In a recent RevOps Co-op webinar, Rubrik's Zach Hoogerland and Openprise's Chaz La Salle sat down with host Matt Volm to answer a question every ops team eventually hits: once you've invested in data enrichment, how do you get that data right at scale? The short answer is that enrichment and orchestration are two different jobs. Clay acquires the data. Openprise governs and activates it. This post recaps the practical lessons Zach shared from running this at Rubrik, plus the framing Chaz uses across Openprise's customer base.
The speakers were Zach Hoogerland, senior director of marketing strategy and operations at Rubrik, and Chaz La Salle, a customer success manager at Openprise, with Matt Volm, CEO of RevOps Co-op, moderating.
Enrichment is a pipeline investment, not a data hygiene project
The first reframe came early. When you're talking about enrichment, you're talking about data governance and a pipeline investment, not a one-time IT motion to clean something up. Chaz called the hidden cost of getting this wrong "invisible rot."
When sellers have to source their own data, QA it, or check LinkedIn profiles to validate the right contact, all of that takes away from the time they could spend making an actual impact. Zach added the macro version of the problem: across hundreds of sellers, that wasted time means you need more sellers and more money just to get to the same revenue.
For ops, the before-state was familiar. It was firefighting, constant reactionary mode, trying to defend the fort without the headcount to do it. Zach described the shift his team had to make as going "from firefighting to carpentry," building foundation layers instead of reacting to everything.
Build the business case with simple math
A recurring theme: don't overcomplicate the ROI story. For Zach, the business case was simple math, how much time reps spend getting data, multiplied by headcount, and the opportunity cost of that time.
He also rejected the usual top-down versus bottom-up debate. You need strong leadership alignment and champions at the ground level who have tested the system and can sell it to other reps, because sellers won't buy in until they see the value themselves.
His test for any business case: if you can't explain it in five minutes, it won't connect at scale.
How Rubrik chose its enrichment vendors
Rubrik's legacy process was just ZoomInfo and Sales Nav, with every rep self-sourcing their own data. When Zach evaluated data enrichment vendors, he ran a pilot and made them prove the data.
He set a simple goal: good mobile and email coverage on inbound contacts, then tested every vendor against it. If a vendor couldn't prove the data, they were out of the conversation. Rubrik tested across its three theaters (AMER, ANZ/APJ, and EMEA) with the SDR teams in each region to verify coverage was actually high quality, not just claimed.
The discipline here is worth stealing: start narrow. The priority was high-quality data and speed to lead, which came down to emails and mobile phones first, before expanding to anything else.
The three enrichment waterfalls
Rubrik designed enrichment around three distinct waterfalls:
- Inbound waterfall, the entry point and "bread and butter of marketing ops"
- Outbound waterfall, what Matt called warm outbound, to give reps high-fidelity data from a clean slate
- Re-enrichment with a feedback loop, the phase Rubrik is building now
The third one is where Zach broke from convention. Most ops teams enrich a record and consider the job done, but he doesn't subscribe to that, because data decays and someone needs to build a feedback loop back into the enrichment process. The plan: when a seller flags bad data on the right tier of account, Rubrik commits to getting them new data within 24 hours, with timestamps marking which vendor enriched each record and when.
Why enrichment and orchestration are better together
This is where the peanut butter and chocolate analogy earned its place. Clay is excellent at acquisition. But enriched data on its own isn't actionable.
Chaz described Openprise as a hub-and-spoke architecture. It has over 400 integrations and moves go-to-market data through three stages: acquire, govern, and activate.
The governance layer is the part that makes enriched data usable.
Clay doesn't know your territories, product lines, or existing CRM records to dedupe against. Openprise sits between acquisition and activation, normalizing data, matching leads to accounts, deduplicating, and enforcing business logic so the record is clean, governed, and routed correctly by the time it hits a rep's screen.
Chaz's framing: if Clay gets the data in, Openprise makes it usable, because without context a phone number is just a number and an ARR figure is just a number.
Notably, Rubrik built their data orchestration motion first. Because the business is complex, with multiple product lines and selling teams, Rubrik had already solved the orchestration problem (mapping leads by persona, getting them to the right people) before turning their attention to enrichment velocity.
What Rubrik's inbound motion looks like now
The current inbound flow at Rubrik runs in three steps.
- A lead comes in and gets enriched immediately through the multi-vendor waterfall
- That lead then runs through Openprise to get mapped to a product persona, then routes to the right SDR team.
- Rubrik then layers in AI. Because they know the job title, email, and likely product interest, they can generate a four-email sequence that flows into Salesforce and then into Outreach.
One guardrail: they don't enrich every lead, only records that score up and will go to an SDR, otherwise they're burning money.
AI readiness: fix the data first
Both speakers landed on the same point about the "just use AI" pressure ops teams are getting from leadership. Zach's take is that when people say "just use AI," what they're really asking is "what are you doing?" and the answer is a use-case-driven roadmap, not AI for its own sake. His example of an unglamorous but valuable win: using AI to translate or standardize job titles.
Chaz tied it back to Openprise's core message. Garbage in, garbage out: AI amplifies whatever data quality you already have, so throwing more AI at bad data just makes you crash quicker.
That's why Openprise leans on an 80/20 approach. We handle the first 80% deterministically with the data on hand, then use AI to close the remaining gap, especially as AI gets more expensive and hallucinations need a human in the loop.
What this means for your team
The throughline of the session is that systems beat tools.
Enrichment gets you clean data; orchestration makes that data work for your business. Neither one alone gets you to a trustworthy pipeline or safe AI. If reps still don't trust the records hitting their screens, no amount of enrichment or AI fixes that. The architecture does.
Watch the full session for Zach's and Chaz's complete walkthrough, and if you want to see how the orchestration layer with Openprise works, request a demo.
