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Business Intelligence for the Enterprise, Chapter 7

This chapter, "Elements of Business Intelligence Solutions," covers business intelligence (BI) topics like setting up information for BI processing, data warehouses versus data marts and reporting tools. Use this chapter for tips on setting up and executing a data warehouse project. You'll find diagrams and checklists for each step of the process.


Excerpted with permission from "Business Intelligence for the Enterprise," authored by Mike Biere. Published by IBM Press, June 2003, ISBN 0131413031. For more information about this book and other similar titles, please visit IBM Press.

Elements of Business Intelligence Solutions

The Three Major Categories of BI Analytics Tools
Here are the three tools categories:

  1. Traditional query and reporting
  2. OLAP
  3. Data mining

The first two pretty much provide answers on questions we feel we need to ask or for values we have determined we need to produce. Users may say, "I may not know how to create a report for profitability by product for all segments, but I know I need it. I may not like what I see when I get it, but it is what I asked for."

Data mining is a very different beast. There are numerous algorithms available in different tools, and the most intriguing are those that perform data discovery types of operations. Here, users say, "I need a tool that simply makes me think outside of the box. I need something to show me what I ought to look at!"

If you recall the tools pyramid, the largest segment of the user population was supported by query and reporting. One way to identify an OLAP solution and its applicability over traditional query solutions is if you see a pattern of queries that are similar but slightly different. Each level of detail is a slight variation of the previous. You notice that users are attempting to mimic drill-down analysis by launching query after query. Such patterns may be better served with an OLAP solution.

The same goes for data mining. If your users are creating and executing query after query in the hope of identifying some anomaly or aberration in the data, mining may be able to point out such results in a fraction of the time. Remember that the quest is to use BI intelligently and to change the business, not just get your tools investment back by the sheer volume of the queries that you execute!


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