Data Health & Governance

What it is

Data health work addresses the foundational problems that prevent organizations from using their data effectively: records that don't match across systems, critical fields that are empty or inconsistently formatted, no clear ownership of data quality, and no process for maintaining accuracy over time.

We work within your existing systems — Excel, Salesforce, Airtable, legacy CRMs, and custom databases — rather than recommending a new platform as the solution. Most data quality problems can be solved with better processes and targeted cleanup, not a migration that costs ten times more and takes a year.

Who it's for

• Organizations whose AI initiative failed or stalled because the underlying data wasn't ready
• Companies with data spread across multiple systems that don't talk to each other
• Nonprofits or SMBs with years of accumulated CRM debt that's never been cleaned up
• Teams spending hours each week manually reconciling data from different sources
• Organizations in regulated industries where data accuracy is a compliance requirement

What's included

• Data landscape mapping: inventory of all data sources, ownership, and data flows
• Data quality audit: completeness, accuracy, consistency, and duplication analysis
• Root-cause analysis for the most significant data quality problems
• Prioritized remediation plan with effort estimates
• Hands-on cleanup of highest-priority data issues
• Data governance framework: ownership, standards, and maintenance processes
• Documentation suitable for handoff to internal teams

What we work with

We're experienced with Excel and Google Sheets (including complex workbooks), Salesforce, HubSpot, Airtable, Notion databases, custom SQL databases, and legacy CRM systems. We'll tell you up front if something is outside our scope.

Example outcomes

A nonprofit with 10 years of donor data across three systems engaged us to consolidate and clean their records before a Salesforce migration. We identified 22% duplicate records, built a deduplication process, documented data ownership, and reduced the migration scope by 40% — saving the organization significant implementation cost and time.

Timeline

Typically 4–8 weeks, depending on the number of systems, data volume, and the severity of quality issues identified. The initial audit (weeks 1–2) determines scope for the remediation phase.