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Meta Data & Knowledge Management:
Capability Maturity Model: Applying CMM Levels to Data Warehousing

  Column published in DM Review Magazine
November 2002 Issue
 
  By David Marco

This is the fourth and concluding column in a series on the systems engineering capability maturity model (CMM - copyright Carnegie Mellon University). In last month's column, I applied the first two CMM levels (levels 0 and 1) to data warehousing. In this month's column, I will continue with the remaining four CMM levels:

  • Level 2 - Planned and Tracked
  • Level 3 - Well-Defined
  • Level 4 - Quantitatively Controlled
  • Level 5 - Continuously Improving

Following the descriptions of companies at the CMM levels are questionnaires. Your company is likely at that CMM level for data warehousing if you answer "yes" to more than one of the questions.

Level 2: Planned and Tracked

At level 2, independent data marts are less prevalent than in level 1; however, they are still the most common form of data warehousing architecture used in your company. Successful data warehousing projects tend to stay successful and may be run in a fairly efficient manner. For example, a company may have many disparate data warehousing activities; however, a particular department or line of business may have implemented a sound data warehouse with the appropriate supporting teams and processes. This group will plan and track their IT deliverables and have defined some best practices (e.g., meta data repository, defined IT standards/documents, program version control, dedicated data warehousing team, data quality tracking, etc.). These repeatable processes exist within the department or line of business; however, these processes and standards are only followed by the group, not the entire organization. As a result, the success of this team/group is not transferable across the enterprise.

Even with this success, organizations at this level have many failed and unnecessary projects, which creates an overly expensive information technology (IT) organization.

Level 2 Questionnaire

  1. Is there a successful data warehouse implementation within your company?
  2. Is the successful data warehouse sustainable?
  3. Do you have a meta data repository that, at a minimum, contains and manages data warehousing related meta data?
  4. Has the successful data warehouse implemented development standards (e.g., meta data repository)?
  5. Does the enterprise lack centralized procedures/standards?
  6. Is your company spending extreme amounts on data warehousing (30 to 60 percent of the entire IT budget)?
  7. Does your company have multiple, large (8 to 15 staff members) data warehousing departments?

Level 3: Well-Defined

The jump from level 2 to level 3 is the most difficult for a large company or government entity. At level 3, IT best practices are documented and performed throughout the enterprise. In addition, IT deliverables are repeatable and transferable across the company. Not surprisingly, this is also the level that provides the greatest cost savings.

At level 3, the organization has investments in centralized IT organizations (centers of excellence, standards bodies, etc.), and the standards are religiously adhered to. Independent data marts are few and far between, and data warehouse project success is more common than project failure. In addition, an enterprise-wide meta data repository has been constructed and is a central part of any IT effort. At this level, the data warehousing investments are truly becoming efficient.

Level 3 Questionnaire

  1. Are there very few independent data marts?
  2. Do your data warehousing initiatives succeed more often than they fail?
  3. Is there a successful data warehouse implementation within your company?
  4. Is the successful data warehouse sustainable?
  5. Does your company know how much it is spending on data warehousing?
  6. Does your company have centralized IT groups?
  7. Does your company have an enterprise-wide meta data repository that supports the data warehouse and the operational systems?

Level 4: Qualitatively Controlled

Companies at level 4 have established measurable process goals for each defined data warehousing process. These measurements are collected and analyzed quantitatively. At this level, companies can begin to predict future IT implementation performance.

At this level, data warehousing efforts are consistently successful, and an organization can begin to accurately forecast future performance of these efforts. Existing data warehouse efforts are improving in data quality and value to the business.

Level 4 Questionnaire

  1. Can you measure each of your data warehousing processes?
  2. Are your data warehousing efforts consistently successful?
  3. Has the quality of the data within your data warehouse improved over time?
  4. Can you accurately predict (within 10 percent) what future data warehousing efforts will take?
  5. Is technology, data and process redundancy minimal throughout the organization?


Figure 1: Data Warehousing - CMM Levels

Level 5: Continuously Improving

At level 5, enterprises have quantitative and qualitative understanding of each data warehousing IT process. At this level, a company understands how each IT process is related to the overall business strategies and goals of the corporation. For example, every programmer should understand how each line of SQL will assist the company in reaching its strategic goals.

At this level, very low levels of data, process and technology redundancy exist, and the redundancy that does exists is documented and understood. Data warehousing investments are becoming optimized.

Level 5 Questionnaire

  1. Can you measure each of your data warehousing processes?
  2. Can you measure the quality of each of your data warehousing processes and compare that quality over time?
  3. Has the quality of the data within your data warehouse improved over time?
  4. Is your data warehousing investment very efficient?
  5. Does your company have a very strong understanding of the quality of each and every data warehousing process?
  6. Do your programmers understand their role in helping the company reach its goals?

I have never seen a CMM level 5 data warehousing initiative. If you have one, please feel free to contact me.

...............................................................................

For more information on related topics visit the following related portals...
DW Design, Methodology and DW Basics.

David Marco is an internationally recognized expert in the fields of enterprise architecture, data warehousing and business intelligence and is the world's foremost authority on meta data. He is the author of Universal Meta Data Models (Wiley, 2004) and Building and Managing the Meta Data Repository: A Full Life-Cycle Guide (Wiley, 2000). Marco has taught at the University of Chicago and DePaul University, and in 2004 he was selected to the prestigious Crain's Chicago Business "Top 40 Under 40."  He is the founder and president of Enterprise Warehousing Solutions, Inc., a GSA schedule and Chicago-headquartered strategic partner and systems integrator dedicated to providing companies and large government agencies with best-in-class business intelligence solutions using data warehousing and meta data repository technologies. He may be reached at (866) EWS-1100 or via e-mail at DMarco@EWSolutions.com.

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