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Learn how Choozle reduced data incidents by 88% with Monte Carlo.
reduction in data incidents.
reduction in time to incident resolution.
- Rapid rate of table creation by over 6000+ end users increases difficulty of monitoring.
- 3rd party data sources from demand-side platforms creates potential of source-side formatting changes, leading to broken analytics.
- Lack of visibility into when new data sources were added, and what data quality metrics should be associated with those objects.
- Automated data quality monitor deployment on every new data source and table added to the warehouse.
- Lineage graphs to show relationships between tables and fields.
- Opt-in monitors covering a variety of data quality metrics, such as % of null values.
“Monte Carlo alerts are high quality. We don’t get many false alarms, which really helps build a culture of urgency to event management and response.”
Adam Woods Chief Technology Officer
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“Data observability has become a necessity, not a luxury, for us,” said Alex. “As the business has become more and more data-driven, nothing is worse than allowing leadership to make a decision based upon data that you don’t have trust in. That has tremendous costs and repercussions.”
“One of the things that helps us is we’re getting fancy reports which for me is a get out of jail free card. Now, whenever someone comes to me and says ‘hey the data is bad,’ I can find out where the data is bad and say ‘well you all haven’t been responsive to alerts for the past month, obviously its bad.'”
Out-of-the-box coverage across all your data tables, opt-in monitors for key assets, and monitors-as-code.
Don’t just sound the alarm when data incidents occur. Empower your data teams to resolve incidents in minutes.