KQIs (Key Quality Indicators) To Measure Data Quality

At the recent MIT Information Quality Industry Symposium, the hot topic was measuring the impact of data quality programs. In a bad economy, it makes perfect sense. If your company is cutting programs, you need to justify your data quality initiatives, or they too will be cut. My favorite presentation on the topic was from Delphine Clement, whose topic was the, “Cost of Non Quality Data.” I thought that was an interesting way to look at it, and she presented a very mature view of Data Management. Delphine credited sessions from previous MIT Information Quality Symposiums with some of the underlying theory. I’m sure there are others to credit as well, and if you know the history please comment.

Delphine reports on the Key Quality Indicators (KQIs) that matter the most to her business partners. She has taught the business community that KQIs are needed to build confidence in the KPIs. I like that the KQI approach mirrors the KPIs (in naming and level of importance), and that they are presented as a complementary report. Think of this as the metadata for the KPIs. That’s the way I rationalized it.

KQIs would make sense to any Data Quality lead, but it might not to a VP of Marketing or VP of Sales. It’s not their job to care how we do ours. So how do you bridge the gap with the executive KPI users? You must understand their needs, and show them that the KQIs are driving the data quality projects in your organization. They will only care if the KQIs help to resolve their issues. Also, KQIs may be used to show them progress in your data quality programs. When you complete a project and are able to turn a yellow (cautionary) indicator to green (good), they will understand how the project affected their work.

Delphine’s approach begins by asking business leads and other data users a simple question, “How should we measure data quality.” She gathers feedback via surveys from her business customers and measures progress through response trending over time. Sounds like internal Marketing, right? 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.

She also recommended involving the end users early on to define:

• What are the Key Quality Indicators (KQIs) that are important to the business?
• Should the KQIs be global or local?
• What is the cost of poor quality data?
• Are the KQI’s different by country?

I love these questions. Simple, direct, and open. Rather than telling our peers how we should be measured, ask them and include them in the KQI process.

MIT Information Quality Industry Symposium Day 1

I’m just settling in for Day 2 of the MIT IQIS 2009 and thought I’d throw out some thoughts for a couple of future posts I’m drafting. Here are the quick recaps from yesterday. 

Danette McGilvray - Ten Steps to Data and Trusted Information

A great primer on how to manage any Data  Quality project. Her 10 steps made a lot of sense. Not all of the methodology would be used for any given project, but still it worked for me. I also won her book, “Executing Data Quality Projects” in the drawing at the end of the class.

Bill Inmon - DW2.0 and Unstructured Data

After 10+ years in Data Warehousing I finally got to see Bill Inmon speak. Bill is the rockstar of the DW world. He’s regarded as the Father of Data Warehousing and treated as royalty at a conference like this. His new stuff was all about contextual ETL. Sounded interesting, but I believe there are others working on the same thing.

Keynote: Ronald Bechtold - Transforming the Army with High

Ronald is the Chief Data Officer at the Army. Cool title. Not what you’re picturing. He is a passionate CIO type who has a huge challenge. Definitely some words of wisdom in there. “Focus on solving problems,” rather than tools, technology or data. Good stuff!

Joe Bugajski - MDM Improves Information Quality to Deliver Value

Joe had some great examples where Data Quality actually led to increased revenue. Imagine that! Value from Data work. I think that’s what we’re all striving for. Joe is a big personality who speaks well, so this one was entertaining.

Mark Goloboy (that’s right, me) - CRM Data Quality for Sales and Marketing

After a bit of nerves, I found my groove and thought the presentation went really well. Some good questions about where my company started with Data Governance - it’s a very new ffocus or us. I also got to push back on some industry experts when asked why we weren’t focusing on MDM to start. Plus, Bill Inmon attended.

Martin Boyd - Product Data Quality Product from Silver Creek

More contextual analysis. Seemed to be done in a very smart way. The software was functional at big clients, and they had figured out how to solve some complex issues around improving poor product data. If they had the same thing for Customers, it would be a more interesting product. More development or a merger are needed here.

Demographic vs. Firmagraphic Appends

Note: This is a continuation of the B2B vs. B2C series. I will also be presenting some related material at the MIT Information Quality Symposium next week (MITIQS 7/15-7/17 2009). If you are attending, please introduce yourself.

Data Acquisition is an important part of any data driven Sales and Marketing program. I associate the append process with Marketing since that’s where these programs are generally funded, but the clear beneficiary of the work is the Sales organization. And of course, it takes a partnership between Technology, Sales and Marketing to make data acquisition programs successful. A data file on a shared drive or standalone table in a database has little or no value. The data has to be integrated with customer and prospect lists and loaded into the CRM and SFA systems and / or presented through your BI/DW tools to show ROI. Sorry… I got on a roll with the acronyms. They’re defined at the bottom. I’m in a Data Governance role now. Acronyms and definitions are my life (in a scary, scary way).

This raises the topics of Data Cleansing, Address Standardization, Merging & Matching, Deduplication, Archiving, etc. I’ll save those topics for future posts, but would encourage questions in the comments section. It’s fair to say that without a Data Quality focus you won’t achieve the optimal results for either Demographic or Firmagraphic Data Acquisition program.

