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BI review
EIM master plan
     
   

BI Review is aimed at achieving the following goals:

  • Leveraging organizations business value by transforming their raw data into a strategic asset that supports efficiency and growth processes.
  • Achieving effective and efficient investments.
  • Keeping business continuity.

BI Review will include the following:

  • Review of enterprise information business requirements and the BI implementations in four main aspects:
      • Business
      • Data model
      • Technology and working processes

                            The above will be done by using predefined best practice                             business areas (all the information, operational and analytics that                             support the industry business   needs). The requirements in each                             track and the insights of the existing situation will be analyzed                             and the outcome will be translated to      recommendations and                             roadmap to improvement.

  • Definition of the process, SOW, budget, milestones and the time table for change management in the BI environment as a result of business change – merging, acquisition, distribution.

EIM master plan

EIM master plan is built to achieve the following goal:

  • Leveraging organizations' business value by transforming the raw data into a strategic asset that supports efficiency and growth processes.

 EIM master plan will include the following:

  • Bringing a predefined best practice business areas (all the information, operational and analytics, that supports the industry business needs) and specifying how to implement them depending on the current data assets and processes.
    Business areas to focus on are:
    • CDI – Customer data Integration
    • PMI – Product Management Information
  • Building a full concept of business language definition, management and deployment (Meta Data, Portal).
efinition of the process and tools that assure the reliability, consistency and completeness of the data (DQM, cleansing, data enrichment).
 
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