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Data Profiling

What to Look for When Profiling Customer Data for the First Time

Profiling a customer dataset for the first time reveals problems you didn't know you had. Here's exactly what to look for and how to prioritize what you find.

You've been handed the company's customer database. It's been in use for four years, fed by three different systems, and no one has ever run a systematic quality check on it. You're about to run your first profile. Here's what you're going to find — and what to do about it.

Start With the Most Critical Fields

Not all columns are equal. Start by profiling the fields that matter most for your primary use case:

  • For email marketing: email address (completeness and validity)
  • For sales outreach: phone number (completeness and format) and company name
  • For segmentation: industry, company size, job title
  • For compliance: consent fields, opt-out flags, data source

Sohovi profiles every column in your dataset for completeness and flags the exact rows where values are missing — free to try.

Profile these first. Everything else can wait.

What You're Likely to Find (And How Bad It Usually Is)

In a customer database that's been in use for 2+ years, typical findings include:

Email completeness: 60–80% (not 100% as you hoped) Duplicate records: 10–25% of the database Phone format inconsistencies: 8–15 different formats across the column Stale or inconsistent job titles: often 50+ variants where you expected 5–10 Missing company information: 15–30% of records have no company

Sohovi automatically finds every duplicate in your dataset — including near-matches — and shows you exactly which rows are affected.

These aren't outliers — they're the norm. The question isn't whether you'll find these problems; it's how severe they are.

The Priority Framework

After your first profile, prioritize fixes using this framework:

Fix first — Problems that directly affect the most frequent use case. If you send weekly email campaigns, email completeness and validity are your top priority.

Fix second — Problems that create compliance risk. Incomplete consent fields or PII in unexpected columns need attention before your next data processing activity.

Fix third — Problems that affect analytics but not operations. Inconsistent job titles matter for segmentation but won't break your next campaign.

Document and monitor — Problems you can't fix right now but need to track over time.

Using Sohovi for Your First Customer Data Profile

Upload your customer CSV to Sohovi and you'll see completeness rates, duplicate flags, format patterns, and PII detection for every column — in your browser, with your data never leaving your machine.

The most important thing about your first profile isn't the specific numbers — it's getting a clear picture of what you're actually working with. Most teams are surprised. The surprises are exactly what you need to see.

See how data profiling fits into a marketing ops workflow for a practical example of profiling customer data before a campaign.

Selva Santosh

Data quality, for people who ship

Selva writes practical guides on data quality, profiling, and governance to help teams ship better data.

Start for free

Stop guessing. Start knowing your data quality.

Sohovi profiles your datasets in minutes — surfacing completeness gaps, type mismatches, and duplicate patterns before they reach production.

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