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Financial Management and Business Data Analytics · Data Processing, Organisation, Cleaning and Validation

Data Validation Techniques: Types of Checks with Examples

Updated 10 October 2026 · Fact-checked

Data validation is the process of checking data against defined rules before you use it, so that only accurate, sensible and complete data enters analysis. Common checks are range, type, format, consistency, completeness and uniqueness. To solve a question, name the check, state the rule, apply it to each record and report which values fail.

Understand Data Validation Techniques

Data is only useful if you can trust it. If a customer's age is entered as 250 or an invoice date is blank, any report built on it will mislead you. Data validation is the set of rules that catches such errors at the time of entry or before analysis begins.

Each rule tests one property of the data. A range check tests whether a number lies between a minimum and a maximum. A type check tests whether the value is the right kind (number, text, date). A format check tests the pattern, such as a PAN having 10 characters in a fixed letter-digit layout. A consistency check tests whether two fields agree with each other, such as a delivery date not being earlier than the order date. A completeness check tests that required fields are not blank.

Other common checks are a uniqueness check (no duplicate invoice numbers), a list or lookup check (the value must be from an allowed list, such as a valid state code) and a check digit (a calculated digit that exposes typing mistakes).

Do not confuse validation with verification. Validation asks: is this data reasonable and in the allowed form? Verification asks: does this data match the original source or what was intended, for example by double entry or comparing with the source document? A value can pass validation and still be wrong. If the true age is 34 but 43 was typed, a range check passes it, but only verification against the source catches it.

In practice you apply validation at entry (for example Excel's Data Validation feature restricting a cell to whole numbers between 1 and 100) and again during cleaning, when you test a dataset you have received. Records that fail are corrected, queried with the source, or flagged and excluded.

Key rules to remember

Range check rule
Valid if minimum ≤ value ≤ maximum
Decide whether the limits are inclusive. Say so in your answer.
Completeness rate
Completeness % = (Records with the field filled ÷ Total records) × 100
Use it to report how much of a required field is missing.
Consistency check rule
Valid if related fields satisfy their logical condition, e.g. delivery date ≥ order date
Compares two or more fields in the same record.
Validation vs verification
Validation = is it reasonable and in the allowed form? Verification = does it match the source?
A value can pass validation yet fail verification.
Error rate
Error rate % = (Records failing a check ÷ Total records checked) × 100
Report it per check, not as one combined figure.

How to solve Data Validation Techniques questions

Use this method for any question that gives you a dataset or a scenario and asks which validation check applies or what the data shows.

  1. 1Read the field and what it represents in the business, such as age, GSTIN, invoice date or quantity.
  2. 2Decide what property is at risk: size of the value, kind of value, pattern, agreement with another field, or presence.
  3. 3Name the matching check (range, type, format, consistency, completeness, uniqueness or lookup).
  4. 4State the rule precisely with its limits or pattern, for example 'age between 18 and 65 inclusive'.
  5. 5Apply the rule record by record and list the records that fail, with the reason.
  6. 6Compute the count or error rate if asked.
  7. 7State the action: correct, query the source, or exclude, and note whether verification is also needed.

Quickest way: Field-to-check matching

When to use it: Use it for MCQs and short scenario questions where you must pick the right check within a minute.

  1. Number too big or small, or negative where impossible: range check.
  2. Text typed in a numeric field, or wrong kind of entry: type check.
  3. Wrong pattern or length, such as PIN code or PAN: format check.
  4. Two fields contradict each other: consistency check.
  5. Blank required field: completeness check.
  6. Same ID appears twice: uniqueness check.
  7. Question says 'compare with source document' or 'double entry': verification, not validation.

Common mistakes in Data Validation Techniques

  • Treating validation and verification as the same thing.

    Both words sound like 'checking' and both aim at accuracy.

    Fix: Validation tests against rules. Verification tests against the source or intent. Write one line distinguishing them.

  • Calling a blank cell a range or format error.

    Students see an invalid-looking record and pick the first check that comes to mind.

    Fix: A missing value is a completeness failure. Range and format checks apply only to values that are present.

  • Assuming a value that passes validation is correct.

    Passing a rule feels like proof of accuracy.