Demographic Appends

Demographic data is generally used by Marketers and Analysts to determine the best way to target a consumer base. Starting with a universe of customers or prospects, a company can buy information on:

• Credit History
• Purchasing Patterns
• Housing Location and Situation, e.g. owner, renter
• Salary
• Lifestyle / Family

You can also buy data that segments customers based on the provider’s best information. Some is based on geographic of financial information. Others are based in buying patterns. Sometimes they even have cool segment titles like “Boomer Barons,” “True Blues,” and “Jumbo Families.” (All of those are from the Acxiom Personicx family of pre-defined segments). The goal of a demographic append is really bulk segmentation. How do I get enough information about my consumers to segment them for marketing and analysis. In most cases, the purchaser doesn’t care about any single individual or household, other than to get them into the correct program. The marketers are looking at sets of consumers, and then targeting from there.

In very high end consumer sales, e.g., luxury items, high net worth banking etc, you may find cases where sales people will use the appended data at the individual level. But generally it’s used for grouping customers into buckets, finding buying patterns and trends across like customers, and performing analyses such as next best product, lifetime value, and similar. Consumer sales is so transactional, that there isn’t time to research before any one conversation.

Firmagraphic Appends

B2B data, which I prefer to call “Firmagraphic” but have also seen called Firmographic or Firma-graphic, can provide value for not only segmentation, but also for pre-call research. B2B Sales is moving toward a more consultative approach where the sales rep becomes more of a partner with the purchaser. The best sales reps do this across all businesses. Rather than try to sell one product or service (transactional), the sales rep tries to understand the company’s need and deliver a suite of prodcuts & services and sometimes even the workers to use them (in an outsourcing arrangement). I’m sure if you’re reading this you’ve seen the varying styles of transactional and consultative sales reps.

To arm those consultative sales reps with the appropriate information, companies often purchase firmagraphic data and load it into their CRM systems. This data may be grouped into:

• Parent Child Relationships
• Locations of Related Companies
• Contact Information for Executives
• Industry Codes
• Number of Employees
• Revenue

This allows the rep to quickly look at the companie’s situation, and taylor their initial pitch accordingly. “I see you’re in a fast moving industry, with over 100 employees, and that you’re company has decentralized offices across the country. Have you heard of Product A that might meet your needs?” That conversation can only happen to a perspective customer if a firmagraphic append has happened previously. Which reminds me that another form of firmagraphic purchase is the ubiquitous, “Get 200 leads in your target industry,” but that’s not what I’m talking about here.

Segmentation of B2B customers is also sometimes based on Firmagraphic appends,  but as mentioned in the original B2B vs. B2C post, householding in B2B is focused on creating parent child relationships among your customers and prospects. You should also use all of your existing customer information including location, purchasing and servicing history, and past communication response to segment your customers. Once you have those relationships built, you can begin to analyze your coverage of headquarters and branches based on firmagraphic data.

Analytics then focuses on similar models for next best product, lifetime value, likelihood to purchase, likelihood to respond to certain campaigns and others. The tools to d

Acronym Glossary

CRM: Customer Relationship Management. Generally refers to the systems used by Sales and Marketing teams to store and organize customer contact information, purchasing, and servicing history. CRM systems also pump out lots of data used for operational reporting and as inputs to customer analysis and segmentation.

SFA: Sales Force Automation. Tools used by Sales Reps to manage their actitivities. This would include follow ups/reminders, appointments, leads, renewals, etc.  SFA systemsalso includes the operational reporting of those activities in some cases.

BI: Business Intelligence. The analytics and reporting tools built on top of the Data Warehouse. Business Intelligence can also be used to describe the practice of analyzing data to determine important insights and drive strategy. These tools look across CRM, SFA, manufacturing and financial systems.

B2B: Business to Business. Marketing and Sales activities along with associated products and services targeted at business customers.

B2C: Business to Consumer. Marketing and Sales activities along with associated products and services targeted at individuals or households of consumers.

ROI: Return on Investment. A simple calcualtion that takes the difference between the cost of a program and the returned value from it, and divides it by the cost of the program. So if I spend $100 and make $150, my ROI would be the $50 of benefit over the $100 of cost: .5 ROI. People often forget to subtract the cost from the numerator and inflate their ROIs.

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.

Upcoming Blog Posts

June has been a light month for blog posts. I’ve settled on upcoming topics, and even drafted my next couple of posts out. Why the slow month? I took a detour into Lean BI research that proved uninteresting to write about. I’ll scrap that. Gone also is my brief foray into Cloud Computing for BI. I’m just not an expert there. Good to learn about, but nothing I want to associate my name with yet. I also had to knock out a conference presentation for this Summer.

I’ve settled on the following upcoming topics. Bear with me while I get them out there. Enjoy the previous posts in the Data Governance folder. Also, please comment if there are any areas you’d like me to explore.

1) Lightweight Data Governance: A Starting Point

A continuation of the previous post. I’ve finished my MIT Information Quality Symposium presentation for this Summer, and think it would be helpful to write some background on the theory I’m developing. In a recent conversation with a Data Governance colleague, they referred to the work as “different from what everyone else is doing.” I hope that’s a good thing. Either way, it confirmed that the presentation will be provocative for that group. So I’ve got that going for me.

2) Data Quality and Data Governance Blogs I’d Recommend

I’ve been keeping a list of those bloggers I think are really good. I look for people who put their opinions out there, and keep the topic light. I’m also a fan of those who are tool agnostic. Too many in our field are married to their vendors. It’s a bad position to take.

3) Demographic and Firmagraphic Appends

A continuation of my B2C vs. B2B series. In this post I’ll explore where the value is, how to incorporate and whether you should even bother. Have you maximized your own data first? Are there other ways to get access to this data for free? Some podcaster’s I follow think so. More to come in the blog post.