Data Warehousing and Business Intelligence Systems


As part of an ongoing release of all my training material following the launch of my new book, I am releasing my training course "Data Warehousing and Business Intelligence Systems" under a Creative Commons Attribution Share-Alike license. That is, you are free to share, copy, and adapt any part of this training course for your own purposes. All materials, including examples are available for download from this page. My related Agile Business Intelligence course is also released under Creative Commons.

Learn how to best manage your information assets and reduce ongoing costs using modern Business Intelligence techniques in a fun, friendly and effective way!

Business Intelligence & Data Warehousing systems are used by SME’s, large corporations and government entities to store key information used to make data-driven decisions. However, many BI/DW projects fail to deliver on expectations (& budget) as the development of such systems is a highly complex task requiring a combination of business sponsorship and solid database systems expertise. This course is designed to provide a thorough and practical coverage of the techniques and methodologies used to build a warehouse including:

  • defining information requirements
  • identifying potential data sources
  • efficient data warehouse design
  • extract- transformation-loads of data
  • query applications, and
  • executive information systems.

Additionally, data analysis algorithms and techniques used to identify expected and unexpected trends in data stored in a warehouse will also be covered. This course provides hands-on experience with some generic tools, but the concepts apply to all modern BI tools.

Course Notes

Download course notes

Course Slides (Prezi)

Course Features

  • Covers the entire Business Intelligence life cycle.
  • Introduces and/or reviews tools & processes as required by the participants,
  • Data Source Identification, Data Analysis, Data Mart Design, ETL, and Reporting.
  • Full interaction through case studies and theoretical modeling

Participant Benefits

Participants will leave this course with a clear understanding of Business Intelligence techniques and will have developed the ability to apply these within their organisation. They will also be familiar with the software currently available to assist an organisation store and report data. Finally, all participants will gain hands-on practical experience through a number of case-studies and interactive exercises.

Who Should Attend

This course is aimed at participants who are:

  • Interested in improving their organisations business intelligence.
  • Involved in data analysis and reporting for their organisation.
  • Involved in the day-to-day use of operational databases within their organisation and wishing to improve reporting requirements from these databases.
  • Any organisation, regardless of industry, with large or multiple data sources, stringent reporting requirements or wanting to improve data quality will benefit from this course.


Day One An Introduction to Data Warehousing Principles

This session provides an introduction to the principles behind data warehousing, the imperatives driving good data quality and modern research in the area. Existing open and closed source data warehouse applications and the rationale behind each will be discussed.

Business Requirements Leading to Data Analysis

The fundamental reasons for deploying a data warehouse will be examined. This begins with developing a business case for data analysis, one of the most time consuming but important, sections of the data warehouse process. The course will then examine tools and techniques to help analyse data, as well as how to plan the integration of that data into the data warehouse. It will cover:

  • Identifying potential data sources (databases)
  • The business case for each data source
  • How to analyse the data meaningfully
  • Using existing analysis tools
  • Other long term benefits of the analysis - "Owning your own data"
  • Why a data warehouse is never complete

Data Warehouse Design

Once the data has been analysed it is time to design the databases to hold information within the data warehouse. A data warehouse is made up of multiple databases of two different types, a consolidation database and data marts. A consolidation database is the central repository of all the extracted information, however given the size and complexity of this database smaller subsets, called data marts, are constructed to hold data for business intelligence and reporting purposes.

In this section we will look at the designing the consolidation database and data marts that make up the data warehouse, including:

  • An introduction to relational theory (On demand)
  • Differences between data warehouse databases and general transactional databases.
  • Star and Snowflake schema
  • Tips and tricks to remember during design
  • Storing historical data
  • Building triggers
  • Optimising data marts for rapid queries

Day Two Extraction and Transformation

One of the most important aspects of data warehousing is the extraction and transformation of data to be inserted into the data warehouse. This section looks at data integrity and validation to give an understanding of its critically importance. The course will cover

  • How to write good extraction queries (Garbage In / Garbage Out)
  • How can you transform your data
  • Data integrity and validation checking during transformation
  • Extracting historical information to build a complete image of your data over time
  • How to resolve extraction problems

Reporting The end result of any data warehouse project is having good data allowing for the creation of good reports and contributing to business intelligence. This section considers how to use a data warehouse to improve organisational reporting requirements and so improve overall business intelligence. Participants will learn how to:

  • Access data from the data warehouse
  • Turn data into information
  • Write good reports
  • Use a data warehouse to help find invalid or logically inconsistent data
  • Use a data warehouse to analyse large data sets
  • Use a data warehouse to improve business intelligence and see the business state at a glance
  • Schedule reports to run daily or at any appropriate time interval
  • Export data into a spreadsheet application for further analysis