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 sounds involved. In practice, it's a structured process that 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.
Here are the five steps.
Step 1: Define the Scope and Standards
Choose Your Dataset
Pick the one dataset where a quality audit will have the most impact. Good starting points:
- Your primary customer contact database
- Your CRM pipeline data
- Your email marketing list
- Your product catalog
- Your financial transaction records
Sohovi finds gaps, duplicates, and format errors in your CRM data — so your team is working from records they can trust.
Don't try to audit everything at once. 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 this dataset. For each key field, specify:
- Completeness threshold: What percentage of rows must have a value? (Email: ≥ 98%)
- Validity rule: What format is acceptable? (Email: must contain @ and domain)
- Uniqueness requirement: Should this field be unique? (Customer ID: yes)
- Allowed values: For categorical fields, what values are permitted? (Status: Active, Inactive, Pending)
Sohovi profiles every column in your dataset for completeness and flags the exact rows where values are missing — free to try.
Standards defined upfront give your findings meaning. Without them, you know what the data looks like — but not whether it's acceptable.
Step 2: Profile the Dataset
Profile the dataset across the five core data quality dimensions. This is the measurement step — the goal is to gather raw numbers without yet analyzing whether they're acceptable.
Completeness: For each field, what percentage of rows have a non-null, non-empty value?
Uniqueness: For fields that should be unique (email, customer ID, order number), what percentage of values are duplicated?
Validity: For fields with format requirements (email, phone, date), what percentage of values match the expected format?
Consistency: For categorical fields, are all values from the approved list? For text fields, is the same information represented the same way across records?
Timeliness: What is the distribution of "last modified" or "created" dates? What percentage of records haven't been updated in the past 12 months?
Record raw numbers for each check. You'll analyze them in the next step.
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. This reduces Step 2 from two hours to five minutes.
Step 3: Score and Prioritize the Findings
Compare your profiling results against the standards you defined in Step 1. Every metric that falls below its standard is a finding.
For each finding, document:
- Which field is affected
- What the actual metric value is vs. the standard
- How many records are affected (specific count is more useful than a percentage alone)
- What downstream process or use case is affected
Then score each finding:
| Score | Criteria | |---|---| | Critical | Affects a compliance requirement OR affects > 20% of records in a customer-facing field | | High | Affects > 10% of records in a business-critical field OR creates a visible customer experience problem | | Medium | Affects 5–10% of records in an important field OR degrades analytics without immediate customer impact | | Low | Affects < 5% of records in a non-critical field OR is a cosmetic issue |
Sort your findings by score. Critical and High findings are your action items. Medium findings are your next project. Low findings are your backlog.
Step 4: Document the Results
An audit finding that isn't documented is an audit finding that will need to be re-discovered. Create a brief audit report that captures:
Header:
- Date of audit
- Dataset audited (name, row count, source system, export date)
- Who conducted the audit
Standards summary:
- Which standards were applied and their thresholds
Findings table:
| Field | Dimension | Metric | Standard | Score | Records Affected | |---|---|---|---|---|---| | email | Completeness | 94.2% complete | ≥ 98% | High | 580 records | | customer_id | Uniqueness | 2.1% duplicated | 100% unique | Critical | 210 records | | phone | Validity | 87% valid format | ≥ 95% | High | 1,300 records |
Recommended actions:
- Immediate: [critical and high findings, what to do, who owns it]
- Near-term: [medium findings, what to do, who owns it]
- Ongoing: [monitoring and prevention recommendations]
Step 5: Identify Root Causes
Documenting findings tells you what's wrong. Identifying root causes tells you how to prevent the same problems from recurring.
For each Critical and High finding, investigate:
- When did the problem start? Check the "created" or "last modified" dates of affected records. Did the problem begin after a specific event (a system migration, a new import, a process change)?
- Where does this data come from? Which system, form, or process creates or updates this field? The source of the data is usually the source of the quality problem.
- 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. An integration that maps fields incorrectly produces validity failures.
For each root cause, add a prevention recommendation to your audit report. Data quality audits without root cause analysis produce temporary fixes that don't prevent recurrence.
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. It answers: what's wrong with this data, how bad is it, and what should be done about it?
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, letting you focus time on analysis and root cause investigation.
Q: What's the difference between a data quality audit and a data quality assessment? The terms are often used interchangeably. In practice, an audit tends to be more formal — with documented standards, scored findings, and root cause analysis. An assessment is often a lighter-weight evaluation without full root cause investigation. Both produce findings; an audit produces more actionable documentation.
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 or business decision that depends on the data. When a visible quality failure has occurred. Some teams run monthly lightweight audits and quarterly full audits.
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 for that dataset. Technical skills aren't required if you're using a profiling tool for the measurement step. 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 data quality profiling tool makes the measurement step faster and more complete — but the audit can be done without one.
Q: What should I do with the audit findings? Share the prioritized findings with the dataset owner and relevant stakeholders. Assign ownership for each remediation item. Schedule high-effort fixes as formal projects. Start low-effort, high-impact fixes immediately. Set up monitoring to track whether the findings recur after remediation.
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. The goal of a business audit is actionable findings, not academic completeness.
Q: What's the most important finding type to look for in a data quality audit? Uniqueness failures (duplicates) are typically the highest-priority finding because they affect almost every downstream process that uses the data — marketing, sales, reporting, billing — 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. For fields with known problems or recent changes, review all records in that field rather than a sample.
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. Block the time, run through the steps, and act on what you find.
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.