# Data Mining
**Source:** https://glossary.keenfunnel.com/terms/data-mining
**Language:** Hindi

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## तकनीकी स्पष्टीकरण

The process of discovering patterns, relationships or anomalies in datasets using statistical, computational and machine-learning methods. Implementation requires documented sources, schemas, transformations, access controls, quality checks, lineage, observability and lifecycle ownership. The architecture should reflect latency, scale, retention and governance requirements.

## व्यावसायिक प्रासंगिकता

It improves the reliability and reuse of information for analytics, automation and AI while reducing reconciliation and decision risk.

## कार्यान्वयन उदाहरण

A cross-functional team applies Data Mining in a production initiative, defines ownership and success criteria, tests representative scenarios, monitors outcomes and records corrective actions before scaling.

## सीमाएँ और आम गलत धारणाएँ

The approach does not guarantee trustworthy data. Poor source quality, missing lineage, uncontrolled access and rising platform cost can undermine the intended value.

## विषय

डेटा इंजीनियरिंग

## स्रोत

Google Cloud Data Analytics — IBM Data and AI — https://www.ibm.com/think/topics/data-and-ai

## अपने सिस्टम पर चर्चा करें

इस अवधारणा को लागू करने या मूल्यांकन करने में मदद चाहिए? कीनफ़नेल कनेक्टेड एआई, स्वचालन और डेटा प्रणालियाँ डिज़ाइन करता है।

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