Pick The Low Hanging Fruit of Data Quality and Data Governance

This has always been my favorite terrible consulting / business cliche. I suppose I’m using this forum to solidify it’s status there, but I imagine many of you have been told or said something very similar. Of course this fits into my Lightweight Data Governance theory as well.

Saying that you are going to Pick the Low Hanging Fruit resonates with budget conscious managers and technologists who want to see quick results. It shows that you are unwilling to get bogged down in low value projects, and that you want to make a difference quickly. And, with slim budgets for new tools and consulting services due to the Economy, it’s a good approach for Data Quality and Data Governance today. Now which sagging branches are the most attractive?

Review Existing Processes

Have you reviewed your matching logic for external data entering your systems? What about the rejection rows from your ETL? These activities are essentially free - you can do them while you’re sitting on conference calls or waiting for others to join a meeting. They don’t take long but you may see patterns that help you to recommend great new projects.

Rethink Rollout of Underutilized Tools

I was at a conference recently and saw a demo of a Data Quality report from a vendor we work with. I went back and asked my Sales Rep if we owned the tool, and sure enough we do. It’s part of a larger contract, but no one is using it. Ca-Ching. That’s a free reporting tool from my perspective. How am I going to use it? To rollout Key Quality Indicators (KQIs) of course!

Educate, Communicate, and Build Relationships

Another freebee. A down economy is a great time to reach back out ot the business to understand their issues, and how you can help to resolve them. Also, take the time to formalize your message. Create a "walking deck" if you don’t have one. A walking deck is 3 or 4 slides you keep in your binder that you can present whenever the topic comes up. I use these when I meet someone new to quickly educate them on the Data Governance work at my company. It’s a relationship building opportunity that could lead to a new sponsor or commitment from a new department to join in your efforts.

Please comment with other ideas!

Data Governance During Organizational Change

This is a continuation of the Lightweight Data Governance series, but very much applies to formal data governance as well. I even find myself using formal terms like Data Steward and Executive Sponsor below, so it definitely applies to both.

One of the most frustrating areas of Data Governance is organizational change. Companies change because of growth, change because of decline, change because of new opportunities, but the result is that executives turn over rapidly. At times of transition, you must be proactive and communicate the value that Data Governance brings to your organization.

Business Turnover - Data Stewards

You’ve spent the last year or two collecting data stewards who know the business, and aren’t afraid to tackle the difficult data and process issues at the company. Then one of your favorites leaves. Has anyone else noticed the “Going POCO” trend? Pursuing Other Career Opportunities? In some cases, we never know if the resource was fired or quit. I guess it shouldn’t matter, but I know I’m always curious.

This case will show how well you’ve built relationships at your company. Do you already know other colleagues in the department you can invite into the Data Governance role? Have you educated the executives so they understand that Data Governance was an key element of the departing employee’s responsibilities? If not, it’s time to get cracking.

Technology Turnover - Systems Owners

This one has given me the most headaches. You’ve finally got your projects on the technology team’s roadmap and have communicated it to all the right people. And then they leave the company. The difference here is that you’re not just talking about one individual, but a commitment to spend valuable time and resources on your projects.

This situation requires you to communicate with the new owner, and introduce the value of the work. As in the example above, it would also help if you’d built relationships with others in the technology group. You may even luck out, and already know the new owner! That’s obviously the best case scenario. If not, reach out and introduce yourself and your work. Keep in mind that Data Governance work provides value to the system owners since it increases the end user perception of how well they are doing their job.

High Level Executive Turnover - Executive Sponsors

When someone in your departement’s leadership team moves on, or if one of your data steward’s executives leaves the company, you will need to begin building a new relationship. Communication to the exective level is all about value and solving business problems. Don’t get bogged down in the details. Most executives don’t care how you will solve their problem, just that you understand it and have a way to fix it. If you find yourself showing architecture diagrams and explaining Master Data Management (MDM) theory, start over. You’re at the wrong level of detail. Instead, show them the money! How will you reduce cost, drive revenue, or fix a compliance issue? Answer those questions, and new executive sponsorship shouldn’t be an issue.

MIT Information Quality Symposium Day 2

With Day 2 of the MIT IQIS complete, I thought it would be good to write up another summary. I was very impressed with the quality of speakers and their dedication to the field of Information Quality. The work shows a lot of innovative thinking and pride. (I’ll add in links and update later today)

Robert Grossman – Information Quality in the Cloud

Bob is part of the Open Cloud Consortium and passionate about the topic. He presented everything you need to know to understand where Cloud Computing is today, where it’s going next (based on open debate among dueling standards boards), and how it affects Information Quality discussions. He has a unique ability to take very complex topics and break them down into simple conversations.

The most interesting part for me was defining Public, Community and Private Clouds, which I couldn’t have described before this talk. I also appreciated his comment that Cloud is the only way to analyze 100TB of data, and that the alternative is to merely entomb it.

Delphine Clement - Cost of Non Quality Data

Delphine is from HP in France and discussed how they have approached their KQI – Key Quality Indicators. I like that KQIs mirror KPIs but that Information Quality is metadata reporting rather than business metrics so it’s separate. Delphine also presented a methodology for measuring direct vs. indirect cost savings from Data Quality initiatives. She has clearly spent a lot of time working on this approach and is doing a great job. I really enjoyed this presentation.

Lyn Robison - Diagnosing IT’s Impact on the Business

Lyn, from The Burton Group has a theory on how to measure data quality from an IT perspective, but I thought it was very pie in the sky. There were lots of questions about the politics of such an effort, and I don’t think the approach was practical. For instance, if your measured data quality metrics turn up as poor, the IT organization will blame the business. There’s no way this could work politically.

