The Data Quality Debt Problem
A business grows from 2 to 10 employees over three years. During that growth, data systems are added quickly: a CRM here, a project management tool there, a new payroll system, a Shopify store, a shared Google Drive that became the de facto file system. Nobody designed this stack — it accumulated.
Now the ops manager needs to find out: is the data in these systems reliable? Can leadership trust the reports that come out of them?
The Audit Scope Decision
Before starting, decide: what data matters most to this business right now?
For a service business: client/contact data quality, project status accuracy, invoice and payment data. For a product business: inventory accuracy, order data, customer database. For any business: financial data (most critical), employee data (most sensitive).
Sohovi scores your dataset against your own accuracy standards and highlights the columns and rows where values fall outside expected ranges.
Audit in order of business impact. Trying to audit everything simultaneously leads to an unfocused process that fixes the wrong things.
The Five-Step Audit Process
Step 1: Inventory all data systems List every tool that stores business data: CRM, accounting software, project management tool, HRIS, email platform, inventory system, document management. Include the ad hoc ones: the shared spreadsheets in Google Drive, the Notion workspace, the Airtable base someone created for a specific project.
Step 2: Identify the critical fields per system For each system, what are the 5–10 fields that most affect business decisions or reporting? These are your audit targets.
Step 3: Sample and assess For each critical field, sample 50–100 records:
- What % are complete (not blank)?
- What % are consistent (standard format, from an approved list if applicable)?
- What % are plausible (no obviously wrong values)?
This sampling gives you a data quality score per system and per field.
Step 4: Identify the source of problems For any field with poor quality, trace back: Who enters this data? When? What validation exists? Is there a clear standard that's not being followed, or no standard at all?
Step 5: Prioritize and fix Fix in order: highest-impact data (what affects the most decisions), with the most achievable fix (process change vs. system change vs. data cleanup). Build a remediation backlog with owners and deadlines.
Reporting to Leadership
Ops managers who audit data quality should report in business terms, not data terms:
"Our customer database has a 15% duplicate rate. This means our email campaigns are reaching 15% of our customers twice and potentially missing others. Estimated fix: 12 hours of deduplication work and a new intake form. Estimated ongoing risk if not fixed: inflated campaign costs and deliverability risk."
Sohovi automatically finds every duplicate in your dataset — including near-matches — and shows you exactly which rows are affected.
The business case makes the investment decision easy.
