| Wiki Markup |
|---|
{scrollbar} h1. 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. h1. |
...
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 {children} |