Another idea often bounced around at conferences is that only a short period of time is needed to make the guidelines and exercises that these data governance rules control. On the other hand, the process of understanding the choices, arriving at an agreement, assisting in decision-making, agreeing on deliverables, confirming areas or responsibility, etc etc etc can taken an excruciatingly long period of time.  While I agree with the sentiment that the basic tenants of Data Governance can be agreed upon quickly…. As they say, “the devil is in the details” and these don’t tend to work themselves out very quickly. 

The success of any program on Data Governance relies on the utmost capacity of a Data Governance worker to effectively work with and coordinate with Data Stakeholders and Data Stewards. How can these Data Governance workers do this task effectively? First, they have to create a very effective Communication Plans. They have to construct fail-proof mediums of communications like Elevator Speeches, Impact Statements, Presentations, Governance Status Reports, emails to and from the Stakeholders and many more. Without these plans, there is no way that a Data Governance worker can fulfill his/her duties very well.

The key I got out of this presentation is to center everything around the benefits. Talk about a powerful position you put yourself in when you can talk to the executives in terms of how much revenue you are responsible for! His tips are to have a team that has analytical skills, finance and accounting skills, tech skills, etc. Make sure their talents are diverse. He said that anybody can learn the needed technical stuff (he has a management, not data, background), so focus on building a team that is competent and well-rounded.


In an interesting side note, Avaya was recently purchased by two private equity firms for $8.2 Billion. In one of my recent data governance article roundups I posted an article by Philip Howard that stressed that a company looking to be acquired should really have their data management under control. Who knows if data played a role in this acquisition, but as you can tell from my writeup, Avaya really does have theirs under control.

- Have a straightforward vision, theirs is: “Elevate Data to Enterprise Intelligence”. His goal was to be the brain of the organization through data quality.

- Achieve Data Governance through the creation and storage of the critical business rules (1) Define business rules (2) Provide global visibility of data quality to whole organization at all levels

- Data Governance exists in every organization (creating rules, defining rules, etc…) whether they know it or not. What many do not have is a formalized process – what we really think of when we think data gov


- Logo is important – when people see it on documents they automatically know its from his team. I wrote an article a while back that talked about the importance of branding your data governance program.

- Develop value drivers that are important to your organization, not “BS”. It doesn’t have to be what you read in magazines, books, or hear at presentations, it should be what matters to your organization. Ideas: Improve time to market, Have better decision making…

- Use the vocab your organization understands. At Nestle, the CEO and management signed a document six years ago that they would do Data Ownership. The industry now prefers data stewardship (because of data my-ning). He decided to stick with ownership because that is what works in his organization.

- Tools are helpful. You can achieve big things with small (or no) tools, but they really do help

- Put your metrics in positive terms. For example, say “80% of a data field is correct” rather than “20% of a field is incorrect”.

- He didn’t create the data governance/quality organization, he just formalized what already existed

- Proposed adding a Q in ETL (Extract, Transfer, Load). At Nestle it is ETQL – Extract, Transfer, Quality Check, Load

The first tool had us look intrinsically at why we do things. On the top of a piece of paper we wrote down the name of a project or program we are currently working on. On the left side of the paper we wrote down how a “program or project will help me” and listed all of the reasons. On the right side we wrote “how the project is an obstacle to me” and listed our honest reasons. This tool is the first step in creating another of Len’s resources called the Power Diagram. Filling it out about yourself clearly defines your motives, and the rest of the diagram has you doing the same thing but for everyone else involved in the project (fill out what you think their motives are as well as their obstacles). This bring back into focus the “me and my” thoughts that were first discussed because it quickly becomes apparent that everyone has their own personal motivations for doing (or resisting) a project.

The next item discussed was the vision for your program. Len thinks that most people create their project vision in such a way that it encompasses the business mission statement. In fact, the opposite should actually be done, and Len displayed a diagram where the business vision encompasses the project vision. He states that, “When we look at the larger picture, in reality, our job in data quality is to support the overall business – just like all the other aspects of the enterprise.”


The last part of the session had us focusing on goals and conflict resolution. In brief, we should always keep the overall goals insight. As for conflict resolution, the following is given for how to overcome it:

1. Don’t react. Stay objective.
2. Disarm. Step to their side.
3. Change the game. Don’t reject… Reframe (holistic, common goal)
4. Make it easy to say yes
5. Bring them to their senses, not their knees (using power, not force)

All in all, I’d highly recommend this session. The principle taught can be used across a wide variety of projects, programs, and organizations.

Len Silverston is a consultant and the President of Universal Data Models.