Data Before AI: Building a Clean Foundation for Smarter RevOps

Data Before AI: Building a Clean Foundation for Smarter RevOps

A conversation with Olga Traskova, VP of Revenue Operations at Birdeye.

The AI boom has hit B2B go-to-market teams hard. Everyone wants to automate, optimize, and accelerate—but few are pausing to address the silent killer of ROI: bad data.

In this episode of The Revenue Lounge, we sit down with Olga Traskova, VP of Revenue Operations at Birdeye, to unpack the unglamorous yet crucial reality behind AI success: data quality and readiness. With over 15 years in revenue and marketing operations, Olga shares battle-tested insights into building a strong data foundation, choosing the right AI use cases, and avoiding common traps in AI adoption.

“We’re trying to drive a Rolls-Royce without a license—and no garage to park it in.”
Olga Traskova, VP Revenue Operations, Birdeye

Olga’s Journey — From Marketing Ops to RevOps Leadership

Olga began her career in marketing operations, eventually expanding into RevOps as she tackled more cross-functional business challenges. Her growth wasn’t just vertical but horizontal—across marketing, sales, and customer success.

Now, as the VP of RevOps at Birdeye, she leads a global operations team serving the US, UK, and Australia. Her mission? Drive revenue efficiency by integrating people, processes, and tools across the customer lifecycle. Her approach is anti-silo: while team members have domain specialties, everyone is cross-trained. This enables agility and ensures seamless support for any GTM leader who engages with RevOps.

Why AI Alone is Not the Answer

AI is not a magic wand. If your systems are riddled with bad data, no amount of intelligence—artificial or otherwise—can generate reliable insights. Olga shares a relatable frustration: attempting to implement a call transcription tool that auto-fills Salesforce fields to save sales reps time. But when the tool failed to sync correctly with CRM, the data became misaligned, rendering the AI-generated insights useless.

“You’re just placing a shiny object on top of garbage. Fix your foundational processes first.”

This experience underscores a key RevOps truth: clean data is not optional. It’s a prerequisite.

Three Common Mistakes in AI Adoption

Olga highlights several pitfalls that GTM teams must avoid:

  1. No Data Foundation: Jumping into AI without structured, accurate data is like building a house on sand.

  2. Undefined Use Case: Many teams chase tools without clearly identifying the problem they’re solving.

  3. Tool Fatigue: Over-purchasing tools creates more chaos. Teams must prioritize longevity and ease of use.

MistakeDescriptionSolution
Data ChaosCRM is inconsistent, fragmented, or siloed.Conduct a full data hygiene audit.
Vague GoalsBuying AI tools without clarity on business outcomes.Define KPIs and workflows before evaluation.
Short-Term ThinkingChoosing flashy new tools that lack long-term viability.Vet vendors for stability and integration.

A Framework for Evaluating AI in RevOps

Olga’s decision-making process is grounded in use-case prioritization and long-term alignment. Here’s how she approaches evaluating new AI technologies:

  • Start with a problem: Identify the business gap (e.g., inaccurate forecasts, time-consuming manual tasks).

  • Map to outcomes: Connect the tool to a measurable objective (e.g., improving forecast accuracy within 5%).

  • Assess adoption impact: Will your reps need major retraining? Is it intuitive?

  • Avoid vendor churn: Choose tools with longevity. Avoid investing in platforms likely to fold or get acquired.

“Sometimes you don’t need a new tool. You just need to explore what your current stack can already do.”

The Anatomy of a Data-Ready Organization

Before AI can thrive, core data elements must be defined, standardized, and consistently used. Olga recommends:

  • Clear object mapping: Standardize definitions for leads, contacts, accounts, and opportunities.

  • Journey alignment: Define lifecycle stages and statuses across marketing, sales, and CS.

  • Field governance: Ensure input fields (titles, stages, reasons) are consistent and readable.

  • CRM integration: All third-party tools (e.g., Gong, Outreach) must sync cleanly into your CRM.

Your data model should be simple enough to translate into a plain-language sentence. If AI can’t “read” it clearly, it can’t act effectively.

Solving the Silo Problem

Despite increasing tech stack integration, most data remains siloed. Tools like sales engagement platforms often retain data internally instead of pushing it into CRM. Olga explains that today, CRM remains the system of record for her team—especially to support forecasting and enforce sales methodologies like MEDDIC.

She envisions a near-future where language-model-powered agents aggregate insights across tools seamlessly, eliminating the need for a singular data warehouse. But until that vision is reality, enforcing consistency in CRM remains critical.

Measuring AI Success: Beyond Buzzwords

One of the most critical, yet often ignored, aspects of AI adoption is ROI measurement. Olga encourages RevOps leaders to think deeply about what success looks like:

  • Is your AI helping reduce CAC?

  • Are sales cycles shortening?

  • Is forecasting accuracy improving?

In her experience, AI tools can identify improvement areas (e.g., objection handling, qualification gaps), but execution still depends on humans. That’s where enablement, coaching, and process management come in.

“AI can’t execute. You still need to ensure things get done.”

Who Owns AI in GTM? A Cross-Functional Responsibility

At Birdeye, AI ownership sits across the go-to-market leadership—sales, marketing, CS, solutions engineering, and RevOps. While RevOps leads vendor evaluation and implementation, all stakeholders collaborate on identifying use cases and defining requirements.

Security and compliance teams are also critical players, especially as legal concerns about data privacy, training models, and proprietary information increase.

“To stay ahead, every GTM leader must go back to the ‘how’ and the ‘what’ of AI. You can’t afford to ignore it.”

Olga’s AI Stack: Tools That Work Today

While enterprise-wide rollouts are still evolving, Olga and her team leverage several tools to boost productivity:

  • ChatGPT & Claude: For research, data analysis, and ideation

  • Gamma: To turn findings into visual presentations quickly

  • Zoom AI Companion: For call summaries and next steps

  • LinkedIn Sales Nav AI: For prospect intelligence

She emphasizes that many AI capabilities already exist within current tools. Leaders should prioritize exploring these features before investing in something new.

Final Thoughts: The Human-AI Partnership

Olga doesn’t believe AI is here to replace people—not yet. Instead, she sees it as a partner that enhances what humans do best. The goal? Free up high-value team members from repetitive tasks so they can focus on strategic impact.

“Treat AI like a copilot. It’s not driving the car, but it can navigate better than ever before.”


RevOps AI Readiness Checklist

StepDescription
Define Use CaseStart with a business problem, not a tool.
Audit DataClean your CRM and align all objects.
Evaluate Existing ToolsLook at what Zoom, Salesforce, LinkedIn already offer.
Select Partners CarefullyVet for long-term integration, not short-term trends.
Train & Enable TeamsEnsure adoption is seamless.
Track ROILink AI usage to outcomes like forecasting, CAC, rep productivity.

Conclusion: Data First, AI Second

AI is a powerful tool—but only when used responsibly. Olga’s insights serve as a reminder that clean, connected data is the true foundation of any successful AI initiative in RevOps.

If you’re thinking of investing in AI, don’t start with the software. Start with your data.

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