Business Intelligence (BI) Reporting

Business Intelligence and the Data Warehouse

Learn About the Available Business Intelligence Tools for Your Data Warehouse

David Haertzen David Haertzen, Principal Enterprise Architect

There are numerous tools available for analyzing and presenting data.  We will help you sort through the options.  The types of tools breaks down into these categories:

  • Interactive Query and Analysis Tools
  • Reporting Tools
  • Data Mining Tools

This tutorial provides an overview of each of these types of tools.  It is our plan to create a multi part tutorial that provides further detail about each major business intelligence tool category and technique.

Interactive Query and Analysis Tools

Query tools enable the exploration of data through a user friendly exploration interface.  These tools are typically: 

  • Interactive
  • Ad Hoc
  • Driven By Spontaneous User Questions
  • Display Lower Volumes of data

They provide views of data that follow familiar patterns: 

  • Spreadsheet
  • Drill Down
  • Roll Up
  • Pivot

Examples of query tools include:

  • Access and Excel - Microsoft
  • ProClairity - Microsoft
  • BusinessQuery - Business Objects (now SAP)
  • ReportNet - Cognos


Reporting Tools

Reporting tools produce outputs that can be stored and reviewed.  Often reports are produced on time schedule such as monthly.  Reporting tools are typically:

  • Less Interactive
  • Less Ad Hoc
  • Report View of Data (Header and Detail)
  • Driven By Pre-established user questions
  • Display Moderate Volumes of data

Examples of reporting tools include: 

  • Access - Microsoft
  • Crystal Reports
  • Managed Reporting Environment - SolutionsIQ
  • SQL Server 2005 Reporting Services - Microsoft

 Data Mining Tools

Data mining tools are used by specialized analysts and driven by the search for patterns. Analytic methods are used such as:

  • Associations and Clusters
  • Decision Trees
  • Fuzzy Logic
  • Genetic Algorithm
  • Naive Bayes 
  • Neural Networks
  • Regression Trees
  • Sequential Clusters
  • Time Series

In addition to these methods, mathematical techniques are used: linear regression, probability and optimization algorithms.

Examples of data mining tools include: 

  • BusinessMiner - SAP/Business Objects
  • Enterprise Miner - SAS
  • See5 - RuleQuest Research
  • SQL Server Analysis Services - Microsoft

The patterns and rules discovered through data mining can be used to improve decision making and to forecast the results of those decisions.

 

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