Business Intelligence for Beginners

I never thought I would write a blog anytime. But Thanks to MIS 587 Business Intelligence course at Eller College of Management at University of Arizona, I am now writing my first blog ever.

Why I selected MIS 587 Business Intelligence (BI) elective for Spring 2014?
Last few years, I have been hearing a lot of boom over Business Intelligence. Had a brief idea that it’s making meaningful information with raw data in hand that a Company/Business already has. Also, I had heard that it has a process called ETL (Extract - Transform - Load) to do this transformation. There may be many more things to it, not sure yet.

Following diagram explains my this understanding:

The curiosity over how this happens, what it exactly means and how this would benefit any Business, made me select BI as an elective for current semester. Also, was aware that BI knowledge on my resume would surely open more career opportunities for me.

Initial few Learnings about BI:
The contents in this blog would be concentrating more on the Data Warehousing concepts of BI.

Let us first understand what Dimensional Modelling is:

Dimensional Modelling is a technique used in Data warehouse which makes data model easy to understand from end user point of view.

Few terms to know:

Dataware house: Central repository of database used for Analytical or Reporting Purpose.

Datamarts: It is a subset of Dataware house confined to specific Functional Area.

Normalised structures: Good for Operational processing but complicated for BI queries.

Schemas for Dimensional Modelling:

There are two main types of schemas used in Dimensional Modelling:
1. Star Schema
2. OLAP cubes.

Star Schema: 

Before we get to know what a Star schema is, it would be better to learn a few terms:
Facts: Used for measurement. Mostly Numeric and Addictive.

Dimensions: They are used to qualify facts.

Grain: Single row of data.

In Star Schema, a single fact table is surrounded by many dimension tables. The resultant structure looks like a Star and hence it is called as Star Schema. Pictorially it can be shown as follows:

Also, once you have star schema you can implement it as cube.The flow for same is shown in figure below:

Learning dimensional modelling techniques for Star Schema are thus even more important.

Stay tuned for my next blog post to know how we would use these basic concepts learnt during different steps for Data Modelling techniques.

References:
http://proquest.safaribooksonline.com.ezproxy2.library.arizona.edu/book/databases/data-warehouses/9781118530801/firstchapter
http://bennyaustin.wordpress.com/2010/05/02/kimball-and-inmon-dw-models/