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Capability Maturity Model: Applying CMM Levels to Data Warehousing

Meta Data & Knowledge Management

This column marks the third part in a series on the systems engineering capability maturity model (CMM – copyright Carnegie Mellon University). In previous installments, I have discussed the value of the CMM model and the key CMM concepts, and walked through each of the six CMM levels. In this month's column, I will apply the CMM model to data warehousing and provide you with metrics to rank your company's data warehousing efforts on CMM's six levels:

  • Level 0 – Not Performed
  • Level 1 – Performed Informally
  • 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. If you answer, "yes" to more than one of the questions, it is likely that your company is at the CMM level for data warehousing just described.

Level 0: Not Performed

If your company has not built a data warehouse or has tried but failed to build a data warehouse, then your company is at CMM level 0. If your company has built a data warehouse, it is still possible to be at a CMM level 0. Companies with warehouses at this level have few or, most likely, no best practices around their data warehouses. Their warehouses are built by a fairly small number of people (possibly even one person) in a very isolated fashion. There is a very limited understanding or testing of the quality of the data in the data warehouse. Lastly, low levels of business value are being provided.

Level 0 Questionnaire

  1. Does you company's IT group lack a dedicated data warehousing department?
  2. Does your company use nonstandard terms to refer to its data warehouse (e.g., data repository, data dumpster, data store, etc.)?
  3. Is there a lack of understanding of the architectural differences between data warehouses, data marts and operational data stores?
  4. Are independent data marts the "standard" data warehousing architecture?
  5. Does the data warehouse lack or have only limited error-handling processes?

Level 1: Performed Informally

At CMM level 1, consistent planning and tracking of data warehousing activities are missing. In addition, data warehousing projects are run in a heterogeneous manner, with few standards and reuse/sharing. As a result, one team will build its data warehouse in one manner and the next team will build its data warehouse in a completely different manner.

This level creates extreme amounts of data, process and technology redundancy. Often, redundant or needless data marts and data warehouses are constructed. For example, I had one client that had built eight independent data marts. After our company analyzed each of these eight data marts, we were able to consolidate them into only two data marts. What we found was that each data mart's data and processes were fundamentally redundant to the other data marts. The only differences were how the data was filtered and organized on the end users' reports. Technology redundancy is also rampant at this level. The most popular software category is "shelfware." In addition, these same companies purchase large amounts of redundant software. For example, is there a business or technical reason for a company to have SQL Server, Oracle, DB2, Teradata, Sybase and Informix? No. However, the majority of the Fortune 500 companies have all six of these relational databases on site. This needless redundancy requires the availability of IT resources that are knowledgeable on each of these technologies. Obviously the software vendors enjoy these spending patterns a great deal.

At CMM level 1, some data warehousing projects are successful, some fail miserably, many projects are unnecessary and all of them are overly expensive. Often, data warehousing projects that are initially successful cannot be sustained. They may be successful in the first few years of operation but then will not be able to scale to meet the end users' demands for increased data volumes.

Companies that are at a CMM level 1 in data warehousing typically spend a great deal of money. It is important to understand that spending money on data warehousing applications will not move the company past CMM Level 1 unless it is spent wisely. In fact, the most costly data warehousing implementations reside at Level 1. Unfortunately, the vast majority of Fortune 500 companies and large government organizations have data warehouses that are at a CMM level 1.

Level 1 Questionnaire

  1. Is your company just beginning to understand that there are considerable data quality issues in the data warehouse?
  2. Is your company spending extreme amounts on data warehousing (40 percent to 60 percent of the entire IT budget)?
  3. Does your company not know how much it is spending on data warehousing?
  4. Does your company have multiple, large (8­15 staff members) data warehousing departments?
  5. Does your company lack a global understanding of all of their data warehousing initiatives?
  6. Are independent data marts the "standard" data warehousing architecture?
  7. Does the data warehouse lack or have only limited error handling processes?

In my next column I will continue to apply the remaining CMM levels to data warehousing.


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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