Data Warehouse Implementation - Guidelines and Principles

Data Warehouse Implementation – Guidelines and Principles

Focus on Business Goals: Make sure DW serves organizational priorities and solves business problems.
Start with the End in Mind: Let the business priority and scope of end-data-delivery in the BI space drive the creation of the DW content.
Think and Design Globally; Act and Build Locally: Let end-vision guide the architecture, but build and deliver incrementally, through focused projects or sprints that enable more immediate return on investment.
Summarize and Optimize Last, not First: Build on the atomic data. Aggregate and summarize to meet requirements and ensure performance, not to replace the detail.
Promote Transparency and Self-Service: The more context (Metadata of all kinds) provided, the better able data consumers will be to get value out of the data. Keep stakeholders informed about the data and the processes by which it is integrated.
Build Metadata with the Warehouse: Critical to DW success is the ability to explain the data. For example, being able to answer basic questions like “Why is this sum X?” “How was that computed?” and “Where did the data come from?” Metadata should be captured as part of the development cycle and managed as part of ongoing operations.
Collaborate: Collaborate with other data initiatives, especially those for Data Governance, Data Quality, and Metadata.
One Size does not Fit All: Use the right tools and products for each group of data consumers.

Data Warehouse Implementation – Guidelines and Principles