This column, as some of you recognize, references Malcolm Gladwell's book The Tipping Point.1 I was impressed, as most readers have been, with Gladwell's keen ability to articulate his self-described "intellectual adventure story" in a concise manner. Gladwell's original article, written in 1996 for the New Yorker, popularized the idea that some problems behave like infectious agents - requiring problem solvers to change their mind-set in order to solve them.

The term "tipping point" comes from epidemiologist's descriptions of where (in time, volume or other characteristics) an ordinary and stable phenomenon transforms into something that makes a big difference - a crisis. Using a number of examples, Gladwell correctly claims that understanding the "fundamental lesson of nonlinearity" will help society better understand a greater number of currently puzzling concepts, ranging from violent crime statistics to lessons "about the fate of modern liberalism." These give rise to different ways of dealing with the associated problems.

Similarly, I believe that tipping points and associated concepts such as connectors, mavens, salesmen, information stickiness and the power of context (covered in future columns) offer good explanations and models of how data management works and can be made to work better for organizations. This is important, considering the current state of the practice - generally acknowledged to be less mature and less pervasive than perhaps we wish it to be. I think it is safe to say that:

  • Fewer organizations employ what we would assess as "good" data management practices;
  • The extent of those practices within organizations is less mature than we would prefer; and
  • Establishing and maintaining organizational recognition of data management efforts has been a largely uphill battle.

Indeed, DAMA has been focusing on some of these basic premises as a professional organization. However, by any measure, collectively we are achieving little progress.
Many in IT believe that data management is obviously good for organizations. Who can argue with the premise that greater investment in efficient and effective management of an organization's data assets will result in better support of its strategies by data? But where can we go for guidance? Who can say how organizational size, technological complexity, mission and dexterity should affect the optimal data management team size? No such guidance exists because the area is largely unstudied from the business, computer science and engineering disciplines.

My research shows that while data managers can be assessed at a generally higher level of maturity than software development efforts, the collective state of the practice is less than desirable. Past approaches have focused on:

  • Whining that it is difficult to justify data management initiatives because they largely produce difficult-to-measure, indirect outputs; or that
  • Data management requires a critical mass (in time or resources or both) in order to produce satisfactory results.2

Our old ways of thinking about data management, its initiation and continued perceptions as a value-added organizational investment could benefit from additional perspectives. Let me conclude with the observation that the implementation of "good" data management practices can be described as, "An ordinary and stable phenomenon [poor data management practices] that transforms itself into something that makes a big difference [good data management practices] but not in a manner that corresponds to traditional thinking."3

Obviously, I recommend reading The Tipping Point - not to learn specifically about data management concepts but to learn about these aspects of life in general. They happen to apply particularly to data management.


  1. Gladwell, Malcolm. The Tipping Point: How Little Things Can Make a Big Difference. New York: Back Bay Books, 2002.
  2. Aiken, Peter. "Data Reverse Engineering." IBM Systems Journal 37, no. 2 (1998): 246-269.
  3. Gladwell, Malcolm. "The Tipping Point." New Yorker, 3 June 1996.

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