You can audit your data quality in 5 steps: define the scope and standards, profile the dataset across all quality dimensions, score and prioritize the findings, document the results, and identify root causes for each issue found.
A data quality audit is a structured process most businesses can complete in a few hours for their most critical dataset. The output is a clear picture of what's wrong, how severe it is, and what to do about it.
Step 1: Define the Scope and Standards
Choose Your Dataset
Pick the one dataset where a quality audit will have the most impact:
- Primary customer contact database
- CRM pipeline data
- Email marketing list
- Product catalog
- Financial transaction records
Sohovi finds gaps, duplicates, and format errors in your CRM data — so your team is working from records they can trust.
One dataset, done well, produces more actionable findings than a superficial review of five.
Define Your Quality Standards
Before running any checks, define what "acceptable quality" looks like. For each key field:
- Completeness threshold: what percentage of rows must have a value? (Email: ≥ 98%)
- Validity rule: what format is acceptable?
- Uniqueness requirement: should this field be unique?
- Allowed values: for categorical fields, what values are permitted?
Standards defined upfront give your findings meaning.
Step 2: Profile the Dataset
Profile across five dimensions. This is the measurement step — gather raw numbers without yet analyzing whether they're acceptable.
Completeness: Percentage of non-null, non-empty values per field.
Sohovi profiles every column in your dataset for completeness and flags the exact rows where values are missing — free to try.
Uniqueness: Percentage of values duplicated on fields that should be unique.
Validity: Percentage of values matching the expected format (email, phone, date).
Consistency: Are categorical values from the approved list? Is the same information represented the same way across records?
Timeliness: What percentage of records haven't been updated in the past 12 months?
[IMAGE: Screenshot of a data profiling report showing completeness rates, duplicate counts, and validity rates per column]
Sohovi automates the entire profiling step — upload your CSV and get completeness rates, duplicate counts, and format analysis for every column in under a minute. Step 2 goes from two hours to five minutes.
Sohovi automatically finds every duplicate in your dataset — including near-matches — and shows you exactly which rows are affected.
Step 3: Score and Prioritize the Findings
Compare profiling results against the standards from Step 1. Every metric below its standard is a finding.
For each finding, document:
- Which field is affected
- Actual metric value vs. the standard
- How many records are affected (specific count, not just percentage)
- Which downstream process or use case is affected
Scoring:
| Score | Criteria | |---|---| | Critical | Affects a compliance requirement OR > 20% of records in a customer-facing field | | High | Affects > 10% of records in a business-critical field OR creates a visible customer problem | | Medium | Affects 5–10% of records in an important field | | Low | Affects < 5% of records in a non-critical field |
Step 4: Document the Results
An audit finding that isn't documented will need to be re-discovered. Create a brief audit report including:
- Date, dataset audited, row count, source, who conducted the audit
- Standards applied
- Findings table: field, dimension, actual metric, standard, score, records affected
- Recommended actions: immediate, near-term, and ongoing
Step 5: Identify Root Causes
Documenting findings tells you what's wrong. Root causes tell you how to prevent recurrence.
For each Critical and High finding, investigate:
- When did it start? Check dates of affected records — did the problem begin after a specific event (migration, new import, process change)?
- Where does this data come from? Which system, form, or process creates or updates this field?
- What's missing or wrong about the source process? A form field that isn't required produces missing values. An import that doesn't deduplicate produces duplicates.
Add a prevention recommendation to your audit report for each root cause.
Frequently Asked Questions
Q: What is a data quality audit? A data quality audit is a systematic evaluation of a dataset across completeness, uniqueness, validity, consistency, and timeliness dimensions — producing a scored, prioritized list of findings and recommendations.
Q: How long does a data quality audit take? For a typical small business dataset, 2–4 hours is realistic for a first audit. Using an automated profiling tool reduces the measurement step significantly.
Q: What's the difference between a data quality audit and a data quality assessment? The terms are often used interchangeably. An audit tends to be more formal — with documented standards, scored findings, and root cause analysis. An assessment is often a lighter-weight evaluation.
Q: How often should I run a data quality audit? Quarterly for your most important datasets, before any major system migration, before a significant campaign that depends on the data, and when a visible quality failure has occurred.
Q: Who should conduct a data quality audit? The person who knows the data well enough to judge whether findings are significant — typically the ops manager or data owner. Business knowledge is more important than technical expertise for the analysis steps.
Q: Do I need a dedicated tool to run a data quality audit? No. Spreadsheet formulas handle completeness counts, duplicate identification, and basic validity checks. A profiling tool makes the measurement step faster and more complete.
Q: What should I do with the audit findings? Share with the dataset owner, assign ownership for each remediation item, schedule high-effort fixes as formal projects, start low-effort, high-impact fixes immediately, and set up monitoring to track whether findings recur.
Q: How do I know if my data quality audit was thorough enough? If you checked all five quality dimensions for key fields and produced specific, measurable findings with root cause analysis and recommendations — it's thorough enough.
Q: What's the most important finding type to look for in a data quality audit? Uniqueness failures (duplicates) are typically the highest priority because they affect almost every downstream process and are often more widespread than teams expect.
Q: Can I audit data quality on a sample rather than the full dataset? Yes. For very large datasets, a random sample of 5–10% of records is statistically sufficient for estimating quality metrics.
A data quality audit is not a project to put off until you have more time. It's the five-step process that gives you the information you need to stop paying the hidden cost of bad data.
Sohovi makes Step 2 instant — upload your CSV and get a complete quality profile in under a minute. Free to try, no credit card, no IT team, no code required.