Once upon a time, there were four expert data modelers engaged on an enterprise data modeling effort. There was also an additional expert data modeler facilitator to foster collaboration among the modelers. The modelers each had different opinions regarding how to model certain constructs, for example, how to model roles, statuses, categories, hierarchies, and so on. The facilitator looked at their suggestions and saw the merit in each of their designs and then, after numerous discussions and debate, recommended a particular way of modeling the requirements so that this could be standardized across the enterprise. After certain modeling decisions were made, one of the modelers objected fiercely, saying that the modeling designs chosen (which were not his modeling designs) were terrible and then proceeded to let many people know about the mistakes. After a great deal of conflict, the team was disbanded and the enterprise data modeling effort was cut without having any established model.
In my experience, in order to successfully establish and implement an enterprise data model, there are certain principles of collaboration that are crucial, namely:
- Understand motivations.
- Have a shared vision.
- Develop trust.
- Effectively manage conflict.
I have written and spoken about these principles in many articles, publications and conferences.[1] And I recommend using Universal Data Models to help address these principles and improve collaboration between team members.
Universal Data Models (UDMs) are templates or re-usable data models that are generally applicable and that can be used to save time and effort while offering holistic perspectives. These models can provide a common ground from which teams can:
- Understand motivations. As demonstrated in our ‘once upon a time’ story, one of the key motivations that can be a problem is one of being ‘right’[2], versus appreciating perspectives. Universal Data Models can provide alternate perspectives and sometimes, they can be used to assess if there are fixed, narrow, ‘right’ ways of thinking or if modelers are open to other outside perspectives. By providing templates, UDMs can sometimes clarify if modelers are attached to a specific way of modeling (‘their’ way being the right way). There is also a danger that modelers will view the Universal Data Models as ‘right’ and be too evangelistic. The UDMs do not represent the ‘right’ models because in my opinion, there is no such thing. Instead, they provide various suggestions about ways to effectively model that have worked in other environments.
- Have a shared vision. Universal Data Models can help with a piece of a shared vision by showing patterns for how subject data areas, as well as data model constructs within these areas, can be laid out. We encourage people to use Universal Data Models not as the final solution but as input that can help formulate how data may be classified and modeled, and then we use Universal Patterns as a mechanism for custom tailoring these models, for example, making them either more specific or more generalized as needed.
- Develop trust. Having a library of re-usable models and templates that have a track history of working in various organizations can often help develop trust. And knowing that you don’t have to reinvent the wheel can encourage better collaboration on refining the models.
- Manage conflict. Using a third-party set of models can often be a way to reduce disagreements among data modelers. Rather than having to decide between one modeler’s choice versus another modeler’s choice, referring to an independent option can sometimes simplify the situation. One of our clients said that the greatest value offered by Universal Data Models is to help politically by having a third-party solution available that often resulted in not picking specific modeling solutions offered by the various modelers, thereby helping to resolve conflicts.
In summary, Universal Data Models can facilitate collaboration by assisting in applying the four collaboration principles mentioned in this article.
[1] One source that addresses these principles fully is Chapter 10, ‘Socializing the Patterns’ of The Data Model Resource Book, Vol. 3: Universal Patterns for Data Modeling, By Len Silverston and Paul Agnew, Wiley Computer Publishing, January 2009.
[2] See ‘Top Ten Things that Data Modelers Need to Know’ by Len Silverston.
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About the Author:
Len Silverston is a best-selling author, consultant, and a fun and top-rated speaker in the field of data modeling, data governance, as well as human behavior in the data management industry, where he has pioneered new approaches to effectively tackle enterprise data management. With over 30 years of experience as a data management consultant helping organizations world-wide, he is well known for his work on "Universal Data Models", which are described in his The Data Model Resource Book series.