data validation

USAGE SUMMARY

The phrase "data validation" is correct and usable in written English.
It is typically used in contexts related to ensuring the accuracy and quality of data, often in fields like programming, data analysis, and database management. Example: "Before processing the information, we need to perform data validation to ensure all entries are accurate and complete."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Structured Data Validation: After the markup is implemented, validate that it is rendering correctly using Google's Structured Data Testing Tool.

Forbes

See Data Validation for more information.

University of California, Berkeley

RNA-seq expression data validation by quantitative RT PCR.

Nature

Interviews have found that managers value data validation and quality over use.

The Guardian

"It's an eye-opener," says Soko. "Data validation, missing reports... everything is at my fingertips.

The Guardian

Though a team does check new data for formatting and new organisations, data validation is currently absent.

The Guardian

W.V. participated in data validation during Phase III and commented on the manuscript.

Nature

J.H. participated in data validation and quality control during Phase III and commented on the manuscript.

Nature

The C-Lock process incorporates three levels of data validation.

Green Trading Markets Developing the Second Wave

These include inadequate data validation, insecure data storage and inadequate authentication mechanisms.

University of California, Berkeley

J.C. entered data during Phase II, participated in data validation during Phase III, and commented on the manuscript.

Nature

Expert writing Tips

Best practice

Implement "data validation" rules early in the data lifecycle to prevent errors from propagating through your system.

Common error

Ensure your "data validation" covers not just typical inputs, but also edge cases and potential boundary conditions that could lead to unexpected errors.

Linguistic Context

The phrase "data validation" functions primarily as a noun phrase, often acting as the subject or object of a sentence. Ludwig AI confirms its grammatical correctness and common usage.

Expression frequency: Very common

Frequent in

Science

45%

News & Media

25%

Academia

20%

Less common in

Formal & Business

10%

Ludwig's WRAP-UP

In summary, "data validation" is a grammatically sound and frequently used noun phrase referring to the process of ensuring data accuracy and quality. Ludwig AI confirms that it is used across various fields like science, news, academia, and business. Related terms include "data verification" and "input validation". Effective data validation is crucial to avoid errors and maintain system integrity, starting early in the data lifecycle and covering edge cases. As such, understanding how to properly implement these processes can be invaluable.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

data verification

Focuses more on confirming the accuracy of data.

input validation

Specifically refers to validating data as it is entered into a system.

quality control

Emphasizes the overall process of ensuring data quality.

data cleansing

Highlights the process of correcting or removing inaccurate data.

data integrity check

Focuses on ensuring the completeness and consistency of data.

validation testing

Emphasizes the testing aspect of the validation process.

error checking

Highlights the process of identifying errors in data.

consistency check

Focuses on verifying the consistency of data across different sources or formats.

accuracy assessment

Focuses on measuring the degree to which data conforms to the correct value.

information assurance

Implies a broader concept of protecting data and ensuring its reliability.

FAQs

How is "data validation" used in software development?

"Data validation" is used to ensure that data entered into a system meets specific criteria, preventing errors and security vulnerabilities. It can involve checking data types, formats, and ranges.

What are some techniques for performing "data validation"?

Techniques include type checking, range checking, format validation, and consistency checks. Regular expressions are often used for format validation.

What is the difference between "data validation" and "data verification"?

"Data validation" ensures data meets predefined criteria, while "data verification" confirms that data is accurate and complete. Validation checks format and consistency; verification checks against a known standard or source.

Why is "data validation" important?

"Data validation" is crucial for maintaining data quality, preventing errors, and ensuring that systems operate correctly. It helps to avoid data corruption, security breaches, and inaccurate reporting.