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Data Science & Analytics vocabulary · Data Collection and Cleaning

Data quality

Meaning in data science & analytics, with examples

nounCore term

In data science & analytics, data quality means: How accurate, complete, consistent and up to date data is, and therefore how far it can be trusted when people make decisions.

In Turkish: veri kalitesi

How it is used

You check, monitor, measure or improve data quality. Teams set up data quality checks or tests that send an alert when values look wrong. 'Data quality issues' is a polite way to say that the data has problems.

Example sentences

  1. We found some data quality issues in the March numbers, so please don't share them yet.

  2. A daily data quality check now flags any day with zero orders.

Common collocations

  • data quality issues
  • data quality check
  • improve data quality
  • poor data quality
  • data cleaningThe process of finding and fixing problems in data, such as wrong formats, typos, duplicates and missing values, so that it can be trusted for analysis.
  • missing valuesEmpty cells or fields in a dataset where no value was recorded, often shown as NULL, NaN or a blank, which need to be handled before analysis.
  • duplicateA record that appears more than once in a dataset, for example the same customer or order stored twice, which can make counts and totals too high.
  • sanity checkA quick, simple test to make sure a result is reasonable before you trust it, for example checking that a total matches a figure you already know.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Data Collection and Cleaning

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