Many companies migrate production data to a "centralized" database environment used for ad hoc queries and say that they are "Data Warehousing".

Daily, weekly, monthly, etc. full-file clones are not data warehousing either; these environments are not optimized for ‘analytical processing’. The physical implementation of database tables required for a Data Warehouse is significantly different from that of an On Line Transaction Processing (OLTP) System.

Industry gurus such as Inmon and Kimball have recognized that the models and methods to design a Data Warehouse are unique and different than traditional data modeling practices. However, it is only recently that these best practices have evolved as analysis tools in specifying Data Warehouse requirements.

In two fast-paced and interactive days we will cover the How-To’s of performing quality business requirements analysis for data warehousing projects, using – Best Business Requirements Practices™ (BBRP):

• Identify, validate and document your Client’s Requirements for a Data Warehouse, Data Mart or Decision Support System with confidence
• Ensure that raw business data harvested from legacy transaction processing systems and other data sources is transformed into powerful Business Intelligence
• Learn the latest practical techniques & best practices for specifying DW systems
• Work directly and interactively with business clients in defining project requirements

As a participant said, "It was incredible to see a process that our business management actually enjoyed – and that yielded a real and usable spec. With this process we can finally do it right. I am ecstatic. This is very impressive!"

This course is valuable for those with many years experience or those new to business analysis in Data Warehousing, Data Mart or DSS architectures:

This session is particularly geared to:

• System & Line Managers
• Project Managers & Leaders
• Business & System Analysts

Conduct Fast and Efficient Analysis

• Of your client’s Data Warehouse and decision support requirements
• Identify & Document the Information Requirements of a DW so that it is:
  • Meaningful to the key business and systems users
  • Sufficient in detail
  • Organized by subject area, usage & data
  • Specifically designed and optimized for decision support processes
• Perform Dimension modeling and dealing with slowly changing dimensions.
• Establish granularity, metrics & historical storage requirements of Fact Tables
• Define and Describe Data Transformation Rules
• Apply Validation techniques to determine the "critical path" domain of the Data Warehouse, Data Mart, DSS.

• Facilitate a DW Requirements Discovery Session using best practices such as Dimensional Modeling

• Opportunities to practice the concepts and techniques to conduct an effective Data Warehouse Requirements Analysis.
• Demonstrations of how these practices work, with real world and sample exercises.

• "Business Case" Justification; getting a real return on the Data Warehouse Investment.
• Aligning with Corporate Objectives.
• Developing the Warehouse Domain Model.

• Identifying the Business Questions.
• Business Fact Modeling; Identifying the Business Facts needed for the Business Questions.
• Data Sourcing; Locating the Business Facts.
• Fact Dimensions; determining the way Business Facts will be "sliced and diced".
• Dimensional Modeling; how to organize and represent the Business Facts and Dimensions used to satisfy the Business Questions.

• System Data vs. Business Knowledge; turning transactional data into Business Intelligence.
• Who, What, When, Where, How, and How Often?

• Facilitating a Warehouse Requirements gathering session with Business Clients / Knowledge Workers.
• Producing clear, concise and useable deliverables.