Orchestrating Siloed Data in RevOps to Drive Business Decisions
A conversation with Mahesh Kumar, VP of RevOps at AppviewX.





“The goal of RevOps is to remove the art from revenue generation—and bring in science.”
— Mahesh Kumar, VP of Revenue Operations, AppViewX
Go-to-market teams are facing a monumental challenge—data fragmentation. With siloed systems, disconnected tools, and inconsistent definitions, organizations struggle to form a cohesive view of their revenue engine. The result? Poor decisions, misaligned teams, and missed growth targets.
In this in-depth conversation on The Revenue Lounge, Mahesh Kumar, VP of Revenue Operations at AppViewX, breaks down his playbook for navigating the messy world of siloed data. With more than 12 years of experience across sales, marketing, and operations, Mahesh offers real-world examples and strategies to help RevOps teams become not just operationally efficient—but strategically indispensable.
The Problem: Data Silos and Misalignment Across Functions
Mahesh began his career on the revenue side—as a pre-sales engineer, then moved to sales, built BDR/SDR teams, and later ran marketing. This 360-degree exposure gave him a unique lens into one of the most persistent challenges in GTM functions: siloed data.
“Every department had its own version of the truth. Even basic definitions varied. It was impossible to align or make strategic decisions.”
He recounted a particularly painful period where marketing believed it was generating high-quality leads, sales felt those leads were weak, and customer success struggled to understand what was promised to customers—because no one had a unified dataset or common definitions.
This wasn’t a minor inconvenience. It was a strategic blocker.
The Solution: Building a Unified, Orchestrated RevOps Engine
To solve the fragmentation problem, Mahesh emphasized that the answer wasn’t just in tools—but in orchestration.
“We can’t consolidate everything, and we shouldn’t try to. The key is orchestrating data across tools, teams, and processes.”
Rather than force-fit every team into a single platform, Mahesh advocates for connecting tools via native integrations where possible and using custom scripts or internal workflows when necessary.
At AppViewX, for example, Salesforce acts as the system of record, but data flows in from various tools—marketing automation, CS platforms, product usage systems, and internal scripts that clean and enrich records in real-time.
The Orchestration Mindset
Traditional Approach | Orchestration Mindset |
---|---|
Attempt to consolidate tools | Embrace point solutions but integrate them |
One-size-fits-all reporting | Custom dashboards by function |
Data owned by each team | Centralized data strategy |
Ad hoc fixes | Long-term scalable systems |
Step-by-Step: Mahesh’s RevOps Orchestration Playbook
Mahesh’s approach to breaking down data silos follows a deliberate, step-by-step method. Here’s how he tackled the challenge at AppViewX:
1. Secure Executive Buy-In Through Use Cases
The first step is not technical—it’s cultural. Mahesh identified a few high-impact use cases where disconnected data caused pain, then presented them to executives.
For example, onboarding delays were traced back to poor visibility into customer expectations during the sales cycle. By involving the CS team earlier in the sales process, the transition became seamless, resulting in faster time-to-value.
“Start where the pain is loudest. When executives see the impact, they’ll back your strategy.”
2. Establish a Single System of Record
One of the earliest wins came from establishing common data definitions across departments. Terms like “lead,” “MQL,” and “sales-qualified” had different meanings in different departments.
“Without standard definitions and a shared system of record, you’re not speaking the same language—even if you’re in the same building.”
Template: RevOps Data Dictionary
Term | Definition | Source of Truth | Owner |
---|---|---|---|
MQL | Lead with score > 70 and engaged in last 30 days | HubSpot | Marketing Ops |
Opp Stage 3 | Proposal shared and scheduled for review | Salesforce | Sales Ops |
Time to First Value | Days from deal close to initial onboarding value | Gainsight | CS Ops |
3. Focus on Categorizing and Structuring the Data
Once teams are aligned, the next challenge is data structuring. Mahesh’s team categorized data into four key buckets:
Human-generated data (manual entry in CRM)
System-to-human data (notifications, tasks, UI flows)
System-to-system data (API transfers, integrations)
External data (from customer intent tools, product signals)
Each dataset was cleaned, normalized, and mapped to the CRM structure, making analysis and automation easier.
“Every new field or process change is evaluated for its downstream data impact. It’s a data-first culture.”
4. Automate Integrations with Native Tools + Internal Scripts
While AppViewX doesn’t use a classic ETL tool, Mahesh’s team built internal automation workflows using scripts to orchestrate data across systems.
Whenever possible, they rely on native integrations—for example, syncing Salesforce with HubSpot, Gainsight, or internal product tools. But for more complex requirements, they’ve written scripts that move data based on business rules.
This flexibility ensures scalability without overengineering.
From Tactical to Strategic: The Future of RevOps
With orchestrated data in place, Mahesh believes RevOps can move beyond its reputation as a support function and become a strategic growth engine.
“When you’re sitting on high-quality, unified data, you can test hypotheses, optimize processes, and influence revenue strategy directly.”
Tactical vs. Strategic RevOps
Tactical RevOps | Strategic RevOps |
---|---|
Report on pipeline and leads | Advise GTM strategy using insights |
Fix sync issues in Salesforce | Optimize funnel stages to reduce CAC |
Build dashboards on request | Drive quarterly planning with data |
Reactive to requests | Proactive in identifying GTM risks |
The Cultural Shift: Building a Data-First Organization
One of Mahesh’s biggest insights wasn’t about tools or processes—it was about culture. Many teams look for a quick fix: “We have a problem—what tool can we buy to solve it?”
But Mahesh believes success starts with a mindset shift.
“Every change—whether it’s a new field, a process tweak, or a tech purchase—needs to be evaluated for its impact on data.”
This long-term thinking is essential, especially in high-growth environments where new tools and processes are being adopted rapidly.
Scaling for Tomorrow: How to Future-Proof Your RevOps Stack
A recurring challenge in RevOps is building for now vs. building for scale. Many teams implement quick fixes that don’t scale—only to rip and replace them six months later.
Mahesh recommends designing every system with scalability in mind.
“Whatever you implement—ask yourself, will this still hold true when we 3X in size? If not, it’s probably the wrong solution.”
He encourages teams to define a roadmap, even a high-level one, that plans for change, disruption, and organizational evolution.
Key Metrics: What Mahesh Tracks in a Modern RevOps Engine
Mahesh categorizes metrics across three layers—from strategic to tactical:
1. Strategic Business Metrics
Customer Acquisition Cost (CAC)
Customer Lifetime Value (CLTV)
Net Revenue Retention (NRR)
2. Funnel Efficiency Metrics
Sales cycle time
Lead-to-opportunity conversion rate
Average deal size, segmented by product, geography, or persona
3. Campaign & Channel Metrics
Campaign ROI
Attribution by source
Web engagement analytics (e.g., time on site, conversions)
By cascading insights from these metrics, RevOps can influence product strategy, sales enablement, marketing spend, and customer success operations.
Final Thoughts: Advice for Aspiring RevOps Leaders
Mahesh closes the conversation with timeless advice:
“Build to scale. Don’t go for short-term fixes. Think long-term. Invest in foundations before you build automation.”
He also believes RevOps is one of the most exciting careers today:
“You get to problem-solve, influence strategy, and see the impact of your decisions on the business. What more could you ask for?”
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