Stop saying “We have a data problem!” Why your RevOps team needs a data diagnosis

How many times have you heard (or silently screamed to yourself) “We have a data problem!”?

Too many to count? But according to Jared Barol, VP of GTM strategy at Copy.ai, most of the time, it’s not actually a data problem.

Instead, your organization might actually have a process problem.

I sat down with Jared for an episode of RevOps Live! to talk about the so-called “big, bad data monster” haunting SaaS companies and how his critical path framework helps diagnose and solve common RevOps challenges.

Let’s dig in.

Your data problem probably isn’t what you think it I

RevOps teams get so caught up in tactical details that they lose sight of the bigger picture. Different teams define the data problem in their own way—sales blames bad leads, marketing points to attribution gaps, and finance struggles with forecasting errors. The result? A patchwork of quick fixes that make things worse, not better.

Jared nailed it when he pointed out the real culprit: lack of horizontal alignment across teams. Because teams don’t agree on priorities, they end up applying tactical band-aids instead of strategic solutions.

Sound familiar?

The shift we need to make is from data problem to diagnosis problem.

Instead of immediately blaming the data, start by asking deeper questions:

• What are the symptoms? Be precise about what’s not working
• Who is impacted? Understand how different teams experience the issue
• What’s the real root cause? Determine whether it’s a process, technology, or people issue
• Which data actually matters? Stop getting lost in irrelevant noise

By asking these questions, we move past surface-level assumptions and uncover the real issues blocking RevOps success.

Dealing with your data problem: Jared’s critical path framework

One of the biggest challenges RevOps teams face is knowing where to start. When you’re looking at a mountain of disconnected issues, it’s easy to feel overwhelmed.

Jared’s solution?

A structured critical path framework that breaks down problems into four categories:

1. People – Performance gaps, misalignment, lack of enablement
2. Processes – Broken workflows, unclear ownership, inefficient handoffs
3. Systems/Technology – Poor integrations, clunky automation, outdated tools
4. Data – Inaccurate records, duplicates, missing fields

Jared argues that true data problems (the ones you can fix with a script, a tool, or a few hours of cleanup) are usually the easiest to solve.

The real headaches come from:

• Systems/Technology: Fixing integrations and automation requires big investments, training, and ongoing maintenance
• Processes: Large-scale process changes (like revamping lead routing) impact multiple teams and require buy-in at every level
• People: The most expensive and disruptive issues stem from team misalignment, performance gaps, and turnover

Countless RevOps teams waste time fixing so-called “data problems” when the real issue is misaligned processes or ineffective teams.

Before jumping to conclusions, ask: Is this really a data problem, or is it a symptom of a larger issue?

Diagnosing RevOps issues: what has to be true?

Ever heard the business school question, “What has to be true for X to happen?” Jared applies the same logic to RevOps challenges.

For example, if your lead routing is a mess, ask:

• What has to be true for this to be just a data problem?
• What has to be true for it to be a data and systems problem?

By systematically ruling out possibilities, you pinpoint the root cause instead of applying guesswork.

Start with the cheapest fix first

Once you’ve diagnosed the issue, it’s time to take action. But Jared’s advice is clear: start with the cheapest fix first.

It might feel counterintuitive—after all, complex problems demand complex solutions, right? Not necessarily. Fixing the low-hanging fruit can create immediate improvements while you tackle the bigger issues.

Take lead routing, for example. If conflicting rules and contact ownership errors are causing chaos, don’t try to overhaul the entire system overnight. Instead, start by fixing the contact ownership issue—a small but meaningful change that reduces friction while larger changes are in the works.

As Jared put it:

“If you don’t fix the cheap thing first, you’re just wasting time and making things worse.”

Lead vs. lagging indicators: how to align GTM teams

Data can unite teams—or drive them apart. The trick is knowing which data to focus on.

Jared breaks it down into leading and lagging indicators.

• Leading indicators – Show early signals of success (e.g., SDR activity, website engagement, webinar attendance)
• Lagging indicators – Measure long-term impact (e.g., revenue, conversion rates, pipeline growth)

How to use both effectively:

• Listen to your frontline teams – Sales reps and SDRs provide early signals of GTM strategy success. Pay attention!
• Blend quantitative and qualitative insights – Data is key, but so is sales and customer feedback
• Hold pipeline calls – Weekly pipeline reviews keep teams aligned and surface early warning signs

By focusing on the right data at the right time, RevOps teams can drive better alignment and efficiency across GTM functions.

Data: the cheapest problem with the biggest impact

Jared made a bold statement that seems shockingly counterintuitive at first:

“Data problems are actually the cheapest to fix.”

At first, that might sound crazy—data cleanup costs can be astronomical! But compared to fixing broken processes or replacing underperforming team members, data issues are a bargain.

Why does this matter? Because data problems have the biggest impact on everything else. If your data is unreliable, your systems, processes, and teams are operating on bad intel. That leads to bad decisions, missed revenue, and wasted resources.

Jared explained that larger companies like Broadcom understand this. When they acquire another company, their priority isn’t the people or processes—it’s the data. Everything else can be changed, but data is the core asset.

Key takeaways: how to fix data problems the right way

• Stop blaming data first – Look at the full picture before making assumptions
• Use a structured framework – Diagnose issues using Jared’s critical path framework
• Start with the cheapest fix – Prioritize quick wins that reduce friction and drive impact
• Balance leading and lagging indicators – Use both to measure and optimize performance
• Treat data as a strategic asset – High-quality data fuels better decision-making across RevOps

Watch the full episode of RevOps Live! For more in-depth best practices, download our ebook: The Authoritative Guide to RevOps Data Quality.