Data Accuracy: How to Measure and Improve It

Your sales team called 200 leads last quarter. Forty of the phone numbers were wrong. The leads existed — the contact information didn't. That's a data accuracy problem, and it costs real time and real money.

Data accuracy measures how closely the values in your dataset reflect the true state of the real-world entities they represent. An address that doesn't exist, a revenue figure that was entered incorrectly, or a product weight that hasn't been updated since the product changed — all of these are accuracy failures.

Accuracy vs. Validity: An Important Distinction

These two dimensions are often confused. Validity asks: "Does this value conform to the expected format?" Accuracy asks: "Is this value actually true?"

A phone number can be valid (10 digits, correct format) but inaccurate (belongs to the wrong person). An email address can be syntactically valid but point to an inbox that no longer exists. Validity checks can be automated; accuracy checks often require ground-truth comparison.

Causes of Data Accuracy Failures

At entry: Typos, transpositions, incorrect lookups, or deliberately falsified information (fake email addresses entered to get past gated content).

Over time: People move, phone numbers change, companies rebrand, prices update. Data that was accurate at capture becomes stale.

During transfer: ETL processes that truncate fields, rounding errors in numeric calculations, or encoding issues that corrupt characters.

From third parties: Vendor-supplied data that was accurate in the source system but doesn't match your context.

How to Measure Data Accuracy

Unlike completeness (which is straightforward to calculate), accuracy often requires a reference dataset or manual sampling. Common approaches:

Sample-based validation — Randomly select 50–100 records and manually verify the most critical fields against a reliable source. Use the error rate to estimate accuracy across the full dataset.

Rule-based flagging — Create business rules that flag implausible values: customers with ages under 18 in a B2B database, revenue figures that exceed your known maximum deal size, dates that fall before your business existed. These flags don't confirm inaccuracy, but they identify candidates for review.

Cross-system comparison — If the same data exists in two systems (customer phone number in your CRM and in your billing system), compare the two. Discrepancies indicate that at least one is wrong.

External verification — For contact data, email verification services and address validation tools can confirm whether records are currently deliverable.

A Practical Accuracy Audit for Small Businesses

You don't need a formal data quality platform to run a basic accuracy audit. Here's a straightforward approach for a contact database:

1. Export your contact records to a spreadsheet
2. Select 50 records at random — use a random number generator to pick row numbers
3. For each record, verify the email (send a test), the phone (make a call or use a lookup service), and the company name (check their website or LinkedIn)
4. Count the errors by field
5. Multiply error rate × total records for an estimate of total inaccuracies

A 10% phone number error rate on a 5,000-record database means roughly 500 wrong numbers. That's a concrete problem with a concrete solution: an append or verification pass on the phone field.

Sohovi lets you upload your CSV and get an instant data quality report — no setup, no code required. It profiles every column for completeness, uniqueness, and format patterns — giving you a starting point for accuracy assessment in seconds.

Accuracy Decay: Why Data Gets Worse Over Time

Data that's accurate today isn't necessarily accurate next year. Industry estimates suggest that B2B contact data decays at around 25–30% annually — meaning roughly 1 in 4 records becomes inaccurate each year as people change jobs, companies are acquired, and contact details shift.

This has a practical implication: data accuracy isn't a one-time fix. It requires ongoing maintenance. The cadence depends on how you use the data:

• High-frequency use (weekly campaigns, active sales outreach): verify and refresh annually
• Periodic use (quarterly mailers, annual audits): verify before each use
• Archival use (historical records for compliance): maintain accuracy at point of capture, document clearly that records may be stale

Improving Accuracy at the Source

The cheapest accuracy fix is preventing errors from entering the system in the first place:

Validation at entry: Required fields, format checks, and dropdown menus reduce entry errors. If your CRM requires a valid email format before saving, you catch typos before they become problems.

Verification at entry: For critical fields like email and phone, real-time verification services can check deliverability as the user types.

Training and incentives: Sales teams that enter data quickly and inaccurately are often incentivized to prioritize speed over quality. If data accuracy is never measured, it's never improved. Track accuracy metrics and make them visible.

When Accuracy Matters Most

Not all inaccurate data carries the same cost. Prioritize accuracy improvement in fields that drive decisions or customer interactions:

• Email addresses — directly affect deliverability and revenue
• Mailing addresses — affect physical fulfillment costs and customer experience
• Revenue and financial figures — affect business decisions and reporting
• Product specifications — affect customer expectations and returns

Fields used only for internal categorization or analytics carry lower stakes. Focus your accuracy efforts where errors have direct financial consequences.

If you're ready to stop guessing about your data quality, Sohovi is built for exactly this. Upload your first CSV free — no credit card, no IT team, no code needed.