# Data Quality
**Source:** https://glossary.keenfunnel.com/terms/data-quality
**Language:** Russian

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## Техническое объяснение

Quality is evaluated against explicit requirements and context. Controls include schema validation, reference checks, reconciliation, deduplication, anomaly detection, data contracts, issue workflows, ownership, and monitoring across sources and transformations.

## Актуальность для бизнеса

Poor-quality data produces unreliable reporting, failed automation, weak customer experiences, regulatory exposure, and unsafe or ineffective AI decisions.

## Пример реализации

A revenue team defines required CRM fields, valid lifecycle transitions, uniqueness rules, and freshness targets, then monitors violations and assigns remediation owners.

## Ограничения и распространенные заблуждения

Quality is purpose-dependent: data suitable for one decision may be inadequate for another. A dashboard score can hide critical field-level failures, and cleansing downstream does not solve defective source processes.

## Темы

Инженерия данных Управление ИИ

## Источники

ISO 8000 Data Quality — IBM — Data Quality — https://www.ibm.com/think/topics/data-quality

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