---
title: 'Data drift'
description: 'A change over time in the distribution of system inputs or features.'
canonical_url: 'https://darkfactory.dev/glossary/data-drift'
markdown_url: 'https://darkfactory.dev/glossary/data-drift.md'
collection: glossary
date_published: '2026-08-03T00:00:00-04:00'
date_modified: '2026-08-26T00:00:00-04:00'
---

# Data drift


## Definition

Data drift is a change over time in the distribution of inputs or features seen by a system. Examples include a shift in customer geography, document length, device type, language, or the frequency of missing values.

Drift can indicate a real population change, a broken pipeline, a new product, seasonality, or an attack. Statistical distance can flag that something moved, but it does not say whether the change matters. Pair drift monitoring with data-quality checks and performance evidence on affected slices.

Choose a reference period deliberately. A stable annual pattern may look alarming against last month's data, while slow deterioration can disappear if the baseline continually rolls forward.

## Distinguish it from nearby terms

Concept drift changes the relationship between inputs and desired outputs. Data drift can occur while that relationship remains valid. Data-quality failure corrupts inputs and may imitate drift without representing a real population change.

## Check your understanding

Document length doubles after a new upload feature launches, but extraction accuracy stays stable. The input distribution drifted; the evidence does not yet show concept or performance drift.

## Related terms

- [Concept drift](https://darkfactory.dev/glossary/concept-drift)
- [Model drift](https://darkfactory.dev/glossary/model-drift)

## Evidence and further reading

- [NIST AI Resource Center Glossary](https://airc.nist.gov/glossary/)
- [Google Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)