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

1. Define Business Need and Approach
2. Analyze Information Environment
3. Assess Data Quality
4. Assess Business Impact
5. Identify Root Causes
6. Develop Improvement Plans
7. Prevent Future Data Errors
8. Correct Current Data Errors
9. Implement Controls
10. Communicate Actions and Results (this is done throughout steps 1-9)

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.

I’d like to start, though, with key takeaways from my half-day session (on the 1st day): “Using Data Profiling for Proactive Data Quality Improvement” by David Plotkin of Wells Fargo Bank. As you probably know by now, I maintain that a good data quality program is a key piece to making Data Governance successful (and vice-versa). This session included some great tips for starting and sustaining data quality. Read on for notes from the session…


David Plotkin is the Data Quality Manager for the Wells Fargo Consumer Credit Group. He began his presentation by outlining the importance and differences between proactive and reactive data quality… the differences are (note that he refers to the business units as his ‘customer’:

Reactive Data Quality

• The customer tells us about data quality issues
• The customer tells us what they think the issue is
• Issues are logged and worked on as time permits
• Users uncertain about who to report issues to or status of issues

Proactive Data Quality

• Review existing elements for data quality
• Review new elements as they are brought in
• Publish results and trends in Data Quality (scorecards)
• Solicit feedback from key business users on results

There are many reasons that proactive data quality is better than reactive. I think both are necessary, but the more you can catch upfront, the better!  A few noted benefits to proactive DQ were: making our jobs easier and less frustrating, increase customer confidence, save money, catch things before they break systems.


To start, David recommends prioritizing issues via a cost/benefit matrix. The projects that have the lowest cost and highest benefits ratio would be best to start with. He then recommends collecting business rules via a standard template that you will use for all rules going forward. He didn’t provide a template, but I would suggest that the template would have a minimum of: data field, systems impacted, business units involved and known issues. The rest of the fields would depend on your organization.

I think they key is to provide a nice ‘snapshot‘ for the business to view the data field so that they can easily capture (or provide) the business rules.

The remainder of the session focused on data profiling tools and the great opportunities they provide to quickly and easily identify issues. David acknowledged that data quality is not tool dependent, and that “big things can be done with small tools” but did concede that tools certainly allow us to be more productive because a ton of manual processing is reduced or eliminated.  Tools can review all of the values in a specific field and tell you the mean, media, mode, outliers, trends, and tons of other statistical information that you can use to make good decisions from.

You can see many tools being advertised on this site, or google “data profiling” for more data profiling products that you can shake a stick at. The sponsors room also had a bunch of companies whose tools did data profiling in some way or another. Rather than me rehash the notes on how you can use these tools in a proactive manner, I suggest you do some research on data profiling tools with the companies themselves – trust me, their sales people will be more than happy to show you what they have to offer