RevOps Strategy

Hey Revenue Leaders, Stop Using Band-Aid Solutions For Your Data Quality Problems

I was on a Zoom call recently discussing data integrity issues with a revenue operations leader when I peered out the window and noticed a car sputtering by. It was held together with a generous supply of duct tape, its bumper threatening to dislodge on the road at any moment. Suddenly it hit me that this car was a lot like the tech stack this leader was describing—mismatched parts all jumbled together, barely holding at the seams.

When data integrity problems arise in the pressure-cooker environment of hypergrowth startups, it’s tempting to reach for the quickest fix possible. Janky Zapier integrations. Sporadic agency cleanup projects. Failed deduplication tools. Blanket reliance on API services. But the convenience of a quick fix comes at a cost.


Trust me, your future self will not be happy. (Image source)

The hidden cost of quick fixes

Here’s the thing. Data problems are probably already costing you money without you realizing it. The average data worker fritters away 80-90% of their time managing and preparing data—leaving only 10-20% of their time to spend actually performing analytics, reports IDC. And the cost goes even deeper than lost productivity: Bad data can cost businesses 15-25% of their total revenue each year, according to MIT.

But even when competing pressures draw your focus elsewhere, it’s important for forward-thinking revenue leaders and operations professionals to invest in solutions that meaningfully address the root causes of data problems. Otherwise, you’re submitting to a never-ending (and expensive!) game of whack-a-mole.

In this blog, I’ll share three ways you can work towards sturdier solutions to protect your data quality in the long term.

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1. Zero in on the sources of bad data to proactively prevent pollution

Spot-cleaning bad data costs marketers an average of 800 hours per year, and salespeople 900 hours per year. Instead of cleaning dirty data downstream, why not stop the pollution at the source?

“It’s easier to govern the front door than it is to govern the whole building,” says Maneeza Aminy, CEO of Marvel Marketers.

Maneeza recommends setting up normalization campaigns in Marketo to make sure every piece of customer data that enters your ecosystem is clean and correct.

Another way to assure quality at data entry points is to motivate your sales reps to keep meticulous records in your CRM. And that’s no small feat, especially when you consider that 84% of salespeople score highly on “achievement orientation,” according to the Harvard Business Review, meaning they’re primarily motivated by “winning big.” Fussing over subtle details usually doesn’t provide thrill-seeking reps the same blast of endorphins…unless you cleverly engineer it to.

How is this possible? Lorena Morales, VP of Marketing at Go Nimbly, uses a tool called Troops, which rewards reps for correctly entering customer data with a celebratory automated message in Slack when they close a deal. The team jumps in to comment and react with emojis to congratulate the person, reinforcing the behavior and helping build good habits over time. Redesigning your processes to keep bad data out on an ongoing basis means you won’t have to scramble for quick fixes when you need to pull accurate reports at the end of the quarter.

2. Think company-wide, not just departmental

Another form of a band-aid solution that can sometimes go unnoticed is a departmental approach to data quality. Since a company’s data is all part of a single ecosystem, an on-the-fly solution in one department may be creating downstream problems for others.

“Build out an organizational data strategy instead of just a departmental data strategy,” advises Cristina Saunders, Co-Founder of CS2 Marketing. “If everyone’s coming to the boardroom with different data sets, it causes huge problems for the business.”

That’s why you must resist the temptation to only fix data problems that pop up in your corner of the business without considering how it fits into the bigger picture. When the marketing team builds their MQL target based on faulty sales forecasting, problems compound.

Creating a data dictionary helps foster cross-departmental alignment around high data quality.  Cataloging your data points and agreeing on common definitions keeps your revenue-oriented teams tightly coordinated. (Not sure where to start? Here’s a helpful guide.)

3. Invest in technology to automate data grunt work

Here are some hard truths: Data entry is tedious, and humans are fallible. And while solid processes can mitigate its effects, some degree of human error is inevitable. That’s why investing in the technology to automate data governance and maintenance is the ideal way to keep data quality high.

“People don’t do what you expect. People do what you inspect,” says Chandar Pattabhiram, CMO at Coupa. “You can’t expect them to enter the right data every single time unless you’re inspecting it, and this is impossible to do at scale. That’s why it’s critical to invest in technology that automates data hygiene and governance. You’ll free up your smartest people to focus on what matters.”

When bad data is being patched, revenue leaders don’t always feel the acute pain their team is experiencing. 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,” says Maneeza. But clever workarounds and recurring data cleanup projects are quick fixes that are part of a losing game.

Forbes recently predicted that data automation will be a defining trend in analytics over the next decade.

“With the magnitude and velocity of data, automation at every stage of data handling will be imperative,” writes Anil Somani.

Data automation platforms allow you to create a global data model so you can create rules and hierarchies that can enact automated maintenance on an ongoing basis.

simplified user interface of Syncari's Sync Studio

Syncari’s data automation platform

“The emerging category of data automation excites me because it lets you act as an air traffic controller. It gives you a bird’s-eye view of your data, and that control center that allows you to anticipate any potential conflicts from a mile away,” says Cristina. “Most revenue leaders don’t have this today and it’s holding them back.”

A final thought: How to use band-aids—if you must

The demands of a hypergrowth technology firm don’t always allow for careful thought and consideration. You may even be encouraged to “move fast and break things.” Looming deadlines, demanding executives, and shifting priorities mean speed can sometimes be prized over precision. Is a band-aid sometimes warranted? Yes, I’ll reluctantly admit. Just make sure it’s temporary.

“To be really candid, I think there’s a place for band-aid solutions,” says Amy Palmer, Head of Revenue Marketing at Autodesk Construction Solutions. “But where you really run into trouble is your band-aid becomes your permanent state and you never go back to figure out how to do it right.”

If you are going to spring for a quick fix, make sure your team is aware that it’s meant to be temporary and you’ve committed to a timeline for fixing it, suggests Lorena Morales, VP of Marketing at Go Nimbly.

“Just don’t fall into the trap of just using a band-aid and moving on,” warns Lorena. “Because trust me, it will come back to bite you.”

About the authorNick is a CEO, founder, and author with over 25 years of experience in tech who writes about data ecosystems, SaaS, and product development. He spent nearly seven years as EVP of Product at Marketo and is now CEO and Founder of Syncari.

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