Clean inputs. Governed schemas. One source of truth. The plumbing every other ops investment depends on, built right the first time.
We design the schemas, fix the duplicates, govern the inputs, and instrument the dashboards. Every dollar spent on automation, scoring, or routing rests on whether the underlying data is clean, complete, and trusted. We make sure it is.
Every dashboard, every model, every campaign rests on the same foundation. If the data is broken, everything above it is theatre.
Every engagement starts with this rubric: completeness, accuracy, consistency, timeliness, uniqueness, validity. Toggle a starting profile to see what each looks like in the wild.
Records exist but you can't trust them. Reps work around the system. Reporting requires a human in the loop.
Five phases. Five working agreements. Each one ends with something live, measurable, and handed back to your team.
Map every system, every field, every flow. Find the drift, dedupes, orphans.
Schema design, entity ownership, naming conventions, data contracts.
Build dedupe, validation, enrichment, and consent pipelines. Govern at write.
Cutover live data to governed schemas. Hold-out validation.
SLAs for freshness. Quarterly reviews. The data layer becomes a product.
The left column is what we walk into on Data Management. The right is what we ship: same operating surface, cleaner ownership, and fewer manual workarounds.
Custom fields multiply. Naming conventions disagree. Two systems use "customer_id" to mean different things. Every integration becomes a 3-week negotiation.
Every entity, every field, owned and documented. Changes route through review. New systems plug in without surprise.
Same person, four times, four spellings.
Fuzzy matching, golden records, ongoing dedupe.
Job titles from 2019. Emails that bounce.
Records refreshed on a cadence, not on demand.
Consent flags that nobody can produce on demand.
Consent, retention, and processing logged from day one.
Marketing's pipeline number doesn't match sales's. Finance's revenue number doesn't match the CRM's. Every weekly meeting opens with thirty minutes reconciling figures nobody trusts.
Shared definitions, modeled metrics, governed dashboards. Marketing, sales, finance, and product all read the same number, and meetings start with decisions, not reconciliation.
Recognize three or more? Book the 10-minute intro call →
Four core capabilities, working as one system. Hover any column, meet the specialist who runs it.
Map every system, every field, every flow. Find the duplicates, the orphans, the silent drift. The audit produces a single picture of where data lives and where it is broken.
Schema design, owners assigned, naming conventions agreed. We define what every entity is and is not, then put guardrails up so it stays that way next quarter.
Dedupe, normalize, enrich, and validate on a cadence. Records get refreshed automatically, not when somebody finally complains. The database stops decaying.
Dashboards on top of modeled metrics. SLAs for data freshness. Quarterly reviews. The data layer becomes a product the company depends on, not a problem the team avoids.
Four phases · One foundation · zero drift
If you don't see yours here, the easiest way to get an answer is to ask. We don't gate calls.
Email us a question →Almost never. The work is on the schema, governance, and pipeline layers, your CRM keeps doing what it does, just on top of clean foundations.
We migrate it. Cleanup, dedupe, normalize, enrich, and validate against a hold-out sample before the cutover. Historical reporting stays continuous.
Your data team. We document, hand off, and train. If you don't have a data team, we stay on retainer as the operator until you do.
Consent flags, retention timers, processing logs, and deletion pipelines built in from day one, not patched later. Audit-ready by default.
Yes, a CDP is downstream of governance. We make sure the data flowing into it is trustworthy first; otherwise the CDP just industrializes the mess.
Most engagements pull in one or two adjacent services. Use these when the build needs cleaner data, sharper routing, or a broader operating model.
Frame the bet, platform mix, operating model, and roadmap before the build queue.
→Production agents with tools, evals, guardrails, observability, and human handoff.
→Capture, dedupe, score, route, and nurture without leaks between marketing and sales.
→Behavior-triggered programs that ship at scale across lifecycle, nurture, and expansion.
→Schemas, cleanup, enrichment, quality monitoring, and governance.
●Integrations and back-office workflows that remove manual handoffs.
→Every dashboard, every model, every campaign you'll ever run depends on the same data layer. Get it right once and stop paying interest on it every quarter.