I liked that Lyn tried to compare the business people’s perception of Data Maturity vs. the IT perception, but how do you align IT perception and Business perception? Someone also asked, should IT be measured on poor data quality? The answer: Not if the Business owns the data.

Steve Sarsfield - Using Data Quality Scores to Sell IQ Value

Steve echoed others who encouraged Information Quality progress by “Leveraging a Crisis” to build momentum. He also asked us to present the “Do Nothing” approach, i.e. present to our management what would happen if they ignored the problem. Steve’s scoring method was based on the Trillium TS Insight product, but appeared to be a practical way to measure Data Quality. I think some of this can be done easily with or without Trillium, but I appreciated how the tool can manage the measurements over time.

Marillo Boccia – Data Quality in the Media Industry

Marillo is the Director of Database Marketing at Grupo Abril, the largest publisher in the Southern Hemisphere. He presented a project (done with the help of service provider Assesso) where his team personalized magazine ads for Banc Itau to 1.2 Million subscribers. Cool stuff. They merged their subscriber database with the bank’s and did a massive customer data cleanup to ensure very high data quality. They amazed their customers in the process.

Dan Defend and Aparna Vani - Data Quality Challenges for Yahoo’s Massive Data Environment

Dan and Aparna presented the Data Quality and Analytics sides respectively. They monitor website interaction and uncover trending and outage information by analyzing a constant flow of clickstream data. Their group deals iwth duplication challenges, security issues, and the need to report outage alerts instantly. Their work was also driven by past MIT IQIS conferences, and they presented their practical approach to establishing a central data quality process and framework.

DEBATE: How should data governance and data quality work together?

I added a comment to a fun debate on the Data Quality Pro site. The question was around the interaction between Data Governance and Data Quality. Most people agreed they were connected, but I think some people are living in the details of Data Governance. It has so much more potential than just fixing data models. My thoughts…

DEBATE: How should data governance and data quality work together?

I think people are on the right track linking Data Governance and Data Quality. No need to rehash above. My one input would be that Data Quality should be the measuring stick for the success of Data Governance programs.

I do feel that some people align Data Governance too closely with data modeling and data object definition. There are examples in the comments above. Sure clean data is the end goal, but Data Governance is the journey to get there. Data Governance is more of a cultural shift starting with 1) Aligning business strategy toward a common goal; 2) Building definitions, re-engineering processes & updating systems to represent those aligned business strategies; 3) Defining data objects to store the common definitions; and finally 4) Measuring success based on quantitative analysis of Data Quality. Without that, Data Governance becomes a theoretical data modeling exercise, and all of our work is minimized. I’m sure this is more of a top-down approach than the classical definition, but it’s where Data Governance will go next.

Lightweight Data Governance: A Starting Point

This expands on the previous article, Lightweight Data Governance. I’ll continue to add to the theory in upcoming posts. If there are any areas you would like me to focus on, please add a comment, or email me directly.

A few weeks back I met with Steve Sarsfield to discuss the upcoming MIT Information Quality Symposium (MITIQS). It will be my first time presenting to a Data Quality focused group, so I was excited when Steve offered to provide some background. My main concern was, “How can someone in the commercial space keep the interest of a combined business, government and research focused community?” We discussed my approach, and I think I’m on the right track. I’m going to describe how we initiated Data Governance at my company, kept it simple, and found early success.

So where did we start? Data Governance grew from an expressed need by the executive team for better data quality. Sounds simple right? Fix the data. It’s like the Kenan Thompson SNL character talking about the economy: Fix It. The company decided that Data Governance was needed, and that they would let me define the path to getting there. I set the scope to include any project where I have an opportunity to build credibility in data or reporting. I’ve formalized processes where necessary, but kept it “lightweight” in most areas. With the current state of the economy, I see no other way to get there.

I previously led the Marketing Analytics department, and we had responsibility for B2B and B2C Analytics. Most of our efforts were focused on the B2B side, since that’s where the most perceived opportunity existed. When I moved into the Director of Global Data Governance role, I built from my strength and worked on B2B issues first. I attacked the low-effort, high-value projects. I looked to expand on the local efforts that were working well. If teams or projects came up with creative solutions, I looked to expand their work globally. My thought was that it’s really hard to come up with the underlying process definition, but that an existing process was easy to expand. It doesn’t work for every existing process, but some are natural fits that resolve longstanding internal issues.

That became the basis for Lightweight Data Governance. Find the projects or efforts that are successful on a small scale, and expand them globally. That way you start with a base of knowledge, documentation, and executive support that’s very hard to build from scratch.

Grow Data Governance efforts organically

Start with existing processes. Find out which can be expanded, centralized or automated.

Focus on project level ROI

Don’t try to sell your management on a huge program to start. Build the business case at the project level. It’s easier for management to support small positive ROI projects.

Partner to be unobtrusive to ongoing work

Find projects that are already in flight. Would Data Governance add to their impact? If so, partner with their leadership to help craft the deliverables to create mutual benefit.

Build momentum from early successes

Get testimonials! If the project went well and the community benefited, you should be able to get the project sponsor to say so.

Measure initiatives on DQ impact

This step is further along the Data Governance continuum. Begin to show the impact on the organization when projects focus on data quality. This cultural shift will underscore the importance of future Data Governance work.

Follow with Formal Data Governance

Does it make sense for the enterprise? Does executive support exist? If not how do you build it? This is where the more traditional theory in most Data Governance efforts becomes relevant.