This series of articles demonstrates Data Management Maturity Trends 2020 based on the Data Management Maturity Review 202o published by Data Crossroads.
In this article, the first one of the series, I would like to:
- Introduce the methodology
- Review the general trends in data management maturity
Methodology
The results of a data management maturity scan published at the Data Crossroads provide input for The Data Management Maturity Review. More than 400 companies worldwide performed this scan on an anonymous, free-of-charge basis during the past two years. The Data Management Maturity Trends 2020 demonstrate the status of DM globally.
The “Orange” model forms the basis of this methodology. Data management is a business capability; “A business capability is a particular ability or capacity that a business may possess or exchange to achieve a specific purpose or outcome.” Data management supports business value chains in reaching business goals. For that, data management delivers three business values. Data management:
- Safeguards data assets
- Gets the value from data
- Delivers data required to perform business operations
Data management accomplishes this by designing and implementing data chains. Data chains realize a data lifecycle. A set of data management sub-capabilities enable data chains at different stages. This model can be seen in Figure 1.
Figure 1: The “Orange” model of data management.
Data management (DM) sub-capabilities form three groups: Core, IT-related, and support.
The core sub-capabilities marked in orange are data management framework (data governance), data quality, information systems architecture, and data modeling. IT-related sub-capabilities include application and technology architecture, data lifecycle management, etc. Business architecture is an example of supporting sub-capabilities. The light grey shapes illustrate IT-related and support sub-capabilities. Each company can define its own set of capabilities specific to its business.
Four components enable each business capability: role, process, data/outcome, and tool.
The “Orange” maturity model allows for measuring maturity at three levels. Figure 2 represents this model.
Figure 2: The “Orange” maturity assessment methodology.
The first level is one of the (sub)-capability components:
- (Sub)-capability components and corresponding items
Role, process, outcome, and tool are components of each sub-capability. Each component has a list of corresponding items. For example, a “process” component includes a list of data management processes. The “role” component contains the set of data management roles. Each item can have one of five statuses: “does not exist,” “informal,” “in design,” “in implementation,” and “operational.” Each status corresponds to one of the maturity levels.
A company assesses the level of maturity for a whole component or a particular item. The next level is the level of a specific data management sub-capability.
- Particular data management (DM) sub-capability
Data modeling or data quality are examples of a particular DM capability. Each sub-capabilities have components such as process, role, outcome, and tool. The “Orange” model aggregates these components’ results and calculates a sub-capability’s average maturity level.
The ultimate level is the maturity of the general data management capability.
- Data management capability
The “Orange” model aggregates the sub-capabilities’ maturity level and calculates the data management’s average maturity level.
More about the “Orange” model can be found in this set of articles.
Now let us examine the general trends in the maturity of data management.
General trends in data management maturity
The general Data Management Maturity Trends 2020 demonstrate the change in the maturity level of:
- Data management capability and its sub-capabilities
- Four components of data management capability
Before analyzing the trends, you should take the following factors into account:
- The number of respondents differs. In 2019, I considered 70 fully completed scan results, while in 2020, the number reached 233.
- The scan is anonymous, and I can’t trace whether the same companies used the same scan. Only new participants are likely to use this tool in 2020.
- The scans have been performed by participants from different countries worldwide. Professionals from 158 countries have visited my site. The top five countries on the list are the United States, the United Kingdom, the Netherlands, Australia, and Canada. The scan is anonymous, so I don’t have information regarding the participants’ backgrounds.
Now let us investigate the trends.
Data management capability and its sub-capabilities
Figure 3 presents the comparison of the data management maturity levels of respondents in 2020 and 2019.
Figure 3: The comparison of the distribution of DM capability maturity levels.
The audience that performed the maturity scan in 2020 demonstrated a higher level of maturity:
- Fewer companies have maturity Level 2
- The number of participants at Level 3 has grown
- The same number of participants declared being at Level 4 maturity.
There is a slight decrease in the percentage of respondents at Level 5.
The deeper analysis at the level of sub-capabilities demonstrates different trends, as shown in Figure 4.
Figure 4: The comparison of the maturity levels per DM sub-capability.
For example, the maturity levels of data quality and data management framework have almost remained unchanged. Data modeling demonstrated a slightly higher level of maturity. However, the data chains and information systems architecture had somewhat lower maturity levels in 2020. In the consequent articles, we will investigate these trends in-depth.
Now let us take a look at the changes in aggregated DM components.
Four components of data management capability
Four components enable a data management capability: process, roles, outcome, and tool.
The changes in maturity levels for these components can be seen in Figure 5.
Figure 5: The changes in maturity levels for DM components.
The trends look less optimistic. Companies have demonstrated improvements in the delivery of outcomes. At the same time, the readiness of roles, tools, and processes has gotten lower marks.
Let us investigate each component and start with the “process” component.
Process
“Process” signifies a data management-related business process at different levels of abstraction. Figure 6 demonstrates changes in the maturity levels of the “process.”
Figure 6: The comparison of the maturity levels of the “Process” DM component.
The changes in maturity levels in the main area look positive. The percentage of companies that do not have processes has reduced significantly. The number of companies at the “informal,” “in design,” and “in implementation” stages has grown.
So even if the overall level of the “process” level has decreased (Figure 5), the results per group demonstrate a positive dynamic (Figure 6).
The next component to analyze is “role.”
Role
Roles describe the participation of people in business operations. Roles can represent business units, functional jobs, etc.
In Figure 5, we have noticed a decrease in the “role” maturity level. The more detailed analysis in Figure 7 demonstrates the different trends.
Figure 7: The comparison of the maturity levels of the “Role” DM component.
- From one side, the trends for the status “does not exist,” “informal,” and “in design” are positive.
- Simultaneously, the number of companies with “in implementation” and “operational” levels has decreased.
- The next component to pay attention to is “data/outcome.”
Data/Outcome
- In the context of this review, data stands for formal outcomes of each data management (DM) sub-capability. As we have seen in Figure 5, the general trend is positive. Now let us take a more detailed look.
Figure 8 compares the maturity levels of the “data/outcome” DM component.
The situation with the “data/outcome” component demonstrates trends similar to the previous components. For the three lower levels, the dynamic is positive. For the higher levels of maturity, we see the situation worsening.
The last component of data management maturity is “tools.”
Tools
The component “tools” represent IT systems and applications. It also includes required resources, for example, budget. The overall situation with “tools” has worsened, as shown in Figure 5. In Figure 9, you will see the detailed overview.
Figure 9: The comparison of the maturity levels of the “tools” DM component.
The trends remain similar to the previously analyzed components: some improvements at the lower maturity levels while worsening at the higher levels. Now we can make conclusions.
Conclusions about general data management trends
The comparison of the maturity levels of data management and its components has shown mixed trends:
- The total level of maturity of the respondents increased in 2020 compared to 2019.
- The maturity levels of data management sub-capabilities have changed differently.
The levels of data management framework and data quality have remained unchanged. The situation with information systems architecture and data chains has worsened. Only data modeling has shown improvement.
- The situation with data management components has also demonstrated mixed dynamics.
The general maturity levels of roles, processes, and tools have degraded. Only the maturity level of outcomes has slightly increased.
At the same time, each component has positive dynamics for the first three lower levels of maturity. The number of respondents has moved from the first to the second and third levels.
In the upcoming articles, we will investigate in detail the trends for each data management capability. In the following article, you will read about the changes in the data governance/data management framework.
Compare the maturity status of your company by performing a Data Management Maturity Scan and downloading Data Management Maturity Review.
Will you compare the maturity of your company with The Data Management Maturity Trends 2020?
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