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Project Management for Data Warehousing

Business Intelligence and Data Warehousing Require Project Management Know How

David Haertzen David Haertzen, Principal Enterprise Architect

How should a data warehousing / business intelligence project be managed?  Planning and organizing the data warehouse project includes:

  • Defining Scope and Objectives
  • Avoiding Major Data Warehouse Mistakes
  • Choosing Enterprise Data Warehouse vs. Data Mart
  • Getting the Right Sponsor
  • Forming the Team
  • Producing the Project Roadmap and Plans
  • Determining the Budget
  • Training the Team

Defining Scope and Objectives

 

Defining the correct scope and setting realistic objectives are key to data warehouse project success. Scope defines project boundaries including:

  • Business requirements addressed
  • Users
  • Subject Areas

Objectives define project success criteria including quantified planned benefits.

Defining an overly large project scope and letting scope grow in an uncontrolled fashion (scope creep) are sure fire ways to hurt the chance of project success.

Remember you can't please everyone:

"I cannot give you a formula
for success, but I can give you a formula
for failure: try to please everybody."

- Herbert Swope



We recommending the SMART objectives approach when setting goals and objectives.

SMART Objectives Support Data Warehousing

Avoiding Major Data Warehouse Mistakes

Be alert against making these common data warehousing missteps:

  • Focus on technology instead of people and process
  • Lack of sponsorship and management support
  • Overly ambitious or undefined scope
  • Undefined requirements
  • Unrealistic expectations
  • Failure to architect a long term solution
  • Failure to obtain high quality data
  • Failure to consider future requirements
  • Trying to turn the prototype into the final solution
  • Designed around one tool/vendor
  • Failure to scale up
  • Failure to store at the right level of detail / grain

The Methodology article provides a step by step approach that should help you to avoid these problems.  For further understanding of best and worst practices see the article titled Sustaining Data Warehousing and Business Intelligence.

Enterprise Data Warehouse vs Data Mart

The choice of Enterprise Data Warehouse vs Data Mart is key to the success of data warehousing projects.

The Enterprise Data Warehouse is:

  • Enterprise Wide
  • All purpose 
  • Takes 2 to 5 Years to Build
  • Requires Executive Sponsor
  • Costs $2 to $5 Million

While the Data Mart is:

  • Business Unit or Business Process Focused
  • Focused Purpose
  • Takes 2 to 9 Months to Build
  • Requires Management Sponsor
  • Costs $200,000 to $2 Million

The project may require both an Enterprise Data Warehouse and one or more Data Marts.  The Technical Architecture explains more about this choice.

Forming the Data Warehousing Team

The right team is key to any successful project and data warehousing projects are no different.  The following roles are needed for an effective data warehousing project team:

  • Sponsor and Data Warehousing Champions
  • Project Leader / Manager
  • Business Subject Matter Experts (SMEs)
  • Coaches
  • Business Analyst
  • Enterprise Architect
  • Data Warehouse Trainer
  • Data Modeler
  • Database Administrator
  • Technical Architect
  • Extract/Transform/Load Designer/Developers

Getting The Right Sponsor

 The executive sponsor is a senior management person who takes overall responsibility for a project.  A good project sponsor typically is a:

  • Person with large stake in the project outcome
  • Person with authority over resources appropriate to project (Data Warehouse requires more authority and resources than Data Mart)

The project sponsor fills a number of roles including:

  • Developer of the business case
  • Harvester of benefits
  • Overseer of the project
  • Link to upper management
  • Project champion - promoting the project across the organization

For more information see Terence J. Cooke-Davies' excellent article, The Executive Sponsor – The Hinge upon which Organisational Project Management Maturity Turns? that describes the role of the project sponsor.

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The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics
By David Haertzen
The Analytical Puzzle