Five New Ideas From 2010 MIT Information Quality Industry Symposium

Here are some quick thought from the first day of the MIT Information Quality Industry Symposium. It’s my favorite event of the year. I refer to it as the “anti-boondoggle.” All academic theory and very little vendor fluff. I suppose that what you get when MIT and the University of Arkansas organize events. I’ll either post another top 5 tomorrow, or a full recap.

Please comment if you’d like me to dive further into any of these topics.

1) Cloud Is No Longer The Focus

Last year everyone talked about Governance in the cloud. This year it’s dead. Why? I think it may be that this group, unlike the Sales 2.0, is focused on Enterprise scale monolithic systems. Last year at MITIQIS, many presentations were focused on the cloud impact on large scale information quality programs. This year, it’s all about internal, installed systems. I find this facinating. Did this group try cloud, and not see the value? Or is it that there is still a duality of idealogies: One that prefers to keep things internal, and a second that wants to move their IT responsiblility to SaaS apps?

2) Master Data Management (MDM) Isn’t The Only Solution

I was surprised that among the Information Quality vendors and practitioners, MDM was no longer the focus. Joe Bugajski focused on it, but others merely touched on how they would interact with MDM rather than focus on MDM as the central system in an Information Quality focused environment. This year, many people talked about Information Quality at the system level, and fixing business process and human interfaces to eliminate dirty data at the source. This reminded me of the Data Warehouse to Data Mart paradigm shift of 10-15 years ago. I just felt old writing that.

3) Data Quality is a Dirty Word

“Information Quality” is now in vogue. I was corrected several times in conversations when I mentioned data quality. This is somewhere between a more highbrow way of marketing ourselves, and snobery. I don’t think this matters in the least bit, but others believe it’s more accurate and lends more credibility to our practice. As you’ll notice throughout my writing, I resist heavily the practice of pluralizing the word data. I never write, “data are,” which I believe is gramatically accurate. I feel the same way here. I do “Data Quality” work, regardless of who says that term is wrong. All right… I’ll use it in this post and try it on for size. This is the Information Quality Symposium after all.

4) Free Sources Drive Down R&D Cost

Data is available from government sources and tools are available from open source communities. No surprise there, but there was in increased focus on it here at MITIQIS. Why? Talend, an information quality vendor, builds their tools on the back of those open source libraries. They credited various shared data models, methodolgies and data sources that allow them to shortcut proprietary R&B spend. Trillium also spoke up, and mentioned that they leverage some of the same open-source thinking in their full price solutions.

5) 60-90% of Operational Data is Valueless

I won’t say worthless, since there is some operational necessity to the transactional systems that created it, but valueless from an analytic perspective. Credit to Kirk Amidon for this insight - he attended the session where this stat was quoted. Similarly, Steve Adler from IBM and others discussed it in their presentations. Data only has value, and is only worth passing through to the Data Warehouse if it can be directly used for analysis and reporting. No news on that front, but it’s been more of the focus since the proliferation of data has started an increasing trend in storage spend. That wasn’t discussed at the conference… just my opinion.

CRM Marketing Strategy To Drive Sales Revenue

For the purpose of this post, I’m defining Marketing Strategy to mean the analytics that complement the work done by the Database / CRM Marketing team and the influence that those analytics can have on Sales and Marketing programs. This article isn’t about Brand, Creative, or Media Purchase. It’s also not about Social Media, SEM or SEO. Just to be clear, I’m writing about using Data, Technology, & Analytics to reach out to customers and prospects, improve program results and drive revenue. Generally my recommendations are specific to Email Marketing, but may also be applied to Direct Mail or TeleMarketing.

Marketing Strategy should be the heading under which your company defines it’s analytically based sales programs. But you don’t need a fully developed internal analytics shop to be strategic. In fact, that isn’t the first step at all. First, focus on your data. It doesn’t matter how good your analysts are, they won’t be effective or efficient if you give them low quality data to work with.

So, let’s begin with the data. The following three areas of Marketing Strategy rely on Data Governance, so for my opinions there read up on Lightweight Data Governance.

Opportunity Analysis

If your data isn’t clean, you can’t get a feel for how well you are doing. For instance, duplicates in your data when analyzed in aggregate cause double-counting within buckets, double-counting across buckets, and general noise that diminishes the value of analytics and modeling. If you have the same company listed as both a customer and a prospect, and they both fall in the same industry or geography, your analytics driven programs will fail.

The solution is to develop a source system down approach to gathering data for your Marketing Strategy work. If you collect the data with reporting and analysis in mind, downstream processes will benefit. So work with your Sales, Service and Finance teams. It will take them an extra few seconds to correct the problem. Every customer facing employee should spend a few seconds each time they speak with a customer to ask, “Is our contact information for you still correct?” That change, which may require a culture shift that starts with the executive team, will dramatically improve program results.

Targeting and Personalization

Do you use Marketing Strategy and leverage data to drive campaign results? Test and Learn is a sneaky term for Marketing Strategy. It sounds less expensive, so use it.

Your content should be specific to your target’s business problem. It should be tailored to their purchase history, or lack thereof, and include next (or first) best product or cross-sell specific messaging. You should personalize the email to the targets name, title, company name, and other fields in your CRM system. If you have analyzed the lift generated by personalized vs. non-personalized messaging, you understand the need to target your emails. 

When you segment targets and personalize the subject line and body content of your emails, your open and click-through rates will increase significantly. Also, your opt-out rates will go down. The quality of these key fields must be monitored and improved to take advantage of that lift.

Compliance and Customer Interaction

If you are not familiar with CAN-SPAM, read my take on your responsibilities as an email marketer. You are required by law to do a great job at managing email opt-outs and confirming that your business is transparently represented in your email marketing. Even forgetting about email compliance, you should still consider the customer impact of bad marketing. “Mark Goloboy or Current Resident” = Trash Can. Trash Can = Poor return on direct mail spend. Read: Not enough revenue to justify your program.

So how do you mature your processes quickly to take advantage of the potential sales impact of Marketing Strategy? You must develop quality processes that remove bounced emails from your lists and opt-out all instances of an email address when requested. You also need to be sure your sales reps are honoring your opt-outs. Lastly, you must review your data for patterns of Sales or Service rep entered garbage. What’s the most frequently occurring word in the First Name field of your contact records? I bet it’s not “John.”

Data Acquisition

Whether you are in B2C or B2B Marketing, there is value in purchasing reference data for your existing customers, and lead data for prospecting. How much value is there? You need to test purchases from different vendors to determine that. There’s no other way. Buy a small list. Or better, ask a list vendor for a free list to test. They aren’t selling a lot now. Take advantage! Then predict ROI by scaling the results and associated spend across your whole universe of customer and prospect data.

You can also measure the impact of cleaning, de-duplicating and referencing your data to a standard set by testing those processes against a statistically significant sample of your customer data. Again, you may be able to get some of these services for free by getting into a competitive sales situation. Your prospects ask you for this, right? Mastering this customer data requires assistance and cooperation from Sales, Marketing, Customer Service and Technology, which means Data Governance.

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!

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.

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.