# Beyond Expectations: Unpacking data quality in feature engineering

**URL:** <https://discourse.greatexpectations.io/t/beyond-expectations-unpacking-data-quality-in-feature-engineering/1272>\
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**Created:** [July 16, 2023, 7:31pm UTC](https://discourse.greatexpectations.io/t/beyond-expectations-unpacking-data-quality-in-feature-engineering/1272 "2023-07-16T19:31:05Z")\
**Posts on this page:** 1\
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**Author:** ![mollie.pettit](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.greatexpectations.io/mollie.pettit/32/196_2.png) [@mollie.pettit](https://discourse.greatexpectations.io/u/mollie.pettit)\
**Post date:** [July 16, 2023, 7:31pm UTC](https://discourse.greatexpectations.io/t/beyond-expectations-unpacking-data-quality-in-feature-engineering/1272/1 "2023-07-16T19:31:05Z")

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👉 [Beyond Expectations: Unpacking data quality in feature engineering](https://greatexpectations.io/blog/beyond-expectations-unpacking-data-quality-in-feature-engineering) 👈

What is feature engineering, and why does it need data quality? GX user [Aditya Nambiar](https://www.linkedin.com/in/adityanambiar7?trk=public_post-text) breaks down how [Fennel](https://fennel.ai) + [GX](https://greatexpectations.io) keep your machine learning data top-tier in blog post.