Autonomous data quality monitoring that builds data trust

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Data quality monitoring for teams that build data products

Monitoring data quality is more than pipeline testing and the job status. Examine the data content, track core quality measures, and incorporate domain-specific conditions with Proximo Data’s comprehensive and autonomous data quality monitoring in one easy setup.

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For data teams

Be the go-to stewards of reliably great data

Quickly track core measures of data quality

By default, monitor timeliness (whether data is late to update), volume (missing or duplicate data), schema changes, and column quality (anomalies).

Ditch the lengthy, manual setups or expensive upkeep

Get the visibility you need to build confidence in the quality of your data products without spending time building or maintaining custom systems.

Get everyone on the same page

Help your team use the same core measures of data quality. Use domain knowledge to customize monitoring further, and share that info with the broader team.

Connect with any database

Sync any SQL compatible data storage solution: Presto, Athena, Snowflake, Postgres, MySQL, Aurora, DataBricks, and more.

For business users of data

Monitor data quality in a self-serve way

View data quality in your terms

Set filters, data aggregations, domain specific conditions, and key metrics that capture the way you actually use the data. No code required.

Be a true partner in data quality

You’re the domain expert. You should have visibility into data quality problems and be able to work with data teams to manage, monitor, and fix issues.

Focus on only the most important problems

There are always problems in the data. With a risk level created per data issue—based on anomaly detection and data flagged by users as most important—you can focus on what matters most.

Build world-class data trust with key stakeholders on day one.

Data teams use Proximo to proactively identify data problems, understand their customer impact, and communicate quickly to relevant stakeholders.

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