5 Ways Siloed Data is Burning Your Revenue
- RevOps
- 12 min
- Updated: July 20, 2026
There are plenty of visible reasons revenue underperforms. A slow quarter, a competitive loss, a stalled deal. Most of those show up somewhere in a QBR deck.
Siloed data rarely does, and it’s usually a bigger problem than any of them. In MuleSoft’s 2026 Connectivity Benchmark, surveying 1,050 IT leaders, 90% respondents said data silos are creating business challenges for their organization. This has risen to 94% among companies actively using AI agents.
Gartner has long pegged the average cost of poor data quality at $12.9 million a year per organization; more recent Gartner research adds a sharper, more current number on top of it: 60% of AI projects are expected to be abandoned through 2026 due to data that isn’t ready for AI to use.
Siloed data isn’t a new problem. What’s new is what it’s now blocking.
Get our latest insights into your inbox
What Is Siloed Data?
Siloed data is information from revenue-generating activity in sales, marketing, customer success departments that’s trapped in disconnected systems. These activities are visible to the team that owns it and effectively invisible to everyone else.
Marketing builds strategy on data sales never sees. Sales logs activity customer success has no visibility into. Each team optimizes its own numbers because that’s the only complete picture available to it.
This is exactly the gap revenue operations exists to close. But RevOps as a function can only align teams around data that’s actually complete and shared in the first place.
How Data Silos Form
Three forces reliably create them:
- Siloed incentives – When sales, marketing, and customer success are measured on separate goals, they optimize for those goals rather than the shared outcome. Misalignment between sales and marketing specifically has been estimated to cost businesses over $1 trillion annually, a figure that’s been widely cited since a 2021 HBR analysis and, if anything, undersells the problem now that buying committees and tech stacks have both grown since then.
- Cultural resistance – Legacy systems persist because switching feels riskier than staying, even when staying is quietly more expensive. Teams that don’t have a shared data culture struggle to turn the data they do have into anything actionable.
- Tech stack sprawl – MuleSoft’s 2026 research found the average organization now runs 957 applications, up from 897 the year before. And only 27% of them are actually integrated. Organizations already using AI agents run even more: 1,103 applications on average, 45% more than organizations without agents. More tools, adopted faster than they’re connected, is the direct mechanical cause of most data silos.
5 Ways Siloed Data Is Damaging Your Revenue
1. Missed business opportunities
When teams default to protecting their own data rather than sharing it, prospecting and pipeline nurturing both suffer. A lead handed from marketing to sales without the context behind it is a colder lead than the data actually supports. Internal competition for credit compounds the problem: teams optimize for defending their own numbers rather than looking for revenue opportunities that fall between them.
2. A worse customer experience
Disconnected touch points mean sales often can’t see where a prospect actually is in their journey, leading to repetitive conversations, generic follow-ups, and a buyer who has to re-explain their situation to every new person they talk to. It also distorts cost measurement: when a deal that closed on a call gets attributed to an email instead because the systems don’t talk to each other, marketing’s cost-per-acquisition numbers become unreliable. And decisions get made on top of that unreliable number.
3. Inaccurate revenue forecasts
Siloed data means no single leader has the complete picture, and different departments’ partial views rarely reconcile cleanly. The result is a forecast built by stitching together incomplete team-level reports rather than one grounded in what’s actually happening across the full customer journey. This is a large part of why forecast accuracy remains a persistent, well-documented problem across B2B sales organizations.
4. Lower productivity and weaker cross-functional trust
Sales and marketing misalignment isn’t just a data problem. It’s a trust problem that compounds over time. When teams can’t see each other’s data, blame-shifting becomes the default response to missed targets, and each function starts optimizing for its own win rather than the business’s. Employees also notice when leadership doesn’t seem to understand how data actually gets used day to day, which corrodes morale in a way that’s hard to trace back to a specific number, but real all the same.
5. Compliance and security exposure
Each isolated system typically runs its own security posture, multiplying the number of places a breach or leak can originate. Manually re-entering the same data across disconnected systems. A rep logging the same lead in a spreadsheet and a CRM, for instance, also introduces the kind of error that erodes trust in the numbers even before any compliance issue arises.
On the privacy side, the landscape has shifted since this post first published: Google reversed its plan to phase out third-party cookies in Chrome in 2024, moving to a user-choice model rather than a full deprecation. Safari and Firefox still block third-party cookies by default, so the underlying trend that first-party data is becoming the more durable, more compliant asset hasn’t changed. Organizations with siloed data are still worse-positioned for this shift than ones with a unified, first-party data strategy, regardless of exactly which browser does what on which timeline.
Data Silos in the Agentic AI Era
For most of the last decade, a data silo was primarily a coordination cost. Teams making worse decisions because they couldn’t see each other’s information. That’s still true. But MuleSoft’s 2026 Connectivity Benchmark surfaces a sharper problem: 50% of AI agents currently operate in isolated silos, disconnected from any cohesive multi-agent system, and 86% of IT leaders agree that without proper integration, AI agents introduce more complexity than value rather than less. Only 54% of organizations have a centralized governance framework for the agents they’ve already deployed.
That matters because a data silo that used to just produce a slower decision can now produce a wrong one, automatically, at scale. When an AI agent inside Salesforce, inside a forecasting tool, or inside a custom workflow acts on incomplete or siloed data, there’s no longer a human default sitting between the bad input and the resulting action.
Gartner projects that 60% of AI projects will be abandoned through 2026 specifically because the underlying data isn’t ready for AI to use, and separate 2026 research finds 70% of organizations admit their data isn’t clean or trustworthy enough for AI in the first place.
A silo used to cost you a missed opportunity. Now it can cost you a wrong decision made by software, at a speed no manager can catch in time.
How to Actually Solve the Data Silo Problem
- Invest in tools that integrate with what you already have, rather than adding another disconnected system to the 957-app average most organizations are already running. The goal isn’t fewer tools for their own sake. It’s fewer tools that don’t talk to each other.
- Build a single source of truth, not a single dashboard. A unified view only works if the underlying data feeding it is actually complete. A dashboard built on top of the same siloed inputs just makes the silo prettier, it doesn’t close it.
- Treat data quality as a shared responsibility, not an IT task. Regular, cross-functional data hygiene and not an annual cleanup project is what keeps a unified data layer from re-fragmenting six months after you fix it.
- Deploy AI deliberately, not reflexively. Given that half of enterprise AI agents currently run in isolation from each other, adding an agent on top of an already-siloed stack usually multiplies the problem rather than solving it. Integration has to come before automation, not after.
This is exactly where Nektar’s Data Foundation fits: it automatically captures email, meeting, call, and calendar activity across sales, marketing, and customer success, and writes it natively into Salesforce, the same structured record every team and every AI agent draws from, rather than each function maintaining its own partial view.
Daisy AI then turns that unified record into signals every function can act on — buying-group visibility for sales, marketing attribution tied to actual pipeline movement, and churn risk flags for customer success — from the same underlying data, rather than three separate, disconnected reports.
Unify Your Revenue Data Before You Automate On Top of It
Siloed data was always a drag on revenue. In an agentic AI environment, it’s also a governance risk. The same gap that used to produce a slow decision can now produce a wrong one at machine speed.
Talk to us to see how much of your own pipeline and customer data is currently siloed away from the teams that need it, or explore GTM Telemetry for how Nektar unifies it automatically.
Enjoyed our content? Follow Nektar on LinkedIn
