Growing a BI Solution Architecture Step by Step

The boss needs a new evaluation based on a new data source. There isn’t time to load the data into an existing data warehouse, let alone into a new one. In this article I introduce the idea of architectural T-shirt sizes. Of course, different requirements lead to different architectural approaches, but at the same time, it should be possible that architecture can grow as a BI system is expanded.

T-shirt sizes for BI solutions

The boss needs a new evaluation based on a new data source. There isn’t time to load the data into an existing data warehouse, let alone into a new one. It seems obvious to reach for “agile” BI tools such as Excel or Power BI. After several months and more “urgent” evaluations, a maintenance nightmare threatens. But there is another way: In this article I introduce the idea of architectural T-shirt sizes, illustrated using our own dashboard. Of course, different requirements lead to different architectural approaches, but at the same time, it should be possible that architecture can grow as a BI system is expanded.

The summer holidays were just over, and I was about to take on a new management function within our company. It was clear to me: I wanted to make my decisions in a data-driven way and to offer my employees this opportunity. From an earlier trial, I already knew how to extract the required data from our SAP system. As a BI expert, I would love to have a data warehouse (DWH) with automated loading processes and all the rest. The problem was that, as a small business, we ourselves had no real BI infrastructure. And alongside attending to ongoing customer projects, my own time was pretty tight. So did the BI project die? Of course not. I just helped myself with the tool I had to hand, in this case Microsoft Power BI. Within a few hours the first dashboard was ready, and it was published in our cloud service even faster.

The Drawbacks of Quick and Dirty

My problem was solved in the short term. I could make daily-updated data available to my employees. Further requirements followed, so I copied the power BI file and began fitting it in here and there. Over the next few weeks, I added new key figures to it and made more copies. However, I was only partly able to keep the various dashboard copies “in sync”. In addition, operational problems came up. Of course, I had connected the SAP system under my personal username, whose password has to be changed regularly. This in turn led to interruptions to the data updating and required manual effort in reconfiguring the new password in Power BI.

T-Shirt Sizes for Step-by-Step Development of BI Architecture

I guess a lot of professional users have been in my shoes. You have to find a solution on the quick – and before you know it, you’re in your own personal maintenance nightmare. At the same time, a fully developed BI solution is a distant prospect, usually for organizational or financial reasons.

As an expert in the adaptation of agile methods for BI projects, I have been dealing with this problem for a long time: How can we both address the short-term needs of the professional user and create the sustainability of a “clean” solution? As a father of two daughters, I remembered how my children grew up – including the fact that you regularly have to buy bigger clothes. The growth process is continuous, but from time to time we have to get a new dress size. It is exactly this principle that can also be applied to BI solutions and their architecture. Figure 1 shows four example T-shirt sizes, which are explained in more detail below.

Figure1
Figure 1 Architectural solutions in T-shirt sizes at a glance
Think Big, Start Small: The S-Size T-Shirt

This approach corresponds to my first set of dashboards. A BI front-end tool connects directly to a source. All metadata, such as access data for the source systems, business rules, and key figure definitions, are developed and stored directly in the context of the information products created.

This T-shirt size is suitable for a BI project that is still in its infancy. It is often only then that a broader aim is formulated involving everything that you would like to analyze and evaluate – this is when “thinking big” begins. Practically, however, not much more known than the data source. But you would also like to be more explorative and produce first results promptly, so it makes sense to begin with the small and technically undemanding.

However, this approach reaches its limits very quickly. Here is a list, by no means complete, of criteria for deciding when the time has come to think about the next architectural dress size:

  • There exist several similar information products or data queries that use the same key figures and attributes over and over again.
  • Different users regularly access the information products, but access to the data source is through the personal access account of the original developer.
  • The source system suffers from multiple, and mostly very similar, data queries of the various information products.
Developing a Common Language: The M-Size T-Shirt

In this size, we try to save commonly useful metadata such as key figure definitions and the access information for the source systems at its own level rather than the  information product itself. This level is often also called the data mart or the semantic layer. In the case of my own dashboard, we developed a tabular model for it in Azure Analysis Services (AAS). The various specifications or copies of the dashboards as such in large part remained – only the substructure changed. However, all variants now rested on the same central foundation. The advantages of this T-shirt size in comparison to the previous are clear: Your maintenance effort is considerably reduced, because the basic data is stored centrally once and does not need to be maintained for every single information product. At the same time, you bring consistency to the definition and designation of the key figures. In a multilingual environment, the added value becomes even more apparent, because translations are centralized only once and maintained uniformly in the semantic layer. All your dashboards thus speak a common language.

