FREE DM Review Site Registration!
Sign-up today and access DM Review on the Web!

Your FREE registration entitles you to:
FREE email newsletters

FREE access to all DM Review content

FREE access to web seminars, resource portals, our white paper library and more!

   

Measuring Data Management Practice Maturity

The first comprehensive look at how data management is practiced worldwide reveals that most organizations do not manage data well, according to a recently published Virginia Commonwealth University study entitled, "Measuring Data Management Practice Maturity: A Community's Self-Assessment," according to the study.

The study includes an assessment of the data management practices of 175 organizations in both the public and private sectors between 2000 and 2006. Most of the organizations scored low on the assessment, indicating businesses and others have significant challenges in the area of data management.

These results are important because good data management practices are a necessary prerequisite to many technology-based organizational initiatives such as data warehousing, business intelligence, customer relationship management and other business analytics, according to lead author Peter Aiken, associate professor of information systems in the School of Business at Virginia Commonwealth University and founding director of Data Blueprint, a data management consulting firm. The poor results indicate a possible cause for these and other organizational initiative failures.

The researchers noted that many organizations do not invest sufficiently in data management, treating data as a maintenance cost rather than as an asset. That failure "is costly in terms of market share, profit, strategic opportunity, stock price and so on," according to the study.

The study's assessment results are based on self-reporting by the organizations involved; approximately 15 percent of the organizations also participated in an in-person investigation by researchers to validate the self-assessments. Researchers aimed to measure not only whether a data process is performed in an organization, but the maturity with which the process is performed.

The assessment results will aid data management practice improvements in organizations by presenting a scale for measuring data management accomplishments. The study's broad scope allows organizations to compare their performance against others in their industry and against the wider community.

Key Highlights

The assessment results suggest a need for a more formalized feedback loop that organizations can use to improve their data management practices. Organizations can use this data as a baseline from which to look for, describe and measure improvements in the state of the practice. Such information can enhance their understanding of the relative development of organizational data management. Other investigations should probe further to see if patterns exist for specific industry or business focus types.

Building an effective business case for achieving a certain level of data management is now easier. The failure to adequately address enterprise-level data needs has hobbled past efforts.1 Data management has, at best, a business-area focus rather than an enterprise outlook.

Likewise, applications development focuses almost exclusively on line-of-business needs, with little attention to cross-business-line data integration or enterprise-wide planning, analysis, and decision needs (other than within personnel, finance and facilities management).

In addition, data management staff is inexperienced in modern data management needs, focusing on data management rather than metadata management and on syntaxes instead of semantics and data usage.

Few organizations manage data as an asset. Instead, most consider data management a maintenance cost. A small shift in perception (from viewing data as a cost to regarding it as an asset)can dramatically change how an organization manages data. Properly managed data is an organizational asset that can't be exhausted.

Although data can be polluted, retired, destroyed or become obsolete, it's the one organizational resource that can be repeatedly reused without deterioration, provided that the appropriate safeguards are in place.

Further, all organizational activities depend on data. To illustrate the potential payoff of the work presented here, consider what 300 software professionals applying software process improvement over an 18-year period achieved:2

  • They predicted costs within 10 percent.
  • They missed only one deadline in 15 years.
  • The relative cost to fix a defect is 1x during inspection, 13x during system testing and 92x during operation.
  • Early error detection rose from 45 to 95 percent between 1982 and 1993.
  • Product error rate (measured as defects per 1,000 lines of code) dropped from 2.0 to 0.01 between 1982 and 1993.

If improvements in data management can produce similar results, organizations should increase their maturity efforts.

Click here for more information about this study.

References:

  1. B. Parker. "Enterprise Data Management Process Maturity." Handbook of Data Management , S. Purba, ed., Auerbach Publications, CRC Press, 1999, 99. 824-843.
  2. H. Krasner. J. Pyles. and H. Wohlwend. "A Case History of the Space Shuttle Onboard Systems Project," Technology Transfer 94092551A-TR, Sematech, 31 Oct. 1994.

Burt Parker is an independent consultant based in Washington, D.C. His technical interestes include enterprise data management program development. You can contact him at parkerbg@comcast.net.

Peter Aiken is an associate professor with Virginia Commonwealth University's IS Department and is the founding director of Data Blueprint(.com). A practicing data manager with 25+ years of experience, he has previously held positions with the US DoD and consulted with organizations in 18 countries. His sixth book is XML for Data Management. Aiken may be reached at peter@datablueprint.com or http://peteraiken.net.

M. David Allen is chief operating officer of Data Blueprint. His research interests include data and systems re-engineering. You can contact him at mda@datablueprint.

Angela Mattia is a professor of information systems at J. Sergeant Reynolds Community College. Her reserach interests include data and systems re-engineering and maturity models. You can reach her at amattia@jsr.vccs.edu.

For more information on related topics, visit the following channels:



Industry Vendors