I’ve been asked on occasion to define Business Intelligence and Data Warehousing. Typically, I am not sure how to answer. I’m terrible at inventing definitions, but here is what I usually respond with: For Business Intelligence, it is “a broad term describing the acquired knowledge obtained through reporting and analysis of organizational information” and for Data Warehousing, “A subject-oriented, integrated, time-variant, non-volatile process of collecting data for supporting decisions” (close to Inmon’s definition). But those answers don’t really satisfy me or those who have asked the question.

There are dozens of ‘accepted’ definitions for Business Intelligence and Data Warehousing. To make matters more confusing, the community itself seems torn. For example, the wikipedia article on the subject is woeful at best. I’ve seen others define BI as a ‘front-end’ to Data Warehousing, which I wholeheartedly disagree with. There is also a strong misconception that Business Intelligence and Data Warehousing are one-in-the same or connected at the hip. They are not.

I believe some of the onus can be placed on vendors who sell “BI” or “BIDW” solutions.

First, Business Intelligence

Business Intelligence is not an activity or process. It’s a result. You either have it or you don’t. This is a very important distinction, considering the level of misconception surrounding it.

How can you get it? Well, lots of ways.

Having intelligence about the business allows you to make informed decisions. The intelligence is derived from many sources. One of which is through Data Warehousing. Enterprise Resource Planning (ERP), knowledge management, and technical reporting are others, for example.

Second, Data Warehousing

Data Warehousing is a process. It is not just a database, nor is its purpose in life to just hold historical data (a common misconception). The process involves Data Profiling, Metadata Management, Dimensional Modeling, Data Integration (ex: ETL, EAI), Data Quality, Reporting, and Governance. There are many variations to this process, all of which are driven by business needs.

The Data Warehouse facilitates Business Intelligence by providing the necessary data and processes needed to integrate disparate transactional data into valuable information presented through various Applications such as executive dashboards, ad-hoc reporting tools, analytics, etc. These Applications are used by decisions-makers to make informed decisions.

It follows that Business Intelligence can be an outcome of Data Warehousing, provided that the data in the warehouse is promoted to information and then applied to decision-making.

Purpose and Justification

Strategic initiatives, goals, and competition will drive how organizations approach obtaining Business Intelligence. One organization might look at it for tactical reasons (i.e. decision support). Another might see it as a mission-critical part of running their day-to-day operations. Yet another might see it as a competitive differentiator. These drivers define the tools, techniques, and processes that might be used. Data Warehousing may solve one or more of these requirements.

Additionally, the drivers dictate the type of Applications that will be created on top of the Data Warehouse Database. Some departments may insist on printable reports or Excel sheets. Some Applications might be designed to analyze trends, issues, and events to gain insight into a process. It is very common to see outcome analysis, scoring, data exports, and some automated processes (such as bulk email operations).

…

The point of this entry is not to “set the record straight”, but rather to help draw the line between Business Intelligence and Data Warehousing. It is an expression of my opinion on the subject. Please understand that this is an important discussion to be having if you are involved or are planning to be involved in a Data Warehousing project.