If I had to summarize all the advice from the RevOps experts we’ve interviewed for the Data Superheroes project over the past nine months, it would be this: You are not alone.
That arcane data transformation that’s been driving you nuts? Others face it too. Fixing it may be as simple as finding your RevOps community and, as Rosalyn Santa Elena of Clari put it, indulging in a little group “ops” therapy.
The other piece of revops advice that shines through is the critical importance of long-term thinking, and encouraging more of it at your company. Nearly everyone interviewed shared some version of, “I wish we had done X sooner because it would have averted a data catastrophe.” Often, by the time the entire company realizes they need higher data quality, it is too late.
Today, we share a recap of our favorite RevOps advice. If any intrigue you, there’s a link to the full interview below each snippet.
If you want good reporting, align on desired outcomes first
From our conversation with Amy Palmer, Head of Revenue at Autodesk.
In 2018, Amy’s previous company PlanGrid was acquired by Autodesk and she was faced with the prospect of integrating three Marketo instances, plus a fourth system—Autodesk’s existing customer data system. The team prized getting the integration done quickly on the assumption that other functions like reporting would be worked out later. Looking back, Amy isn’t so certain that was the right choice.
“We thought a lot about the process of merging our data, since it’s such a huge undertaking, and a little less on what our ideal data end state was,” says Amy. “In some ways it’s inevitable, because there’s just so much work to get it done—we were migrating Marketo programs, nurture campaigns, customer data. We were also redefining people’s roles, training new teams, and trying to avoid slowing down our growth engine during the whole thing. It’s just immense.”
Yet in hindsight it’s clear they could have aimed higher and built more of their wishlist into the primary scope. Not doing so only caused things to take longer.
“To be candid, it’s taken longer than we expected to build back to the level of insight that teams had prior to our merger … the project should have been: How do we blend processes to drive the outcomes we need?,” says Amy. “It seems subtle, but I think framing it this way from the start would have been a big time saver.”
For better data adoption, publish a data dictionary with engagement rules
From our conversation with Toby Carrington, VP of Revenue Operations at Seismic.
Toby Carrington of Seismic believes that if you want everyone speaking the same language, you have to write it down somewhere everyone can see.
“A data dictionary is an invaluable asset for making sure people have a common understanding, even of simple things like pricing naming conventions, capitalization of records,” says Toby. “It might seem basic, but they definitely can take on a life of their own if they’re not put in a common language. The challenge is to find a way to do this without losing important nuance by standardizing on the lowest common denominator.”
For this, a dictionary helps. It provides, if not perfection, at least consistency. Similarly, it’s worth writing a manifesto for why integrations should exist, or a decision tree for when to pursue them.
“The most challenging part is differentiating between which integrations are adding value and which are just integration for integration’s sake,” he says. “It’s tough because various system owners have strong opinions when they’re used to using the system in a particular way. As a leader, it’s my job to get everyone to snap out of their personal preferences and reorient around what’s best for the collective good of the company.”
To overcome the problem, first define it
Sometimes you get to build a new instance from scratch. But more often, you inherit a system that’s such a mess, you don’t know where to begin. In those cases, it’s helpful to revert to pen and paper.
“When your data is a mess, start with a definition of one thing you want to fix. For example, you might want to find all accounts that don’t have a website. First, fix that problem. Then identify all the places where accounts are created without websites in the first place and plug that hole,” says Greg. “That way, you’re cleaning, but also improving processes in a lasting way.”
Speaking of lasting fixes, you can always do better planning and solve future problems if you’ve taken the time to define precisely where you want to go. As Greg put it:
“At first, imagine you’re the detective asking, ‘What kind of problem do I have? How much effort will it take to fix?’ Then, you need to change your mindset into a consultant and think, ‘How can I find all the places where that information flows in in the organization, and plug future holes before they start?’”
When you think about it that way, small fixes accumulate into big gains. You retire more and more data debt until the quality gets better with time. And after a while, like Rosalyn, you’re the one offering ops therapy to others.
When all else fails, engage in “ops therapy”—whether by meme or virtual meetup
From our conversation with Rosalyn Santa Elena, Head of Revenue Operations at Clari and a recently anointed advisor to Syncari.
Connecting with your peers for some good old venting may be crucial to keeping your edge—and your sanity. Rosalyn Santa Elena of Clari calls this ‘ops therapy.’
“There are always some common challenges for everyone who is running an operations function, regardless of whether they have a couple of years experience, or decades,” says Rosalyn. “It’s refreshing to see that you’re not the only one going through this.” And, even better, someone in the group often knows a very specific fix.
In these sessions, three topics tend to arise:
- Organizational alignment. Between marketing, sales, and customer success, how do you keep everybody marching to the same drum?
- Proving ROI of the function. Operators know their work is invaluable for revenue efficiency—how do you get others to see it clearly?
- Data accuracy. How can we make sure we’re not building beautiful processes that just move around inaccurate data?
Answers to all of them are being hashed out on RevenueCollective, where you can find Rosalyn, and maybe you’ll find a little ops therapy?