A Report on Len Silverston’s Enterprise Data World 2011 Conference Presentation
by Charles Roe
At the beginning of his Enterprise Data World 2011 Conference presentation, Len Silverston, the best-selling author of Universal Data Models (in 3 volumes), compared his life to the movie Groundhog Day. He did it jokingly, but even within this opening icebreaker, he gave an important message: human dynamics is the most crucial part of a successful Data Management, and most specifically, a Data Governance project. His work is often a pattern that happens again and again, just like the movie, as he talks to people that have similar issues getting a Data Governance program off the ground or working effectively within a given organization. This article is a report of his presentation. It will cover the highlights, including: the six inevitable scenarios that most often occur with such data management projects, what do organizations or those working with the project need to do for it to be successful, what principles need to be followed and some tools he advises people to use. All of the graphical elements in this article are taken directly from Mr. Silverston’s PowerPoint slides.
A successful Data Governance program needs many elements to work. These include:
Even with all of those elements in place, with buy-in from the entire organization through all levels, it does not mean a Data Governance project will work. Human dynamics can still derail the entire process. Mr. Silverston gave two examples of projects he worked on that had varying levels of success based on those elements. The first had all of them, including top level CEO support, every element was arranged and the project still failed miserably. The second hardly had any of them, including very little top level commitment and lack of resources, but succeeded with amazing results. The primary difference between them was fertile ground; the successful project may have lacked so many traditional elements, but they did have proper channels of communication, sharing, positive relationships and big picture vision. They had fertile ground, instead of rocks, to plant their plan for Data Governance in. Fertile ground allows an organization to (hopefully) distance themselves, or if not distance then deal more effectively with, the six inevitable scenarios that often disrupt the effective implementation of a Data Governance program.
1. Scenario 1: Data “Mine”ing
Data “mine”ing is not to be confused with data mining – they are entirely different terms. The first inevitable scenario in the disruption of a Data Governance program is perhaps the worst culprit of all and it must be solved before any of the others can be dealt with. Data “mine”ing means that someone, or multiple someones, in a project have declared that all that data is “mine!mine!mine!” Thus, they are sitting on top of the data and will not let go or share it. Data is a corporate asset; data is dollars and without proper data sharing between the many stakeholders in a project, that project will fail. It doesn’t matter if a salesman in the organization has built up their contacts over 30 years and so does not want to share it into the grand new Data Governance program – they must. Their data is valuable to the entire organization. The trick is getting them to share and that takes building trust, creating a common vision and providing them with the motivation to share. Arguing with them will get the project nowhere; they will just become more covetous and intransigent about their data.
A. Principle 1: Don’t React – Observe
To relieve the tension created in Scenario 1, it takes the removal of oneself (in terms of the person trying to get the data from the intransigent person in question) from the entire equation; they need to “step to the balcony.” Once the data “mine”ing event happens then it can no longer be stopped, the feelings of anger or resentment cannot be stopped, but a calm reaction with complete awareness of the issues involved in the situation can happen. It is necessary to create space within the issue, not lay blame at anyone’s feet – there are no villains or angels – and then come to an agreement about what needs to be done to move forward. If the data coveter in question is just saying “what’s in it for me?” then provide them with motivation to let go of their data; build trust with them and work with them to show why they are important in the common vision of the data governance program.
2. Scenario 2: I am Right!
In any collaborative project there are always multiple views. In Data Governance such problems occur within teams and also at the project versus enterprise-wide level. Neither of the many views is right, all of them are. In a large government health agency project there was a problem with defining blood types in a model for a person. There were two different systems at work and both said they were correct in terms of their belief about the correct blood type, but how can someone have two blood types? Give a person the wrong transfusion and they die. Both sides were right and wrong in this scenario, but they needed to come to an agreement – which system was correct? A or B? They both said “I am right, my data is correct and your data is incorrect.”
B. Principle 2: Disarm – Step to the Other Side
Each of these principles builds off one another. In the first principle, the Data Governance team member had to not react. In this scenario, both sides need to listen to one another, understand their point-of-view, acknowledge their relevancy, appreciate their importance, respect them, apologize for not accepting them (or their point-of-view) and then contrast their point-of-view to the others so that a collaboration can be attained. This sort of scenario first takes anchoring oneself and creating a “self-understanding,” before it’s possible to work effectively with someone else of a different viewpoint. Once a person has a self-understanding, they can then collaborate within someone else and honestly listen – it all begins with self. Two blood types cannot exist for one patient, so they had to work together.
3. Scenario 3: Enterprise versus Project
There are usually a minimum of two perspectives in a Data Governance project: the project view and the enterprise-wide view. The enterprise Data Governance professional says: “We are chartered to work together with projects like yours so that projects define, integrate and use data in a common, consistent, and powerful way across the enterprise.” The project team member says: “We completely support enterprise wide Data Governance. However, we have very tight deadlines and budgets so we’ll work together as long as you don’t impact our deadline dates, tasks, resources, or budgets in any way.” With such wonderful and collaborative mentalities why would anything ever go wrong? They both seem to want the same thing, but the caveats can derail the entire project. Both sides must work to see the whole picture and why both perspectives are necessary elements in the entire process. Such a whole picture emphasis takes a reframing of the issues at hand, rather than rejection. If there is a problem with tasks or resources, then instead of the project moving forward and rejecting the enterprise viewpoint, the problem must be reframed.
C. Principle 3: Make it Easy to Say Yes – Build a Golden Bridge
Reframing of issues requires an understanding of position versus interest. The project’s position may not be congruent with the enterprise’s position. Instead of just rejecting each other and causing further issues, both positions need to be properly stated, along with the voicing of interests. In the salesman scenario discussed earlier, a forceful approach would not work; it would just cause more intransigence. Thus, reframing the issue with open-ended questions such as “why?” or “why not”, “what if?” and “what’s fair?” may get the salesman to offer up alternatives. He wants to know “what is in it for me?” so open-ended discussion may get him to provide his own ideas. Instead of the enterprise saying “give us your contacts or else,” they could work to find a common ground, voice their position in terms of the importance of the data, their interest in it, but from the perspective of the salesman. Instead of a single point-of-view that creates polarized arguments, expanding the position to find a common ground will help to create paths of common interest; build the golden bridge together.