What is data validation?

Data validation is the process of checking incoming data to ensure it is complete, correct, consistent, and properly formatted before entering a business system or workflow. This prevents errors, saves time, and ensures reliable data for automation and reporting.

Why is data validation important?

Poor-quality data can lead to failed processes, compliance risks, and frustrated customers. Studies estimate companies lose 15–20% of their revenue each year due to bad data. Validating data upfront helps avoid these losses by ensuring information is accurate and usable from the start.

What are common types of data validation?

Typical validation checks include: presence checks (mandatory fields are filled), format checks (dates, phone numbers, postal codes), range checks (values within defined limits), cross-field checks (logical consistency across fields), and reference checks (IDs or codes exist in a valid list).

Validating incoming information: why clean data makes or breaks your support

Every ticket, every form, every attachment depends on the quality of its data. When inputs are incomplete or inconsistent, teams lose time and customers lose patience. Validating incoming information ensures data is usable and reliable from the start.

Explore data validation tools See data preprocessing

Daily reality: when data goes wrong

Return forms arrive without an order ID.
Phone numbers lack a country code.
Product spreadsheets miss mandatory attributes.
Scanned invoices arrive incomplete and trigger an automatic follow-up request.

Each case adds work and slows responses. Across hundreds of tickets, the effect compounds.

Why data quality breaks down

Most incoming information isn’t automation-ready. It arrives as forms, emails, spreadsheets, PDFs, or photos of handwritten notes. Without checks, messy inputs flow into systems. When poor data enters, reports lose accuracy, automations fail, and trust erodes.

See Harvard Business Review and Gartner on the cost of bad data.

Preprocessing before validation

Preprocessing makes raw inputs usable: turn scans into text, extract values from Excel/CSV, and structure unformatted documents into clean fields.

Learn about preprocessing

What “validating incoming information” means

Presence checks: a return form must include an order ID.
Format checks: dates use YYYY-MM-DD; phone numbers include a country code.
Range checks: discounts fall between 0–60%, not 600.
Cross-field checks: an end date must be after the start date.
Reference checks: product IDs exist in the catalog.

Manual vs. automated validation

Manual

Open files, read line by line, send “please complete” emails.
Slow and error-prone at scale.

Automated

Detect the document type, extract fields, check rules instantly.
Trigger smart requests when data is missing; resume automatically once corrected.
See Knots case studies for results.

See Twilio and ITU E.164 on phone number validation.