From data deduplication to metadata management, we break down the critical categories of vendors helping enterprises build a solid AI strategy by improving their data quality.
The recent explosion of generative AI applications is pushing enterprises to invest in cleaning up their data quality. Tech solutions in this space help lay the foundation for deploying reliable AI models and minimize the downstream effects of poor-quality data on AI performance.
Implementing effective data quality solutions can also be a source of time and cost savings, given data teams spend up to 40% of their time doing quality checks.
In the market map below, we identify 67 data quality vendors operating across 8 categories.
Note: Our map primarily includes private startups that have either submitted an Analyst Briefing or have at least $20M in funding, a Mosaic score of at least 400, and have received funding in the last 5 years. This market map is not exhaustive of the space.
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