Project Management for a Data Warehouse implementation
Most enterprises have built data warehouses in order to consolidate data from different sources and make it available for business reporting and insights. The Business intelligence layer built on top of the data repository allows for enhanced data visualizations and helps spot trends, identify discrepancies and helps business executives with making crucial strategic as well as operational decisions. These large projects usually have enterprise wide impact and takes a lot of resources both in terms of time and money, which calls for experienced project managers as well to manage the development as well as ongoing maintenance of the data warehouse.
A typical data warehouse implementation involves the following steps –
1. Identifying the business needs and documenting them – This step is critical and lays the foundation for the project. Clearly identifying the business need makes a good solution possible.
2. Getting the buy in from the business stakeholders – Unless the project has the backing of senior management and their commitment, it will be hard to execute a project of typically large size and enterprise wide impact.
3. Identifying the data sources – A lot of different sources feed an enterprise data warehouse. It could be both internal and external.
4. Design and development stage – Once the requirements, team and the data sources have been solidified the next stage is the design stage where data architects create data models. This logical model is then used to create a physical data model comprising of schema, tables, views etc.
5. The development is an iterative process and involves multiples rounds of Quality Assurance.
6. Once the data has been tested, it is made available to the consumers of the data in the form of reports, charts, graphs etc.