Planning and implementing Business Intelligence (BI) or data warehousing projects is complex and demanding. Business demands grow constantly, budgets tend to be tight, risks materialise quickly and time evaporates.
Corporate BI projects have a global scope with strict time controls; they have to mitigate risk and maximise business benefit. Using a field-proven methodology consistently, and delivering solutions based on best practice, makes a great deal of difference.
The implementation methodology has to incorporate solid project management knowledge, allow for the cultural changes that managing with Business Intelligence requires, and emphasise transferring knowledge to the team so as to secure the project's continuity and sustainability.
In every phase the risks predefined in the implementation strategy are controlled, through the control and assignment of resources to the project teams.
Phases to consider:
1. Analysis
Creates the corporate information strategy with a detailed planning roadmap.
Defines the scope of the indicators to be measured within the macro decision-making processes, and identifies where the associated data sits.
The end goal is to define the BI management concept, linked to the business processes so as to optimise them and identify opportunities for improvement.
2. Data engineering
Defines the detailed business, systems and data requirements. Translates those requirements into a specific data engineering model architecture, with a detailed description of the BI system's design criteria.
Builds the BI data warehouse together with the ETL processes needed to obtain the variables — the indicators — from the systems involved.
3. Application engineering
Implements the BI management concept on the chosen BI platform. During implementation the team works with end users to build their awareness of the process and train them directly on the tool (hands-on training), supporting the transfer of knowledge about managing with BI.
