Data Warehousing and Business Intelligence

31 May 2011

Differences Between Data Warehousing and Business Intelligence

Filed under: Business Intelligence,Data Warehousing — Vincent Rainardi @ 6:38 pm
Tags: ,

Try asking your colleague what is the difference between business intelligence and a data warehouse. I find that a lot of people, even those who work in BI projects and BI industry, do not understand the difference. A lot of people use these 2 terms interchangeably. Some people even prefer to use 1 term instead of the other because it simply “sounds better”. Many people think that business intelligence is not just a data warehouse, but there is more to it. But when asked “what business intelligence systems are not data warehouse systems?” or “what part of business intelligence systems are not data warehouses?”, most of them have difficulties explaining the answer.

These days, “business intelligence” is the norm used by most vendors in the industry, rather than “data warehouse”. Most of them call / classify their tools as business intelligence software, not data warehouse software. The name of Cognos product is “Cognos 8 Business Intelligence”. BusinessObjects label themselves as “BI software company” and “global leader in BI software”. The name of one of Hyperionproducts is “Hyperion System 9 BI+”. SAS Enterprise BI Server provides a fully integrated and comprehensive suite of business intelligence software. Microsoft promotes SQL Server 2005 as the end-to-end business intelligence platform. It seems that only Kimball Group who consistently use the term data warehouse. Bill Inmon, as the inventor of this term, also uses the term data warehouse.

So, let’s get into the details. This is an example of a data warehouse system:

It includes ETL from the source system, front end applications (those 10 boxes on the right hand side), and everything in between. It has a control system, an audit system and a data quality system (also known as data firewall). Not all data warehouse systems have all the components pictured above, for example, some data warehouse system may not have operational data stored (ODS), see this article for details.

The 2 blue items are data warehouse databases. The cylinder is in relational format (labelled as dimensional data store, DDS for short), the box is in multidimensional format (labelled as cubes in the picture above). This blue cube is also known as on line analytical processing cube, or OLAP cube for short.

The yellow items are business intelligence applications. Most business intelligence applications take data from multidimensional format data warehouse, but some do take data from the relational format. The whole diagram above is also known as business intelligence system.

Some business intelligence applications take data directly from the source system. For example, some dashboard systems may get sales summary data from the source system and display it in gauge meter format. In this case, we can not call the system a data warehouse system. It is still a business intelligence system, but it is not a data warehouse system, because it does not have a data warehouse database behind the gauge meter application.

Business intelligence systems, in the past also known as executive information systems, or decision support systems, are a non-transactional IT system used to support business decision making and solve management problems, normally used by top executives and managers. Many varied definitions exist in the market place today about the business intelligence system; one from Dr. Jay Liebowitz is arguably one of the better ones. Most people agree that OLAP and data warehouse systems are a major and important part of business intelligence systems. Most business intelligence systems are in the form of a data warehouse systems. Yes, there are business intelligence systems that do not use OLAP or data warehouses, as illustrated in the example of gauge meter application above, but they are more rare than the ones with OLAP or a data warehouse.

According to Ralph Kimball, in his book The Data Warehouse ETL Toolkit, a data warehouse is a system that extracts, cleans, conforms, and delivers source data into a dimensional data store and then supports and implements querying and analysis for the purpose of decision making. He stressed that a data warehouse is not a product, a language, a project, a data model or a copy of transaction system. In an interview with Professional Association for SQL Server (PASS) on 30th April 2004, he explained about the relationship between data warehousing and business intelligence.

In their latest book, The Microsoft Data Warehouse Toolkit, Joy Mundy and Warren Thornthwaite do not differentiate data warehouse systems and business intelligence systems. They consistently use the term DW/BI system throughout the book. This is understandable because, as I describe above, most business intelligence systems are in the form of a data warehouse system.

Bill Inmon, who invented the term data warehouse, defines data warehouse as a source of data that is subject oriented, integrated, nonvolatile and time variant for the purpose of management’s decision processes. He pointed that the term data warehouse was never trademarked or copyrighted. As a result, anyone can call anything a data warehouse. He recently defined a new term, DW 2.0, and this one is trademarked so nobody can change the definition. He explained the architecture in his article in dmreview, along with the differences between the first generation of data warehouses and DW 2.0 and its advantages.

So, as a summary, back to the original question, what is the difference between data warehouse and business intelligence? Most business intelligence systems are based on data warehouse systems (the one with dimensional model, fact tables, dimension, etc), but some business intelligence systems are not data warehousing, i.e. taking data directly from the source system, like the example described above. Business intelligence application (as opposed to business intelligence system) is the yellow boxes on the diagram above, i.e. the front end applications. The data warehouse database (or sometimes people dropped the word database, so it becomes just ‘data warehouse’) is the blue cylinder and blue box on the diagram above, i.e. the dimensional storage, whether in relational database format or in multidimensional database format.

If people say ‘data warehouse’, be careful because it can mean either data warehouse system (the whole diagram above) or data warehouse database (just the blue items). If people say ‘business intelligence’, it can mean either business intelligence system (the whole diagram above, or a BI system without data warehouse) or business intelligence application (the yellow boxes).

I hope this article makes the terms clearer, but I am open to comments and suggestions. As Ralph Kimball said, if you ask 10 different people what data warehouse is you are likely to get 10 different answers.

Vincent Rainardi
1st May 2006

This is a repost from SQLServerCentral.


  1. Hi Vincent~

    so the relationship between them is:
    1. BI and DW are NOT necessary for each other. (BI can be built without DW, DW can be built not for BI, but CRM or something else)
    2. BI and DW can make each other more useful. (BI can make data in DW valuable for reporting / mining / analysis, DW can make BI reliable and efficient, especially when there’re lots of sources)

    Is this understanding right?

    Comment by Sid — 1 June 2011 @ 10:50 am | Reply

  2. Hi Vincent,
    Thanks for this nice article, since you are an expert in this field I’d like to ask you this question. is it a good idea to have your staging databases on completely different server. I don’t like this idea because a lot of time when I stage the data I can just use store proc to move data to DW but some one in my team wants to this and he has the power to do so. I could not find anything online to talk about the architect of DW environment.

    Thanks in advance for help.

    Comment by Mehrdad Abdi — 9 June 2015 @ 12:39 pm | Reply

    • Hi Mehrdad, it is unusual for the staging database to reside on a different server. Usually the staging DB is located on the same server as the DW. The most common reason is cost, particularly is non SQL Server environment such as Oracle, Teradata, DB2 where a server is more costly than in SQL Server world. In SQL Server, where virtual instances/servers (VMs) are the default for Dev/Test envs and becoming standards for Prod too, there are cases where the staging DB is put on a different VM. The most common reason is performance (it would make the loading faster if we put the staging in different server/VM). I found that this is not the case. There’s only very little improvement to the performance, when we put the staging DB in a different SQL Server. This is because, the advantage from “the disk serving read from Staging is different from the disk writing into DW” is not the major factor. The major factor in loading is the non-efficiency of the code, i.e. lots of joins on the source, multiple threads are inserting into the same target table in DW causing blocking, the extract is not incremental, etc. The advantage from “if the Staging DB is located in the same server it would be faster because there’s no network traffic” is also a minor factor; doesn’t have too much impact to the loading performance.

      Comment by Vincent Rainardi — 9 June 2015 @ 5:36 pm | Reply

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