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Step 8: Database Design

One or more BI target databases will store the business data in detailed or aggregated form, depending on the reporting requirements of the business community. Not all reporting requirements are strategic, and not all of them are multidimensional. The database design schemas must match the information access requirements of the business community.

Step 9: Extract/Transform/Load Design

The ETL process is the most complicated process of the entire BI decision-support project. It is also the least glamorous one. ETL processing windows (batch windows) are typically small, yet the poor quality of the source data usually requires a lot of time to run the transformation and cleansing programs. Finishing the ETL process within the available batch window is a challenge for most organizations.

Step 10: Meta Data Repository Design

If a meta data repository is licensed, it will most likely have to be enhanced with features that were documented on the logical meta model but are not provided by the product. If a meta data repository is being built, the decision must be made whether the meta data repository database design will be entity-relationship based or object oriented. In either case, the design has to meet the requirements of the logical meta model.

Taken from : Business Intelligence Roadmap- By Larissa T. Moss, Shaku Atre

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Step 6: Application Prototyping

Analysis of the functional deliverables, which used to be called system analysis, is best done through prototyping so it can be combined with application design. New tools and programming languages enable developers to relatively quickly prove or disprove a concept or an idea. Prototyping also allows business people to see the potential and the limits of the technology, which gives them an opportunity to adjust their project requirements and their expectations.

Step 7: Meta Data Repository Analysis

Having more tools means having more technical meta data in addition to the business meta data, which is usually captured in a computer-aided software engineering (CASE) modeling tool. The technical meta data needs to be mapped to the business meta data, and all meta data must be stored in a meta data repository. Meta data repositories can be licensed (bought) or built. In either case, the requirements for what type of meta data to capture and store should be documented in a logical meta model. When licensing a meta data repository product, the requirements documented on this logical meta model should be compared to the vendor’s meta model, if one is provided. In addition, the requirements for delivering meta data to the business community have to be analyzed (e.g., online help function).

Taken from : Business Intelligence Roadmap- By Larissa T. Moss, Shaku Atre

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Step 4: Project Requirements Definition

Managing project scope is one of the most difficult tasks on BI decision-support projects. The desire to have everything instantly is difficult to curtail, but curtailing that desire is one of the most important aspects of negotiating the requirements for each deliverable. Project teams should expect these requirements to change throughout the development cycle as the business people learn more about the possibilities and the limitations of BI technology during the project.

Step 5: Data Analysis

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Taken from : Business Intelligence Roadmap- By Larissa T. Moss, Shaku Atre

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Step 2: Enterprise Infrastructure Evaluation

Since BI applications are cross-organizational initiatives, an enterprise infrastructure must be created to support them. Some infrastructure components may already be in place before the first BI project is launched. Other infrastructure components may have to be developed over time as part of the BI projects. An enterprise infrastructure has two components:

Technical infrastructure, which includes hardware, software, middleware, database management systems, operating systems, network components, meta data repositories, utilities, and so on.

Nontechnical infrastructure, which includes meta data standards, data-naming standards, the enterprise logical data model (evolving), methodologies, guidelines, testing procedures, change-control processes, procedures for issues management and dispute resolution, and so on.

Step 3: Project Planning

BI decision-support projects are extremely dynamic. Changes to scope, staff, budget, technology, business representatives, and sponsors can severely impact the success of a project. Therefore, project planning must be detailed, and actual progress must be closely watched and reported.

Taken from : Business Intelligence Roadmap- By Larissa T. Moss, Shaku Atre

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The approach presented in Business Intelligence Roadmap encourages the use of parallel development tracks (subprojects) so that multiple development steps can be performed simultaneously and multiple project activities can occur concurrently. Some project teams may choose to roll up project activities from multiple development steps into one step, while other project teams may not need to perform some steps or activities at all. Figure 0.5 illustrates the dynamics of a typical BI decision-support project, showing several steps running simultaneously (such as Step 5, Data Analysis, and Step 6, Application Prototyping) and multiple iterations of the same step (such as Step 9, ETL Design).

Engineering Stages and the Development Steps
BI projects are organized according to the same six stages common to every engineering project. Within each engineering stage, certain steps are carried out to see the engineering project through to its completion. Business Intelligence Roadmap describes 16 development steps within these stages, as outlined below.

The Justification Stage
Step 1: Business Case Assessment

The business problem or business opportunity is defined and a BI solution is proposed. Each BI application release should be cost-justified and should clearly define the benefits of either solving a business problem or taking advantage of a business opportunity.

Taken from : Business Intelligence Roadmap- By Larissa T. Moss, Shaku Atre

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Moving an organization from a “single-swim-lane” development approach to a cross-organizational, “cross-swim-lane” development approach requires organizational changes, including a culture shift. No other initiative demonstrates this as vividly as customer relationship management (CRM). If organizations would implement more cross-organizational BI operational applications (front-office as well as back-office) like CRM, they could significantly reduce their construction efforts on BI decision-support applications.

Although in Business Intelligence Roadmap we do not address organizational changes and culture shifts, we do define the necessary BI project activities that support an integrated enterprise-wide infrastructure. Both technical infrastructure and nontechnical infrastructure are required core competencies for cross-organizational integration. In addition to defining project activities, we identify the roles and responsibilities to be assigned to project team members for each development step.

The development steps outlined in this book form an engineering roadmap that provides a framework for developing different kinds of BI decision-support projects. The flexible entry and exit points of this framework allow you to start with any step as long as you meet the “entry criteria” outlined in the Entry and Exit Criteria and Deliverables Matrix. We also designed these steps to be agile and adaptive so that you can organize and manage the development of a BI application as multiple subprojects, each going through several of its own iterations or releases. For example, Figure 0.4 shows two iterations each for the Extract/Transform/Load (ETL), Application, and Meta Data Repository subprojects.

Taken from : Business Intelligence Roadmap- By Larissa T. Moss, Shaku Atre

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Unlike static stand-alone systems, a dynamic, integrated BI decision-support environment cannot be built in one big bang. Data and functionality must be rolled out in iterative releases, and each deployment is likely to trigger new requirements for the next release, as shown in Figure 0.3.

Figure 0.3 highlights other major differences between BI applications and stand-alone systems.

- BI applications are mostly driven by business opportunity rather than business need.

- BI applications implement a cross-organizational decision-support strategy rather than departmental decision-support silos.

- BI decision-support requirements are mostly strategic information requirements rather than operational functional requirements.

- Analysis of BI projects emphasizes business analysis rather than system analysis, and analysis is the most important activity when developing a BI decision-support environment.

- Ongoing BI application release evaluations promote iterative development and the software release concept rather than big-bang development.

The Cross-Organizational Development Approach
With the expansion of e-business comes an increasing demand for cross-organizational integration. This integration does not refer merely to bridging old systems across different platforms using enterprise application integration (EAI) middleware. Instead, it refers to:

- Information consolidation

- Information integration

- Information integrity

- Seamless business functionality

- Streamlined organizational business processes

Taken from : Business Intelligence Roadmap- By Larissa T. Moss, Shaku Atre

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