Data systems are intended to support both transactional and operational applications, as well as satisfy the downstream need for subsequent business analysis. A data warehouse, in contrast to transactional systems, is focused primarily on the collection, validation, and analysis of data. For example, a workflow performance management data warehouse contains metrics relating to the work activities that are performed in an organization. When developing a data warehouse application, it is important that the designers and implementers of the upstream systems ensure that the data requirements and characteristics are identified, assessed, and tested. This is accomplished by following a data requirements analysis (DRA) process.
This course reviews the tasks that are performed during the data requirements analysis process for a data warehouse system to ensure the identification, suitability, and quality of the data to meet the business needs and provide the framework for conducting testing and validation, as well as ongoing production monitoring of data in a data warehouse environment. In turn we look at how the project-oriented development life cycle is supplemented with aspects of data requirements analysis to ensure that analytical needs are addressed as early as possible in the information production flow.
You Will Learn
- How to evaluate business uses of information
- How to identify and collect application data requirements
- Business policies and data policies
- How to conduct data assessments
- Dimensions of data quality
- How to specify data requirements
- Data analysts
- Requirements and testing personnel
- Data warehouse designers