How is Project Management different for data warehouse projects?

May 16, 2016

 

Here are a few factors:

 

1. Data warehouse (DW) is a different "animal" They have to be designed, developed and supported to grow.  A data warehouse is never a really completed project - there are phases/releases/sprints that have start and end dates. A data warehouse does not reach an end state until it is terminated. 

 

2. DW projects can involve internal and external organizations that may or may not work congruently with each other. Also companies may have existing infrastructure or programs that may not work well or be compatible with your company’s data warehouse software.  These factors can be significant barriers to project management and data warehouse projects. 


We can use an agile project management to navigate through some of this complexity.  Agile methodology usually consists of 15-minute-long SCRUM meetings that occur multiple times per week or on a daily basis. SCRUM meetings consist of a SCRUM Master, TEAM and product owner. A SCRUM project is defined by a SCRUM plan that prioritizes, organizes and break the project down into specific tasks. The team meets to discuss the progress of tasks, collaborate with other team members to address impediments and to facilitate the completion of tasks. 

 

Here are some tips for a successful Data warehouse Project Manager: 

  • Keep the SCRUM meetings on topic, within the project scope and start and finish within the scheduled time allotted for the meetings.

  • Help the team have a clear understanding of the project scope and tasks in the project document.

  • Help the team optimize data warehouse management.

  • Facilitate team collaboration and communication. 

  • Motivate the team to deliver the project on time.

  • Help the team address obstacles.

     

     

     

     

     

     

     

     

     

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