There are those who want good data quality and those who seriously need it to succeed.
Ask B2B marketers which camp they're in, and the majority will choose the solemn second. In fact, 75% say they wouldn't achieve their 2016 marketing goals without good data quality. Yet, the condition of B2B marketing data continues to be shamefully bad. Year after year, our research here at Dun & Bradstreet reveals the same disturbing trend: Marketers repeatedly identify data quality as a high priority, but the actual state of B2B customer records tells a different story.
Our data quality is deplorable, despite noble intentions. This much is clear.
According to our B2B Marketing Data Report 2016
- 87% of B2B records lack revenue information.
- 85% lack company size by employee number.
- 77% lack industry information.
These alarming stats apply to just one aspect of data quality (record completeness). If data is the foundation upon which we base our strategies, campaigns and narratives, these numbers point to a grim reality: Rather than fortifying our marketing foundation, we're allowing it to wobble.
Well, that's where things get a bit blurry.
I have my own theories. Still, I'm always curious to get an expert's take on puzzling phenomena, so I reached out to Dr. Thomas Redman, an advisor and innovator at Data Quality Solutions with nearly 30 years of experience in data quality. Affectionately known among his longtime fans as "the Data Doc," Redman counsels a variety of clientele, including Fortune 100 executives. Quite graciously, Redman carved out time to answer my questions about the mysterious "why's" underlying people's attitudes toward data quality.
Q. Why do some enterprise leaders attack data quality issues more ferociously than others? In particular, what's keeping marketers from rolling up their sleeves?
Redman: There are a bunch of reasons why people fail to engage in data quality programs. Every organization has different measures and ways of interacting. That said, I've encountered common themes of resistance across various lines of business, including marketing.
First, many people are remarkably forgiving of bad data. Sometimes this is hard to see. To explain what I mean, I like to tell a story about a young executive who is getting ready for her first board meeting. The executive's assistant calls to let her know that one of the numbers in her presentation "looks wrong." Unruffled, the executive tells him to "fix it" and email the final presentation file to her. He does exactly that, putting pencil to pad, roughly estimating what the number should be.
Well, the meeting turns out to be a great success. In fact, the linchpin of the discussion turns out to be the very number the assistant flagged and "fixed." The board is gratified by the valuable discussion; the executive is pleased to have made a good impression; her assistant is glowing with pride.
"Moving forward, let's check these numbers all the time," the executive tells her assistant as they pull on their coats.
I like to point out a few things about this scenario. When the assistant spots the funny-looking number, the executive has an opportunity to call and verify its accuracy, but she doesn't. Even after the meeting, she doesn't offer to lead a data quality improvement project or investigate the root cause of the odd number. Instead, she passes off the accountability to her assistant, leaving others to become victimized by bad data.
Q: Wow! "Victimized" is a strong word. Personally, I think it's a perfect descriptor, but I'm pretty sure most marketers don't think about the upshot of bad data in these terms.
Redman: Well, they should. It's like finding a wet spot on the floor and walking around it rather than stopping to wipe it up. When you find a problem, it's your responsibility to pursue a solution. Otherwise, others will lose their footing, and their resulting injuries will be your fault.
Q: Speaking as a marketer, I can relate to your story. There's a lot of pressure to get quick answers. But you make a great point. Drumming up quick data today shouldn't take precedence over addressing the damage poor data can do tomorrow.
Redman: Certainly, the incremental costs of bad data are hard to quantify. And I'm fairly sure no one overlooks faulty data to screw themselves up. The trouble is we've become damn inured to the problem.
Q: Tell me more about that. Why have we grown so complacent?
Redman: Ego is often part of it. A lot of executives think it's their job to make tough calls in the face of bad data. There's a strange sort of pride that feeds on the ability to make gut-level decisions based on decades of experience. Take away questionable data quality and these executives suddenly become more dispensable, at least in their own minds.
Q: You'd think even these executives would find it hard to argue with the ROI generated by decisions based on high-quality data.
Redman: You'd be surprised. In some organizations, I've seen a pervasive fear of improvement. One of my clients was experiencing a 65% error rate with its data. After investing 30 hours or so on root cause analysis and training employees, they reduced the error rate to 13%. I was pretty pleased, thinking they had created a model they could run with. But my client had a much different reaction.
Pulling me aside, she said, "Don't tell anyone about this."
I've only been caught speechless two times when I've been on the job. This was one of them. Apparently, my client believed that sharing their success would show that she and her team were doing a bad job before the changes. And, of course, the reduced error rate - while impressive - wasn't perfect. She wasn't willing to admit her organization was anything less than flawless.
Q: What about marketers who say they have a data quality initiative, but their database doesn't show any significant signs of improvement?
Redman: They're probably attacking data quality in the wrong way. Any organization that approaches data quality as a "clean up" mission is doomed to mediocrity. But many businesses don't know any better. They think bad data is a technology problem, not a management problem. With that kind of thinking, they'll never get in front of it. It's far better to find the root cause, and it's much easier than dealing with bad data day in and day out.
Q: What's one thing about data quality that marketers should be thinking about, but aren't?
Redman: The way our organizations are set up today is wrong for data. Companies are built around a division of labor, with an assembly line mentality. In the Industrial Age, this was remarkably effective. But now, these silos prevent data-sharing.
What marketers - and everyone else - need to understand is this: Everybody touches and makes data. We're all data creators and data customers.
If we don't recognize our roles as data customers, we're not likely to do anything to improve data quality. We're doomed to work with bad data. And the worst thing about this isn't a lack of progress per se, but the fact that we're passing our problems on to the next person in line. Perpetuating bad data hurts our companies, our co-workers and our relationships with customers.
Why would anyone want to have a personal hand in that?