Fear and Loathing in Project Management

How to insulate your data warehouse project team from potentially devastating business decisions

By Michael L. Gonzales

Continued from Page 2

The DIF Surveys

Quantify the DIF perspective in a series of surveys referred to as DIF Score Sheets. Rate candidate process flows according to the following criteria:

Dysfunctional. How bad is the process in terms of effectiveness and efficiency? Important measurements include:

• To what degree does the time to complete the process exceed industry benchmarks?
• Is the net result of the process even used?
• How would you grade the quality (or reliability) of data in the end product?
• Are "redo"s necessary to create an acceptable end product?
• Do you have duplicate or redundant data in the end product?
• Does the end product need a significant amount of manual data entry?
• Is the data consistently available for the process?
• How much are user communities dependent on IT in order to access the end product?
• Do you have integrity problems in the stored data of the end product?

In a scale from zero to five, a zero would mean that a process is the least dysfunctional, whereas a five would mean that it is the most dysfunctional.

Impact. How many other processes and applications can you affect by making changes to the candidate process? Try to answer the following questions:

• If this process were completely redesigned, what percentage of the overall process would be simplified or reduced?
• How many applications or processes would you simplify or reduce by changing this process?
• How much end user pain (family time, quality-of-life complaints, nonmotivating work, and so forth) would you reduce?
• By how much would you reduce cost?
• What is the reduction in person-hours devoted to this process?

Feasibility. This criterion specifically attempts to quantify how likely you are to succeed in simplifying a particular process given the following:

• What is the probability of successfully carrying out a complete redesign?
• Do you believe that you have well-defined business rules to carry out the process?
• Does the necessary technology to accomplish a significant improvement exist?

A zero would mean that redesigning this particular process has the least feasibility, whereas a five would mean that it has the most feasibility.

You can add, change, or delete survey questions to suit your needs. However, it is important to retain the overall integrity of the survey process with regard to the goal: getting a DIF score.

The Next Step

As part of the DIF Matrix approach, we use the second model shown in Figure 3. The model establishes a formal procedure to complete the analysis of candidate processes. As illustrated, the model incorporates all traditional means to document and identify requirements such as use case, class diagrams, and interaction diagrams. The documentation lets you understand the process under consideration and determine how to best implement a solution.

The net of this model is one of three possible outcomes per candidate:

• Quick fix. Candidates you can quickly resolve.
• Redesign. Candidate processes that will require a full redesign.
• Gradual improvement. Much is still unknown about the process even after analysis. Consequently, you must approach the candidate process via a pilot project.

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Take the High Road

Overall, the DIF approach effectively identifies, defines, and quantifies candidate opportunities and delivers clear criteria for executives to make decisions regarding a work schedule. This accomplishment is quite a coup because you often have numerous opportunities to consider in disparate environments with different agendas, business requirements, data sets, and technologies. The DIF Matrix lets your team stay above the fray.

Jaime Gonzalez, senior data architect and data modeler with The Focus Group Ltd., contributed to the content of this article and the development of the DIF Matrix approach.

Michael L. Gonzales, [mlg@starfocus.com], is the president of The Focus Group Ltd., a consulting firm specializing in data warehousing. He has written several books, speaks frequently at industry user conferences, and conducts data warehouse courses throughout North America.