Data management, as we know it, was conceptualized by Richard Nolan's 1970's writings in the Harvard Business Review culminating in "Managing the Crises in Data Processing" (for which, in part, he was awarded the 2004 DAMA International Achievement Award). Nolan introduced the concept of a "transition point" where an organization realizes that it has shifted its focus from computer management to data resource management. In today's, parlance it would be called a "tipping point." We have some good evidence that, while individual groups have made impressive advances, most organizations are on the uphill side of the data management tipping point. For example, our surveys have noted that fewer than 1 in 10 achieves a CMMI-like score greater than 1, meaning that the maturity self-rating of most organizations is "initial" or the least mature of the five CMMI maturity stages.
If we accept the premise that most organizations would be better off if they were able to shift toward data resource management, then it is easy to see how one of DAMA's goals might be to help organizations make the transition from computing management to data resource management. A restatement of this goal simply might be - to attempt to help organizations practice "good" data management.
If we define good data management as that practiced on the other "good" side of the data management tipping point, how can we, as data managers, help to get our organizations to the other "good" side? Returning to Nolan's writings, his first step was to "recognize the fundamental organizational transition," and this first necessary (but, of course, insufficient) prerequisite is probably as good a starting place as any.
We have often spoken among ourselves about those organizations and/or persons who either "get it" or fundamentally don't "get it." Organizations operationalizing data-centric development practices are said to "get it." While we know it when we see it, what actually do we mean by the term "good data management practices"? The first step toward better data management practices is to collectively articulate what we mean by good data management practices and then to take steps to implement them in our organizations.
As the premier data management organization, DAMA has been asked by DM Review to explicitly take on the challenge of using this column to define good data management. Measuring the number of organizations that practice good data management is also a necessary prerequisite to doing it better. Collectively, only then can we determine the current state of the practice and take steps to improve it - to use some of our favorite language, to map the "as-is" in anticipation of developing a suitable "to-be."
Of course, using our vocabulary does not bring us into better communication with our business partners. While they are smart and business savvy, we must ensure that they understand two things:
- That our purpose as data managers is to best support their business practices, and
- That it is in their best interest to allow us to demonstrate to them how they can best benefit from good data management practices.
This will be the direction for our initial efforts. We will attempt to define for our business users what we mean by a data management tipping point, and we believe that you may benefit from a similar talk with your own management as well.
Much of the reason for the various forms of confusion surrounding the data management tipping point is the fact that data management as a discipline has expanded so much since its early inception. This and other relevant publications provide a wide range of articles from compliance to a survey of analytics. Read any subset and discover that data management today encompasses a wide variety of interests. If your work involves any of them, then you should consider getting involved with DAMA. The street is two-way. DAMA needs to hear about your goals, objectives and concerns. This is our way of staying relevant for your needs. In subsequent columns, I will focus on the process of examining our much expanded world of data management.
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