Data Management Maturity 2020: Information Systems Architecture
Data Management Maturity 2020: Information Systems Architecture
Data Crossroads has published the Data Management Maturity Assessment Review for the second year in a row. In the first article of the series, we have explained the methodology to measure maturity. Then, we demonstrated the general trends in data management, data governance, and data modeling. Here we will focus on trends in information systems architecture.
In this article, we will:
Explain the structure of this capability
Demonstrate the general trends in data systems architecture
Investigate changes in four key performance indicators
Four components enable this capability. These are process, role, data/deliverable, and tool. Each component consists of a set of items. The complete list of items per component is shown in Figure 1.
Figure 1: Information systems architecture components in detail.
The logic behind this model is simple. For example, an application and data set flow a deliverable of this capability. A process to develop, document, and maintain such flows delivers this artifact. Data and application architects take part in this process and produce these flows. An enterprise data architecture tool assists in designing, integrating, and maintaining such flows.
Now let us look at the general trends in information systems architecture maturity.
General trends in information systems architecture maturity
In the first article of this series, we see the available changes in data management maturity. The maturity of information systems architecture slightly worsened in 2020 compared to 2019, as shown in Figure 2.
Figure 2: The comparison of the maturity levels per DM sub-capability.
Some movements between the maturity levels presented in Figure 3 provide some possible explanations for this fact.
Figure 3: The changes per maturity level.
The number of companies at the “uncontrolled” level has reduced. This is a positive mark. The number of companies at “ad-hoc,” “in development,” and “capable” status has slightly increased. This fact is also positive. At the same time, the percentage of the “effective” maturity level has crimped. So the trends are mixed.
The “Orange” maturity scan uses four performance indicators to assess the maturity of this capability. Let us take a look in-depth at each of them.
Indicator 1: “reporting practices.”
Many companies still deliver information in the form of reports. Sometimes, the number of reports exceeds reasonable limits. Often, reporting staff does a lot of manual operations with report figures. The fewer manual interventions, the higher the level of maturity. The trends with the reporting practices look very positive, as shown in Figure 4.
Figure 4: Trends in the development of reporting practices.
The number of respondents at “ad-hoc” and “in development” maturity levels have significantly reduced. At the same time, the number of respondents at the two highest levels, “capable” and “effective,” have grown convincingly.
Reporting practices are closely connected to the quality of data and application architecture.
The optimization of data and application architecture requires time and resources. It assumes the substitution of legacy software and the reduction of redundancies in the data processing. The trends with the application architecture are positive, as shown in Figure 5.
Figure 5: Trends in the development of optimized application architecture.
More companies have achieved the two highest levels of maturity. The number of companies at the “uncontrolled” level has reduced significantly.
The architecture solutions for master and reference data indicate the overall maturity of the information systems architecture.
Indicator 3: “master and reference data management.”
Master and reference data are used across the whole company. Therefore, the management of these data types has a higher priority. The optimization of information systems architecture closely relates to the optimization of reference and master data architecture. The trends in managing master and reference data have a positive character, as shown in Figure 6.
Figure 6: Trends in the development of optimized application architecture.
The most significant trend is the increase of companies at the 4th and 5th levels of maturity. The substantial reduction in companies with an “uncontrolled” level of maturity is in line with the positive trends.
An established enterprise architecture practice is necessary to optimize information systems architecture.
Indicator 4: “enterprise architecture function.”
Each company that develops data management practices should have a formal enterprise function in place. This function implements processes and produces artifacts to optimize enterprise architecture.
The situation with the formal enterprise architecture function improved in 2020, as demonstrated in Figure 7. The number of companies that did not have such a function has reduced substantially. At the same time, the number of companies that develop or implement this function has grown.
Figure 7: Trends in the establishment of an enterprise architecture function.
The demonstrated results have brought us to the following conclusions.
The general level of information system architecture maturity slightly worsened in 2020 compared to 2019.
At the same time, trends remain positive.
All four indicators have demonstrated positive trends.
The following facts express the positive trends:
The number of respondents with the lowest level of maturity has significantly decreased for each of the four indicators
The number of companies that have reached the “capable” or “effective” level has increased. The situation applies to all four indicators.
An optimized information systems architecture should elaborate on the significant changes in data processing. The volume of data has increased drastically. New types of data require new ways of data processing. These changes have led to new requirements for the underlying technology. All of it leads to an increase in IT operational costs.
To keep costs under control and ensure effective data processing, companies should:
Optimize the data delivery to meet information requirements most critical to the business
Improve master and data reference architecture to avoid data redundancy
Elaborate on new technologies in application systems design
Implement the “data fabric” concepts
In the following article, we investigate the trends in the maturity of data value chains.
Irina is a data management practitioner with more than 10 years of experience. The key areas of her professional expertise are the implementation of data management frameworks and data lineage.
Throughout the years, she has worked for global institutions as well as large- and medium-sized organizations in different sectors, including but not limited to financial institutions, professional services, and IT companies.
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