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Collaboration by Data Organizations, Data Management 2.0

Does the metadata organization collaborate with the data steward group? Does the data architecture team collaborate with the operations team? Does the data quality team collaborate with the end-user community? Clearly, these are unfair questions because no one in their right mind would actually admit to not collaborating. Unfortunately, much of what we call collaboration really isn’t. If we are not collaborating, then what are we doing? Well, we might be communicating, cooperating or coordinating our activities.


Communication is simply the transfer or attempt to transfer information from one party to another. Communication may be the attempt to make sense of confusing data or mixed messages throughout the organization. We communicate through email, phone, instant messaging and many other media. Poor communication is still one of the most important problems we have to deal with in large organizations. Peter Drucker described it this way: “In no other area have intelligent men and women worked harder or with greater dedication than psychologists, human relations experts, managers and management students have worked on improving communications in our major institutions. Yet communication has proven as elusive as the Unicorn.”1


Coordination is about creating efficiency across a divergent set of resources. Because most of us serve others outside our own organization, coordination of activities is essential and allows us to employ a boatload of project managers throughout the organization. When you start a data type project, coordination is one of the central activities. For example, when we integrate a new data source, much of our time is spent coordinating the activities of the producers of metadata information. This coordination is essential to ensure that we get it right the first time. However, coordination of disparate activities isn’t collaboration either.


Cooperation informs us to be part of the team effort. Employees that cooperate may keep their individual processes separate, but they are willing to sit down and discuss the various interactions and possibilities. They may even alter their own offering simply to ease the transition or effort. Cooperation is a base requirement in any technology endeavor but perhaps more critical within the data area. Data is a complicated technology where groups like metadata, data architecture, data modeling and data stewardship must cooperate and align their functions. That being said, cooperation is about compromise, not collaboration. Collaboration is a process of creation between two or more resources. The product of this effort would not have been possible in the other three situations. Collaboration creates a shared understanding and meaning about a product, service or solution. Communication, coordination and cooperation are all required in order to actually collaborate. I can only imagine what the data space would look like if all of the parties actually collaborated together. Data would once again be regarded as an asset and an essential resource. Collaboration creates a shared emotion of ownership, which in turn delivers a far greater product. L. Denise puts it this way: “Unlike communication, it is not about exchanging information. It is about using information to create something new. Unlike coordination, collaboration seeks divergent insight and spontaneity, not structural harmony. And unlike cooperation, collaboration thrives on differences and requires the sparks of dissent.”2

What would collaboration look like with a metadata environment? The metadata team has the knowledge and capability to expand the functionality of the repository environment. Many authors talk about moving beyond the passive utilization of information to one more active. This cannot be done in a vacuum. The metadata group needs to collaborate with a variety of other teams in order to move to this next level of metadata maturity. When collaboration becomes part of the norm, the data architecture, data stewards and many others are as much a part of the metadata team as the current staff. A collaborative environment includes both producers and consumers working together to create a product or service that expands the utilization of data. Perhaps this is simply a level of maturity you reach after you handle the basics of communicating data’s value via the metadata effort.

The problem with collaboration is that many people believe they are collaborating when in fact they are not. As managers, we really don’t help the situation by giving such vague instructions as, “John, I need you to work with Mary on that master data management project.” What did this manager actually mean when he said “work with”? Should John just keep Mary informed on his progress and allow her to review the effort? Should John try to coordinate his activities with Mary’s in order to deliver a better product? Should John cooperate with the current process and allow Mary to take ownership of various deliverables? Or, should John collaborate with Mary from the start to deliver something different, something outstanding and something that returns data to its rightful position in the information technology world?


  1. P. Drucker. Management: Tasks, Responsibilities, Practices. Collins: New York, 1993.
  2. L. Denise. “Collaboration vs. C-Three.” Innovating Newsletter, 1999.


R. Todd Stephens, Ph.D., is the director of Collaboration and Online Services for AT&T, located in Atlanta, Georgia. He has more than 20 years of experience in IT and speaks around the world on metadata, data architecture and information technology. Stephens recently earned his Ph.D. in information systems and has more than 70 publications in the academic, professional and patent arena. You can reach him via e-mail at or to learn more visit

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Source:  Collaboration by Data Organizations, Data Management 2.0, By R. Todd Stephens, Ph.D., DM Review Magazine, December 2007

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