In this M-size t-shirt, we still do not store any data permanently outside the source. Even if the source data is transferred to the tabular model in AAS, it must be re-imported for larger adjustments to the model. Other manufacturers’ products come completely without data storage, for example, the Universes in SAP BusinessObjects. This means that a high load is sometimes applied to the source systems, especially during the development phase. Here is a list of possible reasons to give your “BI child” the next largest dress size:

  • The load on the source systems is still too large despite the semantic layer and should be further reduced.
  • The BI solution is also to be used as an archive for the source data, for example in the case of web-based data sources where the history is only available for a limited period of time.
  • If the source system itself does not historize changes to data records, this can be interpreted as another reason for using the BI solution as a data archive.
Decoupling from the Source: The L-Size T-Shirt

The next larger T-shirt for our BI architecture, the L-size, replaces the direct data access of the data mart to the source. To do this, the data is extracted from the source and permanently stored in a separate database. This level corresponds to the concepts of a persistent staging area (PSA) (see also [Kim04] pp. 31ff. and [Vos]) or an actively managed data lake (see also [Gor19], for example p. 9). They all have one thing in common: that the data is taken from the source with as little change as possible and stored permanently. This procedure means that the existing data mart can be reassigned to this new source relatively easily. In my own dashboard example, we’re not at this stage yet. But in the next step, we have planned to extract the SAP data using the Azure Data Factory and store it permanently in an Azure SQL database. For people who, like us, use the ERP as a cloud solution, this layer reduces the lock-in effect of the ERP cloud provider. Other advantages of this persistent data storage beyond the source systems include the archiving and historization function it brings with it: Both new and changed data from the source are continuously stored. Deleted records can be marked accordingly. Technically, our data model becomes very close to the data model of the source we use, and under certain circumstances, we are already harmonizing some data types. While this layer can be realized practically and fast, there are also indicators here when to jump to the next T-shirt size:

  • The desired information products require integrated and harmonized data from several data sources.
  • The data quality of the source data is not sufficient and can’t simply be improved in the source system.
  • Key calculations should be saved permanently, for example for audit purposes.
Integrated, Harmonized and Historicized Data: The XL-Size T-Shirt

The next, and for the time being last, T-shirt, the XL-size extends the existing architecture to a classical data warehouse structure between PSA and a data mart. It foregrounds the integration and harmonization of master data from the various source systems. This is done using a central data model, which exists independently of the source used (for instance, a data vault model or a dimensional model). This enables systematic data quality controls to be carried out when initially loading and processing data. Historization concepts can also be integrated into the data here as required. The persistent storage of data in this DWH layer means it is also permanently available for audit purposes.

The various T-shirt sizes don’t only differ in the number of levels they use. To characterize the four architectural approaches more comprehensively, it’s worth taking a brief look at Damhof’s model of Data Management Quadrants [Dam]. I’ll do this in the next blog post.

Literature

[Dam] Damhof, Ronald: “Make data management a live issue for discussion throughout the organization”. https://prudenza.typepad.com/files/english—the-data-quadrant-model-interview-ronald-damhof.pdf , accessed on 22.11.2019

[Gor19] Gorelik, Alex: The Enterprise Big Data Lake. O’Reilly 2019

[Kim04] Kimball, Ralph: The data warehouse ETL toolkit: practical techniques for extracting, cleaning, conforming, and delivering data. Wiley 2004

[Vos] Vos, Roelant: “Why you really want a Persistent Staging Area in your Data Vault architecture”. 25.06.2016, http://roelantvos.com/blog/why-you-really-want-a-persistent-staging-area-in-your-data-vault-architecture/, accessed on 22.11.2019

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

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