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BI Application Development

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Activities in Phase 5
Phase 5 - Incremental Development, Testing, Deployment and Improvement
Content Model Relationship

Contents

Activity: BI Application Development

Objective. Once the database is in place, Business Intelligence Application Development can begin. This activity builds on the prototyping and design phases of the earlier phases to deliver a finalised product.

Major Deliverables
  • Business Intelligence software
  • Unit Test Cases
  • Changes to software environment (if required)
Tasks

Task: Develop Reporting Components

Objective: Reports may include aggregate information as well as detailed data. Users may also want the capability to drill-up, drill-down and drill-across. Reports will follow the standards and guidelines and build on the prototypes defined in the earlier phases.

Besides business reports, this task also refers to the development of operational reports that describe the health of the information environment. Operational users often want the same information as business users: time-variant, detailed and aggregated in a fashion that will allow them to quickly see the ‘big picture’, and be able to drill into detail when required. Operational reports typically focus on areas such as data quality, load results and performance measures.


Input:

  • Report Design
  • Target Physical Data Model
  • Implemented Database
  • testing environment
  • test data


Output:

  • Business Reports Developed and Unit Tested
  • Operational Reports Developed and Unit Tested

Task: Develop Ad-Hoc Analytical Components

Objective: There will likely be a need to provide ad-hoc access to the information environment so that users can look to answer their own sets questions about the data (often referred to as \’power users\’). Ad-Hoc access should be simplified for the users so they do not need to understand the detail of the underlying physical table structures. It should also provide a business context through supplementary metadata.

  • Integrated metadata into target environment


Input:

  • Ad-Hoc Analytical Design
  • Target Physical Data Model
  • Implemented Database
  • testing environment
  • User interaction sessions to drive out requirements


Output:

  • Components to support Ad-Hoc Analytical Access Developed and Unit Tested

Task: Develop OLAP Model

Objective: Depending on the solution approach, there may be a need to develop OLAP "cubes" to provide users with the capability to quickly analyse and report on multidimensional or time-oriented data, using techniques such as drill-down, drill-up, drill-across and visualisation. This would be a specific technology approach for implementing a solution for Ad-Hoc analysis.


Input:

  • OLAP Model Design
  • Target Physical Data Model
  • Implemented Database
  • Integrated Metadata into Target Environment
  • OLAP Design


Output:

  • Components to support Ad-Hoc Analytical Access Developed and Unit Tested

Task: Develop Data Mining Components

Objective: The process for Data Mining is different from that for decisions support: whereas a DSS system provides answers to questions, Data Mining is about discovering new questions and the associated answers. Data Mining is a discovery-driven process involving the use of detailed and historical data. The task on Data Mining Application Design describes this process in more detail; this build task for Data Mining involves finalising this discovering work into an analytical model that can be used in an ongoing fashion.


Input:

  • Data Mining Application Design
  • Target Physical Data Model
  • Implemented Database
  • testing environment
  • Integrated Metadata into Target Environment


Output:

  • Analytical Data Model to be used within the Data Mining Tool

Role: Information Architect

Role: BI Application Development Lead

Role: BI Application Developer

Yellow Flags

  • Frequent changes required to analytical data model
  • Representative data sets are not available for development
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