Data Warehouse Lifecycle Management: Concepts And Principles
Data warehouses provide accurate, timely management information for large enterprises. In a business world characterized by frequent, far-reaching change, data warehouses quickly lose their relevance if they do not adapt to this change.
When built using traditional software development methodologies, data warehouses are costly, and do not adapt efficiently to change.
Data Warehouse Lifecycle Management (DWLM) is a new discipline for the creation and ongoing management of a data warehouse throughout its operational life. DWLM delivers enterprise-scale data warehouses that adapt efficiently to change, at lower cost than traditional software development methodologies.
DWLM employs two development methods: Rapid Iteration and Federation. These methods enable organizations to implement an enterprise-wide data warehouse in bite-sized pieces.
Successful DWLM can only be achieved with a data warehouse that can react quickly to change: an adaptive data warehouse. Only a data warehouse capable of making structural changes in hours or days rather than the usual weeks or months will support the rapid iteration and federated development methods. While traditionally-constructed data warehouses require IT expertise for any structural changes, adaptive data warehouses give users access to a business model that can be changed without IT expertise, delivering unprecedented flexibility.
Offering high-speed implementation and faster cycles of change, Data Warehouse Lifecycle Management reduces risk and increases return on investment.