“It’s like a ferocious little monster that suddenly wakes up.” That’s how Maneeza Aminy, CEO of Marvel Marketers, describes the problem of bad data that attacks B2B organizations—seemingly out of nowhere—when they reach a certain level of maturity.
The more you grow your digital marketing footprint, she explains, the more entrenched—and costly—your data problems become. In the early days at most scrappy startups, nobody thinks twice about a band-aid solution here, a quick-fix Zapier integration there. But if you’re not careful, data problems will inevitably become a major blocker that inhibits your organization’s growth.
While most companies wait until the pain of bad data is unbearable to meaningfully address it, Maneeza and her team are on a mission to change this myopic approach to data management.
In this interview, Maneeza shares her cautionary tales and her best tips for triumphing over this “little monster”—including an innovative approach she invented called “data dry cleaning.”
Nick: What’s the advice you most frequently give clients about data quality?
Maneeza: It’s a lot easier to govern the front door than it is to govern the entire building. If you invest time in creating checkpoints for new data entering your system, you’ll be far better off than if you shrug it off and decide, “Oh, it’s fine. We’ll just clean up data once a quarter.” Don’t clean up the problem. Prevent the problem. Trust me, it will save you a ton of time, money, and effort. For example, I always recommend creating campaigns to normalize data before you’ll even let it enter your Marketo instance. That way, you won’t be forced to clean it up twice when the dirty data is passed into your CRM.
What’s your best story of data management gone wrong?
I’ll tell you a funny one. Six years ago, we were working with a client and every week we’d sit down to review marketing performance reports, but our client knew instinctively that the data we were looking at didn’t look right. When we sat down in the boardroom to conduct a thorough review, we all looked at the funnel metrics report. The data looked fairly consistent over time, but then suddenly there was a big spike of alerts where the dashboard was telling us the data was wrong. It literally looked like our data was giving us the middle finger.
The lesson learned here: trust your gut when it comes to data. If you sense it isn’t right, it’s worth investigating the root cause of the problem as soon as possible. Or else your data might just flip you off.
What framework can revenue operations professionals use to assess their data management maturity?
Revenue operations teams can be in one of three phases of maturity, and I always start with new clients by assessing their baseline to craft a stage-appropriate data strategy.
Phase 1: Data hygiene. Ask yourself: Where does bad data show up and how can you clean it up?
Phase 2: Data governance. Once data is clean, what rules do you need to implement to keep it clean?
Phase 3: Data enrichment. Now that bad data problems are minimal, what would the ideal data ecosystem look like?
For example, if your company is account-centric, ideal data may consist of building an aggregate score field, and enriching the top of the funnel with surge data from a vendor to help identify the hot leads to prioritize among your target account list.
But most companies are still stuck in the data hygiene arena. Achieving a state of clean data is a lot easier said than done!
For those still striving for data hygiene, what’s one baby step they can take to improve?
Even within the data hygiene phase, there are still varying degrees of sophistication, so you can always work towards the next level.
Back when I worked as a consultant, we created what we called a “data washing machine,” which consisted of setting up three to five campaigns to normalize all lead data that entered a client’s system. Simple things like normalizing location data when a lead would fill out a form—for example, making sure “CA,” “Cali,” and “California” were all recognized as the same state, so marketers could effectively geo-target emails. Another one would be blacklisting competitors so they get removed from your lead lifecycle at the “front door” of your marketing. It may sound basic, but you’d be surprised how often I’ve gotten emails from competitors trying to pitch their services to me because they’re not blocking me as a competitor at the top of the funnel.
Eventually, our most data-minded clients were ready for an increased level of sophistication, so we created what my team coined as “data dry cleaning,” which took the washing machine concept to a whole new level. We built about 15 sophisticated automated data cleaning campaigns to ensure an even higher level of data quality. This consisted of data cleanup techniques like field validation on forms, so when phone numbers come through, they’re accurate. Another one was blocking personal email addresses and mandating business email addresses to ensure only professional email addresses made it into clients’ CRMs.
Who should own data quality in an organization?
If I were to design a perfect organization where data quality could thrive, I would appoint a data governor. This role would own all things data across all systems and departments. And believe me, this is a full time job! In most companies, the person who relies on the data feels the pain of poor data quality the most. So they become the guardians of data quality in their organizations, whether it’s their job or not. Because if it’s wrong, their reports are wrong.
This can become problematic when they aren’t properly resourced to take on such a large project, or if it distracts them from nailing their primary job duties. Appointing a dedicated headcount to data quality alone frees up the rest of the organization to focus on what they do best.
What’s the biggest misconception revenue leaders believe when it comes to data?
Here’s a finer point I often see people miss: there’s a difference between a data point, a metric, a report, and insights. They are four distinct things, but people conflate them all the time.
People will say, “I’m in charge of data,” but they’re actually in charge of metrics. Or they’ll say, “I’m our insights person,” but all they’re doing is producing the reports. It’s someone else’s role to interpret and translate them, which is the real insights piece.
Organizations know they need insights, but insights are dependent on the reports, which are dependent on the metrics, which are dependent on clean data. And that becomes the forcing agent for data cleanup projects. Because the level of insight you can provide is a direct result of the health of underlying data.
Don’t overlook the importance of using a common vocabulary to describe data. Speaking the same language is a necessary precursor to alignment.
What’s stopping revenue leaders from prioritizing data quality?
- Leaders don’t feel the pain yet. But once they feel it, it’s already too late.
- Bad data is being patched. You’d be shocked how many companies have a brilliant person who’s taking bad data, then moving it, filtering it, and manipulating it in Excel. Then they provide their CMOs with the exact reports they’re looking for. But not only is this not scalable, but it is a single point of failure.
- Leaders feel the pain, but they don’t realize it’s coming from bad data. CMOs keep asking the same questions, and their teams are unable to get satisfying answers. Nobody realizes bad data is to blame.
These are the types of thinking that feed the little monster, making it stronger. Until one day, it wakes up and causes destruction. It’s your job as a leader not to feed the monster! It’s your job as a leader to identify and mitigate the risk before it becomes a pain point. Understanding the role of data quality is one of the main things revenue leaders must prioritize to grow.