Project Management for Data Warehousing
Business Intelligence and Data Warehousing Require Project
Management Know How
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
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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.
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Remember you can't please everyone:
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"I cannot give you a
formula
for success, but I can give you a
formula
for failure: try to please
everybody."
- Herbert
Swope
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We recommending the SMART objectives approach when setting
goals and objectives.
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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