David Patterson-Cole, Co-Founder of DataLane, announces the launch of DataLane Foundations, the data foundation layer for RevOps and go-to-market organizations selling to contractors, healthcare clinics, restaurants, and the local economy.
About the Speaker
David Patterson-Cole is the Co-Founder of DataLane, the go-to-market data layer for companies selling to the local economy. DataLane works with large enterprises and fast-growing vertical tech companies to accelerate revenue through account scoring, campaigns, rep efficiency, and data foundation initiatives.
Summary
1. DataLane Foundations is live. A purpose-built data foundation for RevOps and GTM teams selling to contractors, healthcare clinics, restaurants, and any local business.
2. Data foundations are the #1 topic with large customers. Across DataLane’s largest accounts, data foundations is where they spend by far the most time and it’s only getting more important as AI agents succeed or fail based on the context they have access to.
3. The local economy makes this uniquely hard. Millions of accounts, complicated hierarchies, dozens of data sets that need stitching together, all without unique identifiers to match on.
4. Getting it wrong has real consequences. One large public company missed their pipe gen number for the quarter because their cleanup logic accidentally removed all net-new businesses from the data set. Another had to redo territories mid-year after inflating them by 30% due to missed duplicates across three data sources.
5. Even the best teams struggle. The strongest tech companies run multi-year projects with RevOps, data, and software engineering teams and still hit these same issues.
6. Foundations solves three core problems. Entity resolution algorithms (tuned for precision and recall to unify data sets into a single source of truth), non-destructive CRM enrichment (with deep configurability for field mappings and business logic), and a step-by-step rollout approach with diagnostics, alerting, and monitoring.
7. DataLane forward-deploys with partners. Rather than handing off software, the team embeds with customers to run diagnostics, set up monitoring, and share edge-case learnings from building data foundations with many of the largest companies in the space.
Biggest Takeaways
Data foundations are the most important and most underestimated layer of go-to-market
For every large customer DataLane works with, data foundations is the topic that consumes the most time and attention. This isn’t surprising once you understand the scope: companies selling to local businesses are dealing with millions of accounts, fragmented hierarchies, and dozens of data sources that need to be unified without any clean unique identifiers to match on. There are no reliable domains, no standardized business emails, no LinkedIn profiles to anchor against. And with AI agents becoming central to GTM motions, the quality of the underlying data foundation determines whether those agents deliver value or generate noise.
The cost of getting it wrong is measured in quarters, not days
David shared two examples that illustrate the stakes:
· A large public company missed their pipe gen number for the quarter because the cleanup logic they applied to their unified data set inadvertently removed all net-new businesses — one of their most important segments. The data was technically “cleaner,” but the business-critical accounts were gone.
· Another company had to redo all their territories mid-year because they built territories off three separate data sets. When they unified those sources, they missed a significant number of duplicates, inflating their territories by 30%.
These aren’t edge cases. They’re the natural result of trying to solve entity resolution and data unification at scale without purpose-built infrastructure.
Even the best teams run into these problems
It’s tempting to think this is a problem only under-resourced teams face. It’s not. Even the strongest tech companies with dedicated RevOps, data engineering, and software engineering teams run multi-year projects to get their data foundations right, and still encounter the same failure modes. The complexity isn’t about talent or investment. It’s structural: the local economy’s data landscape is fundamentally harder to unify than enterprise B2B.
What DataLane Foundations actually does
Foundations addresses the problem across three layers:
- Entity Resolution: Algorithms carefully tuned for precision and recall that allow companies to unify all their data sets into a single source of truth. This is the hardest technical problem in local business data — identifying who is who when businesses share domains, use personal emails, and sit inside complex franchise or holding group structures.
- CRM Enrichment: Built on top of the entity resolution layer, so enrichment syncs are non-destructive. This means you can push data into your CRM without overwriting existing records or creating duplicates. It also supports deep configurability for field mappings and business logic — critical for companies with established CRM architectures.
- Step-by-Step Rollout: Rather than shipping software and walking away, DataLane forward-deploys with partners. The team runs diagnostics, sets up alerting and monitoring, and brings the accumulated knowledge from building data foundations with many of the largest companies in the space. This hands-on approach is designed to catch the edge cases that typically surface weeks or months after a data project launches.
AI agents make data foundations more urgent, not less
One of the most important points in the announcement: as GTM teams increasingly rely on AI agents for prospecting, research, and outreach, those agents are only as good as the data they have access to. A perfectly built AI workflow will still fail if it’s operating on duplicated accounts, missing segments, or inflated territories. Data foundations aren’t just a RevOps project anymore — they’re the prerequisite for AI-powered go-to-market.
Watch the Full Announcement
Full Transcript
David Patterson-Cole (DataLane): Today, we’re launching DataLane Foundations for RevOps and go-to-market orgs selling to contractors, healthcare clinics, restaurants, or really any local business. For all of our largest customers, the topic we spend by far the most time on is data foundations, and it’s only becoming more important with AI agents that largely succeed or fail based on the context that they have access to.
The challenge is getting this right for small businesses is incredibly difficult. You have millions of accounts, complicated hierarchies, dozens of data sets that need to get stitched together without any unique identifiers to do so. And the consequences of getting it wrong are very real. We’ve talked to multiple large public companies recently, one of which missed their pipe gen number for the quarter because the cleanup logic they applied to their unified data set ended up removing all the net new businesses in the data set, which was one of their most important segments.
Another company had to redo all their territories mid-year because they built it off of three distinctive data sets and when they unified that, they missed a lot of duplicates and ended up inflating their territories by thirty percent. There are so many examples like this because getting operational projects right at scale is incredibly difficult.
Even the best tech companies we work with end up running multi-year projects with teams of RevOps, data, software engineers that still run into the issues I just mentioned. So this is why we built DataLane Foundations to handle all of this. The entity resolution algorithms that we’ve carefully tuned the precision and recall to allow you to unify all your data sets into a single source of truth.
A CRM enrichment that’s built on top of those entity resolution algorithms, so you have no destructive syncs, and that also supports a pretty incredible amount of configurability for field mappings and business logic. And most importantly, a step-by-step approach to rollout where we forward deploy with our partners, run the right diagnostics, set up the alerting and monitoring, and ultimately just share everything we’ve learned about edge cases from building these data foundations with many of the largest companies in the space.
For the next two weeks, I’m gonna be personally running one-on-one calls where we can discuss the pros and cons of your current architecture, run some of our lighter weight diagnostics, and answer any questions that you have. So if you’re interested in that, you can book at the link down below, and if you just wanna learn more, you can check out datalane.com/foundations.
