Management for Profitable Analytics – Part 1

In this blog tutorial series, you will learn about the management of successful business intelligence and analytics projects. Topics include:

• Defining Scope and Objectives
• Finding the Right Sponsor
• Producing the Project Roadmap and Plans
• Organizing the Team
• Executing the Plan
• Finishing the Project
• Avoiding Major Data Warehouse Mistakes.

Defining Scope and Objectives

Scope specifies the boundaries of the project. It tells what is in and what is out. The scope definition started in the business case will be expanded, if needed, when the project is underway. This effort includes:

• Overview of the project (Mission, Scope, Goals, Objectives, Benefits)
• Scope plan
• Scope definition
• Alternative development.

Defining the correct scope and setting realistic objectives are critical to any project’s success, and a data warehouse project is no exception. Scope defines project boundaries including:

• Business requirements addressed
• Anticipated/planned users
• Subject Areas such as inventory transactions or customer service interactions
• Project success criteria, including quantified planned benefits.

Defining an overly large project scope and letting scope grow in an uncontrolled fashion (scope creep) are certain to cause project failure. Remember you cannot please everyone:

I cannot give you a formula for success,

but I can give you a formula for failure: try to please everybody.

Herbert Bayard Swope

Enterprise vs. Departmental Focus

The choice of Enterprise Data Warehouse vs. Departmental Data Mart is critical to the success of data warehousing projects. This choice is a major component of project scope. Examples of factors that arise with each focus, based on my experience, are shown in Table 1.

Table 1: Enterprise vs. Departmental Focus

The project may require both an Enterprise Data Warehouse and one or more Data Marts. The future Technical Architecture blog article will explain more about this choice.