Stephanie Frignoca

Positioning

I work upstream of the dashboard that keeps breaking.

Most CRM data work is reactive. A report breaks, a vendor gets blamed, a batch of records gets cleaned once, and then the next report breaks for the same reason.

Underneath it is usually the same issue: no shared definition of what a usable record actually looks like.

I work in the upstream layer: the frameworks, scoring models, field tiers, gated readiness logic, vendor benchmarks, and governance systems that decide whether the data behind routing, segmentation, reporting, and AI workflows can actually be trusted.

Quality is what you measure repeatedly, not what you clean up once.

The lane

Not data cleanup. Operational definitions of ready.

The work is specific. It looks like:

• Defining what a complete, standardized, accurate record means for your business, at the field level, with tiers and weights that reflect what your GTM motion actually needs.
• Building scoring models that measure each dimension on its own and refuse to average a fatal weakness away.
• Benchmarking enrichment vendors against each other on real records, by field, so source-of-record decisions stop being assumption-based.
• Automating the parts of the data foundation that are mechanical (hierarchy population, matching, deduplication, routing) so the humans can focus on the parts that are not.
• Designing the governance and process so the definition holds after launch. Ownership, review cadence, monitoring. The part that makes it durable.
• Building controlled paths for the revenue motions standard automation cannot safely carry: CPQ price-book migrations, usage-based billing, and renewal ownership.

It is the work that has to exist before AI workflows on top of CRM data become anything more than expensive, very confident guesswork.

Background

Design school, then a decade in GTM systems.

Most recently, I was a Senior Data Quality Specialist at Cockroach Labs, where I led CRM data quality and AI-ready account governance work. The frameworks featured on this site are generalized from patterns I have built across SaaS GTM environments, with company-specific details removed.

Before that, I spent four-plus years in Sales Operations and GTM Applications at Lansweeper, operating from the Salesforce seat across the GTM stack: account restructuring, lead routing, deduplication and matching, enrichment governance, cleaner address collection, CPQ and renewal support, and the process work that makes CRM data usable in the first place. Earlier still, I worked in sales roles at Embarcadero Technologies, which is part of why I care so much about how GTM systems actually behave for the people using them.

I am a Salesforce Certified Administrator with a BFA in Communication Design from Texas State University, which is why this site reads more like a product than a resume.

How to work with me

Open to the right full-time role in GTM or product-side data quality. Selective consulting through Page Frignoca LLC.

The best fit is a company that treats data quality as infrastructure, not after-the-fact cleanup. My work sits at the intersection of GTM systems, product data, and AI readiness: defining the quality gates, source trust, confidence logic, and governance that determine whether data is reliable enough to move downstream into a report, workflow, customer-facing product, or AI agent.

I've built these frameworks in Salesforce-centered GTM environments, but the logic is platform-agnostic. Tiered fields, gated readiness, vendor-source trust, cross-field accuracy, and explainable scoring apply anywhere a record, signal, or data product needs to be trusted before a person or system acts on it.

I'm open to full-time roles across RevOps, GTM Systems, Data Quality, CRM Strategy, Data Governance, and product-side data quality. I also take selective consulting and contract work through Page Frignoca LLC, usually scoped around data-quality assessments, vendor and source benchmarks, quality-gate design, governance models, and monitoring layers that keep the system honest after handoff.

If your data is not ready for the AI workflows you are about to point at it, that is the work I do.