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What is data observability? 
What is a data mesh--and how not to mesh it up 
The ULTIMATE Guide To Data Lineage Say hello to data reliability.
Monte Carlo’s data observability platform ensures your data is reliable at every stage of the data pipeline.
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What is data observability?
Data breaks – call it a fact of life. Poor data quality erodes stakeholder trust, data team resources, and company revenue. So, what’s a data team to do?
Enter: data observability. Data observability is a company’s ability to fully understand the health of its data at each stage in the data life cycle. In today’s world, data observability is a must-have for companies serious about accelerating data adoption and realizing the potential of their data investments.
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Freshness seeks to understand how up-to-date your data tables are, as well as the cadence at which your tables are updated. Freshness is particularly important when it comes to decision making; after all, stale data is basically synonymous with wasted time and money.
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Monitoring data volume can help identify missing data, duplicate data and other issues. If 200 million rows suddenly turns into 5 million, you should know.
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Is your data in an acceptable range? Quality gives you visibility into null values, duplicate data, and other specific issues based on what you should expect from your data.
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Were any changes made to the organization of your data? Monitoring who makes changes to these tables and when is foundational to understanding the health of your data ecosystem.
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When data breaks, the first question is always “where?” Data lineage provides the answer by telling you which downstream assets were impacted, which upstream sources are contributing to the issue and which colleagues need to be looped in.
Is data testing enough?
Short answer is, “no.” Data can break for millions of reasons, and the sooner we know—and fix it—the better. A complete data observability platform extends beyond testing to enables data teams to automatically scale monitoring, alerting, triaging, root cause analysis, impact analysis, and more to ensure the reliability of your data at each stage in the pipeline.
Data observability: the key to reliable data.
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Monte Carlo connects to your existing data stack in minutes, monitoring and alerting to freshness, volume, and schema changes out-of-the-box.
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Monte Carlo equips data teams with rich context about data incidents, including end-to-end field-level lineage for rapid triaging, trouble-shooting, impact analysis, and resolution.
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Monte Carlo generates insights to help you understand what data matters most to your business, where you can cut costs, and how data quality has improved over time.
What if I already…?
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Data catalogs are great for discovery, but weren’t built to manage data quality and reliability. Data observability platforms like Monte Carlo integrate with data catalog providers to offer an advanced layer of visibility into data trust and ensure incidents are identified and fixed.
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Data can break in millions of ways, and relying on testing to keep pace with data quality is a losing game. Data observability automatically scales to meet the needs of your data team with out-of-the-box and custom monitors.
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Traditional data quality solutions are tedious to implement and maintain. Data observability provides automated checks out-of-the-box, end-to-end lineage, and integrations with your existing data engineering workflows to ensure coverage across your entire data environment.
Teams are maximizing the value of their data with Monte Carlo.
“With Monte Carlo, we are able to reinvest the time developers and database analysts would have spent worrying about updates and infrastructure into building exceptional customer experiences.”
Adam Woods CEO