Are you interested in benchmarking your data management progress? Then take a look at the data management maturity 2019 review results.
In the first article of the Data Maturity 101 series, I demonstrated how a company could develop its data management maturity assessment and map it to a standard model to benchmark the results. Finding benchmarking results can also be a challenging task.
In this article, I will present some of the results you could use for benchmarking. These results are part of the’Data Management Maturity Assessment Review 2019’, published recently. This review has been based on the results of the data management maturity scan performed by participants on an anonymous and free basis on our website. The results were obtained between January and October 2019. You can download the review in full here.
An overview of the methodology for measuring data management maturity
The maturity scan applies the ‘Orange’ data management model developed by Data Crossroads and is described in the first article of the series.
Data management is considered a business capability that safeguards a company’s data and information resources and optimizes data and information value chains (‘data chain’) to ensure the effective conduction of the business.
The set of key data management sub-capabilities ensures the optimized design of these chains. These sub-capabilities traditionally include but are not limited to data management framework (data governance), data modeling, information systems architecture, and data quality. The following dimensions enable a (sub) capability: role, process, data (input and output), and tools. The model of data management (sub) capabilities can be seen in Figure 1.
Figure 1. The structure of data management capability.
The scan has been developed to measure simultaneously the maturity level of data management capability, five sub-capabilities, and each of four dimensions that characterize a (sub-)capability. For this measurement, a set of indicators has been used. Each sub-capability has its own set of indicators. The model of the maturity level measurement is shown in Figure 2.
Figure 2. The maturity measurement approach
In this article, we will briefly review the results of the overall data management capability and its four dimensions.
The maturity level of the data management (DM) capability
The results have shown that each company that performed the maturity scan has formally established a data management function. The management of data has become an unavoidable part of business operations. 16% of participants claimed that DM capability still was at the ad-hoc stage. 77% of companies were at the design or implementation stage. And only 7% of participants enjoyed a fully operational data management function.
The graphical representation of the results can be seen in Figure 3.
Figure 3. Distribution of DM capability maturity levels among participants.
Four dimensions enable the operations of the data management function. These are role, process, data, and tools.
The maturity level of data management capability dimensions
Role
The dimension ‘role’ describes the participation of people in business operations. The role can represent business units, functional jobs, a set of data management-related accountabilities, etc. The status of the maturity level of data management-related roles can be seen in Figure 4.
Figure 4. Distribution of maturity levels of the dimension ‘role.’
The situation when 8% of respondents do not have any formal data management roles and 26% have informal roles demonstrates potential areas for improvement. Only 28% of respondents claim to be either in the ‘in implementation’ phase or having roles in the ‘operational’ phase.
Process
The process signifies a data management-related business process at different levels of abstraction.
The status of maturity levels of the process dimension can be seen in Figure 5.
Figure 5. Distribution of maturity levels of the dimension ‘process.’
The analysis shows that, in total, 47% of companies do not have formal processes in place.
32% are busy with design, and only 21% are either currently implementing or already have operational processes in place.
Data
In the context of this review, data stands for formal deliverables/artifacts of the data management capability.
The status of maturity levels of the data dimension can be seen in Figure 6.
Figure 6. Distribution of maturity levels of the dimension ‘data.’
Notably, 44% of respondents declared that they don’t deliver any artifacts or do it in an “informal” manner. 32% of companies focus on the design of formal deliverables. Only 24% of companies demonstrate that they either implement or already have formal deliverables.
Tools
Tools include information technology systems, applications, and resources required to perform the data management function, i.e., budget.
The status of maturity levels of the tools’ dimension can be seen in Figure 7.
Figure 7. Distribution of maturity levels of the dimension ‘tools.’
In this analysis, tools describe IT applications and other assets. The distribution of maturity
levels has a very positive trend compared to data and process dimensions.
Conclusion
We all know that effective data management requires a balanced structure of supporting processes, people, tools, and deliverables.
The common trend is that most companies have developed the required processes and possess enough resources to move on with data management initiatives. Implementing these processes into practice is still a challenge that needs to be worked on.
If you want to see the relationships between the overall level of data management maturity and each dimension, please consult the Data Management Maturity Assessment Review 2019. You can download it for free here.
In the following articles of the series, I will demonstrate how to develop a set of indicators (KPIs) per each data management sub-capability to measure and benchmark maturity.
For more insights, visit the Data Crossroads Academy site:
