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.

B2B vs. B2C Matching for Sales and Marketing

I recently read the KnowledgeBanks article Why is b2b marketing different from b2c marketing? The article works to disprove the common misperception that “B2B marketing is just marketing to consumers who happen to have a corporation to pay for what they buy.” I completely agree, and would like to extend the points made to include the differences in matching, sales, and marketing. I’ll also point out ways to know if you’ve reached your goals.

Some background before I begin. I spent two years working for Harte-Hanks implementing primarily B2C matching for Financial Services Marketing systems. Did you ever wonder how the banks knew that your accounts were linked even though you opened them as different names and addresses? Think about Bank of America’s history. It’s a collection of dozens of banks, and you could have opened accounts any time in the past. To merge that data which could include 10-100 million rows of accoutns, massive B2C matching systems are required, and equally complex logic. My work at Monster has centered on B2B data and how it’s used by Sales and Marketing teams. I’ve worked primarily with matching engines based on Trillium software, but I’ve gotten to know most of the other technologies used at an Enterprise level over the years. I’m a free-agent when it comes to technology, and I’d recommend all technologists embrace an open mind when it comes to vendor selection.

B2C Matching

B2C matching is absolutely nothing like B2B matching. The difference? Householding. In B2C the goal is to household different contacts at the same address. You household John’s accounts, then you household Mary’s accounts, then you merge them both together if they live at the same address. Sounds simple right? Well, sometimes John goes by Jack, and sometimes Mary uses her maiden name, and their phone company and credit card records are all listed as “M & J.” These are the troublesome records to figure out, and most of the records have some flavor of variation similar to this.

B2C Sales and Marketing relies on volume, and the companies focused on matching process huge data sets. You need to find many interested parties because the available money from each is very small. A person may buy $100 worth of software, a $2000 computer, or even a $20,000 car. But they will never spend $1MM with you unless you sell houses to the moderately rich or toys to the ultra rich.

B2B Matching

B2B matching is also about householding, but we don’t call it that. We call it Parent-Child Hierarchies. The goal is to determine all of the locations of a business that you are selling to, and try to figure out how they fit together. Of course this is an enterprise perspective, and SMB would be more focused on single locations. So the enterprise question is, which locations are headquarters of other branches? Does that headquarters control purchasing for the child branches? Or are the branches empowered to buy on their own? What does the sales history tell us about them? Do the reps know anything that can help, and how do we capture that data in an automated matching process. All that, and I haven’t mentioned that companies buy, sell, and merge all the time. Think about GE, Berkshire Hathaway and Tyco. Is each of those 1 business or 20?

For B2B Matching each sales and marketing person would like to know how much each location has purchased and which has the purchasing power. Some of those locations will purchase services that could result in multi-million dollar deals. When I worked for Accenture the philosophy shifted to “Big Bets” and the partners (who functioned as sales people) only targeted accounts willing to commit to $25MM per year. That year they sold several Billion dollar deals. Think about that for a minute. The sales reps will need appropriate level high quality contacts and contact information at each location. You can’t sell a $25MM deal to a line manager or team lead. There are many ways to get B2B contacts - list purchase, telemarketing, partnerships, etc. - but that’s a different post.

Measuring Results

If you think you’ve reached your goal and found success with your matching, you’re wrong! Matching is more of an art than a science, and as soon as you get to an acceptable level of completeness and accuracy you need to start looking for the next round of improvements. Matching (like data warehousing in general) must change with the business. As new products are developed, new technologies released, and new business processes implemented, the matching must be updated to dovetail with those changes.

To Address in Future Posts…

Preferences and Opt-out Management
B2B vs. B2C Analytics
Number of Services per Contact (B2C) vs.
Number of locations per Company (B2B)
Demographic and Firmagraphic Appends

Meet the Data, Technology and Analytics Blog

This is the first in a series of blog posts discussing important trends in Data Governance, Marketing Analytics, Sales and Marketing Technology, and Social Media. I’ve decided that in order to learn from the constant flood of information on podcasts, blogs, books, and internet articles, I need to embrace the technologies and put my opinion of them out there for others to review.

I have included links to more information about me, and will integrate personal and professional information into the site in the future. For now, check me out on LinkedIn for professional interests, and Facebook for personal interests.

Please visit the site to see future posts, and leave your feedback.

Mark