Part 04: Core element technology

„Business Intelligence Technology includes software, hardware and middleware as well as instructions for the usage of it.“

This means that the element technology focuses on business intelligence software and hardware and gives a clear statement which technology and how it is used in the company. Furthermore, there has to be a tool strategy, a construction plan and an general overview about the architecture of the information Transportation.

For some people, between Business Intelligence (BI) and Data Warehousing (DWH) is huge difference and for others they are quite the same. In my humble opinion it depends on the educational background and the experience of each person. For me, it is important to bring a defintion before talking in depth about the characteristics of each terms.

My definition of Business Intelligence:

„Business Intelligence is an initiative in user companies in order to collect, transform and provide data for users to plan, control and steer the company to achieve the company`s goals.“

My definition of Data Warehousing:

„Data Warehousing includes the collection, transformation and the provision of consolidated data in conceptual, logical, and physical models to support business goals and end-user information needs.“

As you can see (or acutally read), Data Warehousing and Business Intelligence is pretty good match. Data Warehousing is part of the Business Intelligence initiative and is a success factor for it.

In the header, there is the part “architecture of an analytical landscape”. It stands for the business intelligence architecture which I will describe on a very high level. But this high level contemplation is the early stage before I will explain the different types of business intelligence architectures. In the figure below, you can see five layers Source System(s), Collecting & Transforming, Storage, Provisioning and Application(s).
BI architecture simple
Let´s start with the source system(s). A source system is an OLTP (Online Transaction Processes). OLTP is a class of systems that supports or facilitates high transaction-oriented applications. OLTP’s primary purpose is to control and run fundamental business tasks as well as handles inserts into rows of tables and updates short and fast initiated by end users. The database design is highly normalized with many tables. To shorten it and not explained in all details, OLTP systems are not made for business intelligence.

But the data in the OLTP systems are the base of the business intelligence environment which starts with the Collecting & Transforming Layer. In this layer, the data will be extracted out of the source systems, often transformed and loaded into the Storage Layer. The data is often pushed in packages (PK). It can be a full load or only a delta load (which means only changed data in the source system is pushed). The StorageLayer is often called the data warehouse, but that is totally correct. The Storage Layer stores the extracted and transformed data from the source systems in a neutral and independent way.

Only when data gets pushed to the Provisioning Layer, it will be transformed further and stored application related. The data in this layer can be stored in one or more data base(s) or only be referenced.

The Provisioning Layer is the starting point for the Application Layer. There are six general application classes which focus different approaches of presenting data and gaining information out of it. This six classes are

– DB: Dashboards
– SR: Standard Reports
– AD: Ad hoc Analysis
– AN: Analysis
– PS: Planning and Simulation
– DM: Data Mining

In the next weeks I will show the differnt types of BI architectures and explain the application classes in detail. After that I will match that with the Business Intelligence vendors portfolio.


About Tobias Riedner
Tobias Riedner foundet in 2015. He works and worked as innovator, consultant, analyst and educator in the fields of business intelligence and data warehousing. He learned a lot from the best consultants, managers und educators in the past and shares his knowledge worldwide. He works for a steady growing traditional company which is a leader in industry 4.0.