Every business believes that their data is unique. However the storage and management of that data uses similar methods and technologies across all organisations. As a result the same issues of consistency, performance and quality occur across all organisations. The commercial difference between organisations is not whether they have data issues but how they react to them in order to improve the data.
This paper examines how data is structured and then examines characteristics such as the data model depth, the data volumes and the data complexity. Using these characteristics it is possible to look at the effects on the development of reporting structures, the types of data models used in data warehouses, the design and build of interfaces (especially ETL for data warehouses), data quality and query performance. Once the effects are understood it is possible for programmes and projects to reduce (but never remove) the impact of these characteristics resulting in cost savings for the business.
This paper also introduces concepts created by Data Management & Warehousing including:
- Left to right entity diagrams
- Data Model Depth
- Natural Star Schemas
- The Data Volume and Complexity graph
- Incremental Phase Benefit Model
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