Data Crossroads has published the Data Management Maturity Assessment Review for a second year in a row. In the first article of the series, we have explained the methodology to measure maturity. Then, we have 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
Information systems architecture
In the “Orange” model of data management, the information systems architecture is a business capability that delivers data and application architecture artifacts required for the design of data chains. Report, data, and application flows are the most important outcomes.
Four components enable this capability. These are process, role, data/deliverable, and tool. Each component consists of a set of items. The full list of items per component is shown in Figure 1.
The logic behind this model is simple. For example, an application and data set flow is 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 take a 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 have seen the general changes in data management maturity. The maturity of information systems architecture slightly worsened in 2020 compared to 2019, as shown in Figure 2.
Some movements between the maturity levels presented in Figure 3 provide some possible explanations to this fact.
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 the 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.
The number of respondents at “ad-hoc” and “in development” maturity levels has 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.
Indicator 2: “optimized 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 data processing. The trends with the application architecture are positive as shown in Figure 5.
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 is 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 the management of master and reference data have a positive character, as shown in Figure 6.
The most significant trend is the increase of companies at the 4th and 5th levels of maturity. The significant reduction of companies with the “uncontrolled” level of maturity is in line with the positive trends.
An established enterprise architecture practice is the necessary condition 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.
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 is applicable for all four indicators
- An optimized information systems architecture should elaborate on the significant changes in the data processing. The volumes of data have increased drastically. New types of data require new ways of data processing. These changes have led to the 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 next article, we investigate the trends in the maturity of data value chains.
Compare the maturity status of your company by performing a Data Management Maturity Scan.