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How to Calculate the ROI of Data Observability
Learn to measure the ROI of Data Observability to ensure your investments in data management pay off through improved data quality and trust.
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Stay ahead of data issues by quickly detecting schema changes, duplicates, and null values.
Employ smart detection model to gauge the potential downstream impact of anomalies on data assets.
By connecting our platform to Microsoft Teams or Slack, you'll never miss any failed tests or unexpected data issues.
Our solution offers seamless integration with diverse data sources for effortless configuration module.
The asset configuration feature simplifies the monitoring setup process through centralized control panel where you can set up all your monitoring needs in one place.
Have the flexibility to create custom monitors for your unique business use cases. Set up custom SQL monitors to closely track query performance and identify any issue that may arise.
Configure data quality tests on a schedule that works best for you. You can choose to run these tests daily, weekly, or at any other interval you prefer.
Easily run tests and perform manual checks instantly if you suspect any data quality issues.
Train the alerting system by giving feedback to reduce or increase the sensitivity of alerts for ML-Generated tests.
Easily run tests and perform manual checks instantly if you suspect any data quality issues.
Write your own tests with SQL scripts to set up monitoring specific to your needs.
Find where the incident took place and replicate events for faster resolution times.
Enable monitoring across multiple tables within sources by our one-page bulk config.
Choose which fields to monitor with 12 available test types such as null%, regex_match, cardinality etc.
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Thresholds for table tests such as Volume and Freshness are auto-detected by our system once data source is connected.
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Alerts are grouped so we don't spam you 100s of notifications. We also deliver them directly to your email or Slack.
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Always experience missing data? Check for data-diffs between any two datasets such as your staging and production tables.
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