    Fix: Validation only shows the value is plausible. Transposed digits within the allowed range still pass, so verification is needed.

  • Confusing type check with format check.

    Both deal with how a value looks.

    Fix: Type asks whether it is a number, text or date. Format asks whether it follows a pattern, such as 6 digits for a PIN code.

  • Leaving the range limits vague or getting inclusivity wrong.

    Students write 'reasonable age' instead of exact limits.

    Fix: Always state minimum and maximum and whether the ends are included, then test the boundary values.

  • Using a consistency check on a single field.

    The term is mistaken for 'consistent formatting'.

    Fix: Consistency compares two or more fields (or the same field across records). Single-field limits are range checks.

Worked examples

Example 1

A dataset of 6 employee records has these entries (ID, age, joining date, date of birth): E1: 28, 12-04-2020, 15-03-1997; E2: 214, 01-07-2019, 10-10-1980; E3: blank, 05-08-2021, 22-02-1995; E4: 45, 10-01-2015, 05-05-2000; E5: 33, 09-09-2018, 14-06-1992; E6: 51, 03-03-2012, 18-11-1973. Rule: age must be between 18 and 65 inclusive and must be filled; joining date must be after the 18th birthday. Identify the failures by check type.

Show the solution
  1. Range check on age (18 to 65): E2 has 214, which is above 65, so it fails.
  2. Completeness check on age: E3 is blank, so it fails.
  3. Consistency check: the joining date must be on or after the date of birth plus 18 years.
  4. E4: born 05-05-2000, 18th birthday is 05-05-2018, but joined 10-01-2015, which is earlier, so it fails.
  5. E1: 18th birthday 15-03-2015, joined 2020, passes. E5: 18th birthday 14-06-2010, joined 2018, passes. E6: 18th birthday 18-11-1991, joined 2012, passes.
  6. Count: 3 records fail out of 6. Each record fails one check.

Answer: E2 fails the range check, E3 fails the completeness check and E4 fails the consistency check. E1, E5 and E6 pass. Error rate = 3 ÷ 6 × 100 = 50%.

Example 2

Distinguish data validation from data verification with one example each for a company capturing vendor invoices, and state which would catch a typed invoice amount of ₹45,200 when the invoice shows ₹54,200.

Show the solution
  1. Validation: checking data against preset rules for allowed form or reasonableness. Example: the invoice amount field accepts only positive numbers up to ₹50,00,000 (range and type check).
  2. Verification: confirming that the entered data matches the source. Example: a second clerk re-enters the amount, or the system compares it to the scanned invoice.
  3. Test the case: ₹45,200 is a positive number and is within ₹50,00,000, so it passes validation.
  4. The source shows ₹54,200, so the entry does not match the source. Verification would reveal this transposition.

Answer: Validation checks the rules; verification checks against the source. The ₹45,200 error passes validation and is caught only by verification.

Exam tips

  • In MCQs, match the check to the symptom: size is range, kind is type, pattern is format, two fields is consistency, blank is completeness.
  • In written answers, give the rule, a one-line example and the action for each check. This earns step marks.
  • Always include the validation versus verification distinction when the question mentions accuracy, as it is a frequent theory point.
  • If asked about Excel, mention Data Validation under the Data tab, with settings such as whole number, decimal, list, date or custom formula, plus an input message and error alert.

Practice questions from Data Processing, Organisation, Cleaning and Validation

Data Validation Techniques in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

Data Validation Techniques: frequently asked questions

What are the main types of data validation checks?

The common ones are range, type, format, consistency, completeness, uniqueness and list (lookup) checks. Each tests a different property of the data. Name the check that matches the error in the question.

What is the difference between data validation and data verification?

Validation checks whether data follows rules, such as allowed range or format. Verification checks whether the data matches its source or what was intended. Data can pass validation and still be wrong.

Can you give range check and format check examples?

A range check might require a discount percentage between 0 and 100. A format check might require a PIN code of exactly 6 digits, or a date written as DD-MM-YYYY.

How do I validate data in Excel?

Select the cells, open the Data tab and choose Data Validation. Pick an allowed type such as whole number, decimal, list, date or custom, set the limits, and add an error alert message. You can also use Circle Invalid Data to find existing bad entries.