The Aim of the Inception Phase

To clarify the requirements by way of working reports, dashboards and OLAP views using prototypes based on real data.

The problems that BI users have with a 'dry' requirements process and why it is important to do prototyping during this phase. Why iteration is critical here.

How to Gather Requirements

The concept of a 'Dry Spike'. Why Inception iterations (Dry Spikes) are not 'potentially releasable' (because ETL and schema design is avoided).
Why it is important to use real data: sponsors and users will be more excited by real data, real data can lead to higher ROI estimation.
The problems with using real data (ETL, staging, latency, sensitivity etc). How spreadsheet / flat-file data-sources and metadata can help (e.g. bypass access and security issues). How to select the data used. How to augment using spreadsheet-based calculations.
The importance of validating data accuracy during inception.
How to handle 'real-time' and operational BI.
Requirements:

• How to spot rip-and-replace requirements
• How to spot desktop reporting requirements
• How to spot self-service reporting requirements
• How to spot ad-hoc reporting requirements
• How to spot OLAP / Pivot View requirements
• How to spot dashboard requirementsHow to spot ETL requirements
  • 6.1 Inception Stake-Holders
  • 6.2 Inception Phase Plan
  • 6.3 Inception Iteration Plan
  • 6.4 Inception Iteration Templates
  • 6.5 Inception Iteration Tutorials