The data warehouse is the central repository for corporation information representing the integrated data requirements of the enterprise and designed to support the analytics, DSS and reporting requirements of the entire organization.
The data warehouse is characterized by:
The challenges in designing and building the data warehouse lie in the details related to what the components of the data warehouse will be and structuring them consistent with actual business data.
From experience we know much of the data that needs to be components of the data warehouse - "GL accounts, customers, orders, channels, products, BBB..." and as many related pieces of data as can be conceived by business function.
The challenge is to identify the best-practice data structures consistent with business practice and integrate legacy and other sources of data.
What is needed is a 'blueprint' or picture of the target data structures that can be used to load current data, integrate legacy data and establish a data architecture supporting the needs of the organization into the future.
Any failure of the data warehouse normally lies in its design being driven by the data warehouse effort and not actual business data and requirements.
In order to identify the needs of the business at both a high-level and low-level of data granularity, comprehensive detailed data models are required. A comprehensive enterprise data model establishes the overall framework with successive Business Area Models providing ever more detailed and comprehensive data representations.
This is the ADRM Software approach to building the data warehouse.
The ability to apply comprehensive industry content to an initial design that is graphically easy-to-understand and based upon CASE technology will reduce development time, costs, manpower requirements and provide an immediate 'picture of the data warehouse'.
Having a 'blueprint' of the data warehouse at the earliest possible time is the single greatest contributing factor to the success of the data warehouse.