluate the maturity of your data management capabilities to ensure they are effective, efficient, and up to date. Whether you’re just starting to build your data management framework or looking to improve an existing one, a data management maturity review can help you achieve your data management goals and drive business success.
Data Crossroads has issued the Data Management Maturity (DMM) Review for the fourth year in a row. You can download this Review HERE.
In this article, I will:
- Provide the background information about this DMM Review
- Demonstrate key trends in the developing core data management capabilities for the last four years
- Show the easiest way to measure and benchmark your company’s maturity
DMM Review Background
The DMM Review is based on the “Orange” data management framework developed by Data Crossroads. This practice-based framework assists in designing, implementing, and measuring the maturity and performance of core data management capabilities. This framework consists of the following components:
- A data management capability model and corresponding maturity model
- Metadata, data lineage, and knowledge graph metamodels
- An integrated implementation method for six core capabilities
- Multiple templates for data management artifacts, policies, standards, roles, and plans
You can find more about this framework in the book, “The ‘Orange’ Data Management Framework,” and the series of articles.
This model has formed a basis for a simplified anonymous Data Management Maturity Scan offered at the Data Crossroads site. The DMM Review is based on the results of this scan completed by hundreds of respondents globally.
The goals of the DMM Review are the following:
- Demonstrate the status of data management among companies worldwide
- Assist companies in comparing their performance with that of their peers
- Provide a comparison between data management maturity levels in 2019, 2020, 2021, and 2022
The maturity assessment is based on the following principles:
- The DMM Scan measures the maturity level of the following:
- Overall data management maturity
- Maturity of 5 core data management sub-capabilities: data governance, modeling, quality, information systems architecture, and data chain management
- The components of each sub-capability (role, process, tool, and data)
- The “Orange” DMF recognizes five maturity levels (1st is the lowest).
- The scan measures the maturity of DM capability components. It integrates the results to the upper levels of the data management sub-capabilities and the whole DM capability.
Key Trends in Developing Data Management
Figure 1 demonstrates the general trends in the maturity levels of the DMM scan respondents.
Figure 1: Trends in the maturity of the data management capability.
From 2019 to 2021, the maturity level of the overall data management capability demonstrated has steadily grown. However, the results of 2022 have broken this dynamic. The 2022 audience seems less mature compared to the previous years:
- More companies have maturity Level 3.
- The number of respondents with maturity Levels 4 and 5 has decreased.
Key Trends in Developing Data Management Sub-Capabilities
Figure 2 illustrates trends in the maturity levels for each of the five data management (DM) sub-capabilities: data chain management, data management framework, data modeling, information systems architecture, and data quality.
Figure 2: Trends in the maturity of the data management sub-capabilities.
The maturity levels of each sub-capabilities mentioned above in 2022 have remained similar compared to previous years.
Data chain management
Figure 3 illustrates some improvements in the results of 2022 compared to those of 2021:
- The number of participants at the two lowest levels has decreased.
- The number of participants in the “capable” and “effective” levels has increased.
Figure 3: The comparison of the maturity levels of the data chain management.
Data Management Framework
Figure 4 illustrates the dynamic deterioration of the “data management framework” sub-capability.
The results demonstrate a visible decrease in the maturity of the data management framework compared to the 2021 results:
- The percentage of respondents with the status “uncontrolled” and “ad-hoc” has remained on the same level.
- The percentage of respondents with the status “in development,” “capable,” and “effective” has slightly decreased.
Figure 4: The comparison of the maturity levels of the data management framework
Data Modeling Capability
You can also see trends of worsening in the maturity levels of data modeling sub-capability, as demonstrated in Figure 5. These trends mirror the trends we saw in the “data management framework” capability:
- The percentage of respondents with the status “ad-hoc” and “in development” has increased.
- The percentage of respondents with the status “capable” and “effective” has decreased.
- The number of respondents at the “uncontrolled” level has remained in 2022 compared to 2021.
Figure 5: The comparison of the maturity levels of the data modeling capability.
Information Systems Architecture
The dynamic of the maturity of this capability is similar to that of the above-discussed capabilities: the level of maturity has worsened compared to 2021. The percentage of respondents at the lowest levels has increased, and the percentage of respondents at the two highest levels has decreased slightly, as shown in Figure 6.
Figure 6: The comparison of the maturity levels of the “information systems architecture” capability.
Data Quality
The trend of maturity levels worsening can also be seen in the “data quality” capability, illustrated in Figure 7. The number of respondents at the “uncontrolled” and “ad-hoc” has increased, and the “capable” and “effective” levels remain the same compared to 2020 and 2021.
Figure 7: The comparison of the maturity levels of the “data quality” capability.
Benchmarking Your Company’s Results
You can use the Data Management Maturity Scan to benchmark the results of your company.
By conducting this scan, you can better understand your organization’s data capabilities and develop a roadmap for enhancing your data management processes to meet business needs.
