# Data Volume Is a Poor Measure of Data Team Sophistication

DevFeed: [Data Volume Is a Poor Measure of Data Team Sophistication](<https://devfeed.tech/articles/i-don-t-care-how-big-your-data-is-82969.md>)

Original publisher: [Read original article](<https://montecarlo.ai/blog-big-data-quality>)

Author: Barr Moses

Published: 2022-06-23T18:57:20Z

Content type: opinion

Language: en

Sources: [Monte Carlo](<https://devfeed.tech/sources/monte-carlo.md>)

Topics: [Data Quality](<https://devfeed.tech/topics/data-quality.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>), [big-data](<https://devfeed.tech/topics/big-data.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [big-data-quality](<https://devfeed.tech/tags/big-data-quality.md>), [data-discovery](<https://devfeed.tech/tags/data-discovery.md>), [data-management](<https://devfeed.tech/tags/data-management.md>), [data-platforms](<https://devfeed.tech/tags/data-platforms.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>)

## AI overview

The author argues that data teams should prioritize quality, usability and business value over collection volume. Curated data products, reliable machine learning inputs, documentation and discovery help make data useful. Deprecating unnecessary assets and improving lineage can also reduce data debt and downstream quality problems.

## Source excerpt

Get proven strategies for managing big data quality and learn why, when it comes to big data quality, bigger isn't always better.