Positioning Architecture T-Shirt-Sizes

In my previous post, I’ve introduced the idea of architecture t-shirt sizes to depict the idea that you BI architecture should growth with your requirements. In this blog post I position the four example t-shirt sizes on Damhof’s Data Management Quadrants.

T-Shirt Sizes in the Context of Data Management Quadrants

If you haven’t read my previous blog post, you should do so first.

In [Dam], Ronald Damhof describes a simple model for the positioning of data management projects in an organization. Here, he identifies two dimensions and four quadrants (see also Figure 1). On the x-axis, Damhof uses the terms push-pull strategy, known from business economics. This expresses how strongly the production process is controlled and individualized by demand. On the right or pull side, topic-specific data marts and from them information products such as reports and dashboards, for example, are developed in response to purely technical requirements. Agility and specialist knowledge are the key to this. The first two T-shirt sizes, S and M, can be categorized as belonging on this side. On the left or push side, the BI department connects various source systems and prepares the data in a data warehouse. The focus here is on economies of scale and deploying a stable basic infrastructure for BI in the company. Here we can see the other two T-shirt sizes, L and XL.

Figure2
Figure 2: Damhof’s Data Quadrant Model

On the y-axis, Damhof shows how an information system or product is produced. In the lower half, development is opportunistic. Developers and users are often identical here. For example, a current problem with data in Excel or with other tools is evaluated directly by the business user. This corresponds to the S-size T-shirt. As can be seen in my own case, the flexibility gained for research, innovation, and prototyping, for example, is at the expense of the uniformity and maintainability of results. If a specialist user leaves the company, knowledge about the analysis and the business rules applied is often lost.

In contrast, development in the upper half is systematic: developers and end users are typically different people. The data acquisition processes are largely automated and so do not depend on the presence of a specific person in daily operations. The data is highly reliable due to systematic quality assurance, and key figures are uniformly defined. The L- and XL-size T-shirts can be placed here in most cases.

The remaining T-shirt, the M-size, is somewhere “on the way” between quadrants IV and II. This means it is certainly also possible for a business user without IT support to implement a data mart. If the solution’s development and operation is also systematized, this approach can also be found in the second quadrant. This also shows that the architecture sizes are not only growing in terms of the number of levels used.

The positioning of the various T-shirt sizes in the quadrant model (see Figure 2) indicates two further movements.

  • The movement from bottom to top: We increase systematization by making the solution independent of the original professional user. In my own dashboard, for example, this was expressed by the fact that at some point data was no longer access using my personal SAP user name but using a technical account. Another aspect of systematization is the use of data modeling.
  • While my initial dashboard simply imported a wide table, in the tabular model the data was already dimensionally modelled.
  • The movement from right to left: While the first two T-shirt sizes are clearly dominated by technical requirements and the corresponding domain knowledge, further left increasing technical skills are required, for example to manage different data formats and types and to automate processes.
Figure3
Figure 2: T-Shirt Sizes in the Data Quadrant Model
Summary and Outlook

Let’s get this straight: BI solutions have to grow with their requirements. The architectural solutions shown in T-shirt sizes illustrate how this growth path can look in concrete terms. The DWH solution is built, so to speak, from top to bottom – we start with the pure information product and then build step by step up to the complete data warehouse architecture. The various architectural approaches can also be positioned in Ronald Damhof’s quadrant model: A new BI solution is often created in the fourth quadrant, where business users work exploratively with data and create the first versions of information products. If these prove successful, it is of particular importance to systematize and standardize their approach. At first, a data mart serves as a guarantor for a language used by various information products. Data decoupling from the source systems also allows further scaling of the development work. Finally, a data warehouse can be added to the previous levels to permanently merge data from different sources and, if required, make them permanently and historically available.

Organizations should aim to institutionalize the growth process of a BI solution. Business users can’t wait for every new data source to be integrated across multiple layers before it’s made available for reporting. On the other hand, individual solutions must be continuously systematized, gradually placed on a stable data foundation, and operated properly. The architecture approaches shown in T-shirt sizes provide some hints as to what this institutionalization could look like.

This article was first published in TDWI’s BI-SPEKTRUM 3/2019

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