The BI project cycle
How to manage Business Intelligence projects? Business Intelligence projects require a specific project approach – the BI project cycle – because they significantly differ from traditional system development projects on a number of aspects.
Be aware of the nature of BI
The BI project cycle starts with awareness: we need to be aware of the nature and character of Business Intelligence. Once this is clear, we start looking for the business cases and we determine the scope. The blueprint of the BI system is created by using the basic principles that govern the BI architecture whilst defining the indicators and dimensions (information analysis) based on which we create a functional design that ultimately leads towards both a balanced architecture and a suitable data model.
Data quality audit
The organization will also perform a data quality audit (in order to determine the quality of data) and select the required tools that both fit the architecture and match the user requirements. During the first eight phases, we use the waterfall methodology. In the phases that follow – detail design, build and test – we use the incremental method. Ultimately, the Intelligent Organization will ensure that the project results are embedded in the business and evaluated.
Start with a business case
We must have a well written business case: stating what the project contributes to the improvement and development of the organization. When we start using BI, we must determine a scope that allows us to create a business case after the first or the second sub-project has been completed and put into production. Such a scope should contain roughly 20% of the total information needs of the organization. The main purpose of information analysis: what information do we need in order to improve the results and the processes and to achieve our goals faster?
Matrix of all indicators and dimensions
The functional design includes a matrix and the definitions of indicators and dimensions. It also describes the correlations between indicators and dimensions. Building a prototype is recommended whilst designing the architecture. The selection criteria are subdivided into ‘need to have’ and ‘nice to have’ so to reduce the chances that good products are rejected just because they score poorly on aspects are not essential. In the detailed design, we describe the transformation and loading processes, which we will then realize during the build phase and test during the test phase. After approval by the users, the BI system is rolled out and embedded in the organization after which it can be put into production.
High risk projects
BI projects are characterized by an extremely high risk factor and many obstacles. These obstacles are mainly related to the fact that Business Intelligence projects typically go beyond the boundaries of departments, processes and even business units; contain a mix of strategy, business operations and technology; are often highly political.
Make or break a BI project
In addition, the Business Intelligence systems use data from the operational information systems. The obstacles refer to the most common and most important powers and risks that can make or break a Business Intelligence project. The obstacles refer to four ‘faulty’ behavioral cultures, the complexity of ETL, the ‘technology’-push and to the amount, the quality and the absence of data. The most critical success factors of Business Intelligence mainly deal with how we use information for analysis and proper action, and whether we regularly evaluate and adjust standards and targets.
Effective BI teams
Effective BI project teams are characterized by their multidisciplinary nature. For a multidisciplinary project team to function properly, it is necessary that adjacent roles have sufficient knowledge of each other’s problems, experience, ambitions, plans and expertise. We should particularly focus on the different ideas and visions that project members may have with respect to the architecture.
Small BI projects
In BI projects, we distinguish between basic roles and additional roles. Small projects can usually suffice with the basic roles. Most successful BI projects are managed by a project manager who has knowledge of both business and technology. These projects are characterized by the continuous interaction between IT and business operations. BI projects that are solely data-driven or technology-driven are more likely to fail.