Thursday, March 13, 2014

Using Enterprise Bus Matrix for developing your BI solutions

Enterprise Bus Matrix

The enterprise bus matrix is used in the requirement gathering process while designing enterprise wide BI systems.Many organizations who are using an "Agile" approach to designing warehouse solutions use the Bus matrix to get their requirements specified.

How do you make the bus matrix ?

  • Invite your business users,stakeholder and an experienced facilitator to drive the conversation
  • Identify your key business needs.
  • Involve multiple departments using the system.
  • Identify all the data that will be used in the system.
Conformed dimensions are the heart of the bus matrix. Making sure that consistency is there across different business units makes key stakeholders looks at KPI's over a variety of business processes.
Facts should also be conformed.

The objective of using a a bus matrix approach is so that stovepipe data marts are not created.
The matrix defines processes that can be used. Dimensions represent the grain that can be defined.

An overview of the Bus Matrix
  • Conformed dimensions and conformed facts
  • Shared business process and dimensions
  • Planning,communication and expectation management.
  • Rows-Present processes,Columns-represents dimensions
  • After the core processes and dimensions are identified, you shade or “X” the matrix cells to indicate which columns are related to each row.

Matrix extensions

  • Opportunity matrix:
Replace dimension columns with business functions.
  • Analytics matrix:
In this case, reference the stable bus matrix rows but list the complex analytic applications as columns, shading the boxes to indicate which business processes are needed by each application to convey the prerequisite building blocks.
  • Strategic business initiatives:
List organization key initiatives or executive hot buttons as columns mapped to the underlying process metric rows.
  • Detailed implementation bus matrix:
Matrix rows are expanded to list individual fact tables or OLAP cubes, along with their specific granularity