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Introduction to Business Intelligence & Data Warehousing

A very brief introduction to the differences and components of business intelligence and data warehousing solutions. Presented by Next Dimension Analytics (see - www.nextdimensionanalytics.com for more resources)
by

Andy

on 27 May 2011

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Transcript of Introduction to Business Intelligence & Data Warehousing

Business Intelligence
& Data Warehousing Definitions Components Benefits Lifecycle Business Intelligence Data Warehouse “Business Intelligence" is an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems." - 1989 Howard Dresner – Gartner Group

Business intelligence provides instrumentation of key business processes to enable both tactical and strategic decision making. Large database serving as a centralized repository of all data generated by departments and units of a large organization.

Reporting and analytical software is used to extract meaningful information from a data warehouse to support business decisions

The term was coined by the US consultant W. H. Inmon. Business Intelligence Data Warehouse Business processes
Business process decision points
Business process metrics needed to support decision points
Business data analysis needed to support decision points Operational Systems
Financial/Accounting
ERP
CRM
Manufacturing
E-commerce
Census
Geographical
Often with:
Different allowable values
Different attribute names
Different schema's Data Staging for ...
Cleaning operational data
Combining data from different sources
Ensuring data conforms to standards Data Warehouse ...
Consolidated source for analytical data
May have multiple data marts
May support multiple business process metrics
Simplifies analytics w/ consoldated schema Data Access & Analtyical Tools enable ...
Ad-hoc queries & reports
Automated report production
Analytical statistics and projections
Management dashboards for controlling business processes Single View For Example ...
Customer or Product information may be stored in several source systems.

Data Warehouse brings them together into a single structure with all relevant attributes Data Quality http://www.flickr.com/photos/gingiber/ Data Cleansing & Conforming ...

Extract Transform & Load (ETL) process ensures all data meet business requirements

Data that do not pass audits will either be: suspended to be fixed offline OR tagged so that reports can still access the data http://www.flickr.com/photos/velo_city/ One Data Model Historical Data Data Security With one data model ...
Analysts do not need to learn the intricacies of each individual source system.
Training is simplified.
Many reporting and analysis tools are designed to work with standard data warehouse designs. http://www.flickr.com/photos/andrec/ Can store many years of historical data.
Historical data can be reported on an “As Is” or “As Was” basis.
Example: when sales territories are realigned, historical data can be reported based on current alignment or the alignment in place when the sale occurred. http://www.flickr.com/photos/chealion/ With one consolidated database, limiting access by department or other criteria becomes easier.

Access can be defined at the row or column level, attempting to implement it in multiple databases with different designs is difficult. Incremental development based on ... Business process focus
Regulatory schedules
Data availability For more information and resoruces, come visit us at:
http://www.nextdimensionanalytics.com Thank you for viewing our short tutorial. We hope you found it useful http://www.flickr.com/photos/eob/
Full transcript