Certified Data Mining and Warehousing Professional Planning principles and success factors

Planning principles and success factors

Planning Principles

Principles are general requirements or values that almost always apply. In systems planning, the principles are lessons learnt from a wide-ranging literature that has captured the essence of considerable and often bitter experience. Use these principles when you decide how to go about planning: deciding which methods to use and how much effort to make in order to obtain a satisfactory result.

Principles are not equally important in all circumstances. One of the most important responsibilities of the planner is determining the priorities of the principles.

Principles for planning
There are many recommendations about good practices in the very large literature about planning methods. The recommendations usually consist of descriptions of the steps that should be taken when carrying out planning.

It is difficult to remember these steps and to see when what should be done when. It is easier to follow the principles of sound planning rather than follow detailed steps of possibly unsuitable practices.

There is a set of principles that capture all of the recommendations given in the literature and can apply for all types of decisions or designs. They reduce the need to remember particular approaches for particular circumstances. They can be easily remembered and readily followed. In most cases, using these principles is sufficient for carrying out effective, efficient, and acceptable planning.

The nine principles of systems planning are

The principle means...
Plan the plan to use the appropriate planning tools and techniques, obtain sufficient resources for planning, check the planning is acceptable.
Look for feedback to see that the results of the plan are acceptable.
Avoid assumptions avoid assuming the solution; check the facts.
kNow your audience make sure the key people understand - and accept - what you are doing and how you are doing it.

Please Everybody understanding the values of the important points-of-view.
Analyze consider the attributes of the situation, analyze components of options to form creative combinations, consider attributes of options to see how well they meet the purpose.
Consistently Evaluate to make sure every option is compared against every value.
Fix Up developing options by growing them to remedy risk.
Lessen costs obtaining the ideal option.


Critical success factor (CSF) is the term for an element that is necessary for an organization or project to achieve its mission. It is a critical factor or activity required for ensuring the success of a company or an organization. The term was initially used in the world of data analysis, and business analysis. For example, a CSF for a successful Information Technology (IT) project is user involvement.[1]

"Critical success factors are those few things that must go well to ensure success for a manager or an organization, and, therefore, they represent those managerial or enterprise area, that must be given special and continual attention to bring about high performance. CSFs include issues vital to an organization's current operating activities and to its future success." [2]

Critical success factors should not be confused with success criteria; those are outcomes of a project or achievements of an organization that are needed to consider the project a success or to esteem the organization successful. Success criteria are defined with the objectives and may be quantified by KPIs.

Data Warehousing Success Factors

By knowing the critical success factors of a data warehouse or data mining project, the project manager is able to focus on what absolutely has to be in place for the project to be successful. Data warehouse experts Sid Adelman and Larissa Moss walk you through the steps that will help your project succeed:

If a factor or characteristic is critical to the success of a project, it is called a "critical success factor" (CSF). The absence of that factor or characteristic dooms the project. The following seven CSFs, therefore, are mandatory for a successful data mining or data warehouse project:

1. Expectations are communicated to the users

IT is often unwilling or afraid to tell the users what they will be getting and when. Users should be told about the following:

  • Performance - what to expect for response time
  • Availability - scheduled hours and days
  • Function - the data that will be accessible and what pre-defined queries and reports are available. The level of detail data, as well as how the data is integrated and aggregated
  • Historical data
  • The expectations of accuracy for both the cleanliness of the data and an understanding of what the data means
  • Timeliness - when the data will be available and how frequently the data is loaded/updated/refreshed
  • Schedule expectations involve when the system is due for delivery

2. User involvement is ensured

There are three levels of user involvement, as follows:

  • Build it; they will use it
  • Solicit requirements from the users
  • Have the users involved all the way through the project

The last level is by far the most successful approach, while the first almost always results in failure.

3. The project has a good sponsor

The best sponsor is from the business side, not from IT. Most importantly, the sponsor should be in serious need of the data warehouse's capabilities to solve a specific problem or gain some advantage for his or her department.

4. The team has the right skill set

Without the right skills dedicated to the team, the project will fail. The emphasis is on "dedicated to the team."

5. The schedule is realistic

The most common cause of failure is an unrealistic schedule, usually imposed without the input or the concurrence of the project manager or team members. Most often, the imposed schedules have no rationale for specific dates, but are only means to "hold the project manager to a schedule." A realistic schedule will include all the required tasks to implement the project along with their durations, assigned resources and task dependencies.

6. The right tools have been chosen

The first decisions to be made are the categories of tools: Extract/Transform/Load, data cleansing, OLAP, ROLAP, data modeling, administration, and so on. The tools must match the requirements of the organization, the users, and the project. The tools should work together without the need to build interfaces or write special code.

7. Users are properly trained

In spite of what the vendors tell you, users must be trained and the training should be geared to the level of user and the way they plan to use the data warehouse. All users must learn about the data, and power users should have additional in-depth training on the data structures.


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