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Data Observability

Data Observability Platform

High quality, reliable, and trusted data means more productive data teams, happier customers, and greater data adoption.

Monte Carlo, the data observability platform your data team needs

What is data observability?

Your company’s ability to fully understand the health of the data in its systems. Healthy—high-quality, reliable, and trusted—data starts with the ability to monitor and understand the five pillars of data observability at each stage of the pipeline.

  • Freshness
How up-to-date are your data tables, and when was the last time they were updated? Freshness is particularly important when it comes to decision making.
  • Distribution
  • Volume
  • Schema
  • Lineage

Is data monitoring enough?

Short answer is, “no.” Data observability doesn’t end with anomaly detection across a few data assets. Data can break for millions of reasons, and the sooner we know—and fix it—the better. A complete data observability platform enables data teams to:

Know when data breaks, as soon as it happens

Data observability platforms connect to your existing data stack quickly and seamlessly to monitor for freshness, distribution, volume, and schema changes continuously, and immediately notifies stakeholders once data incidents are detected.
Explore Detect

Find the root cause, fast

Data observability platforms equip data teams with the rich context they need, including end-to-end field-level lineage to enable rapid triage and troubleshooting, drastically reducing time to resolution and building trust with stakeholders.
Explore Resolve

Eliminate data downtime and maximize investments

With end-to-end visibility, data observability platforms generate insights that help data teams change and modify tables and schema responsibly, manage storage and compute costs, and make smarter data architecture decisions.
Explore Prevent

What if I already…?

Data catalogs help maintain the availability, usability, provenance, and security of data. But, traditional tools tend to be manual and difficult to maintain given the demands of the modern data stack. Data observability platforms are automated, scale with the growth of your data stack, and federate the meaning of data across domains to enable real-time distributed ownership of data assets. Data observability platforms like Monte Carlo integrate with major data catalog providers, and offer additional layers of visibility, trust, and discoverability.
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Leading teams are maximizing the value of their data stacks with data observability.

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,

Chief Technology Officer

Talk to a data observability expert

Ready to build data trust in your company?

Here are three resources to help you plan your next step

Why now?

Bad data may be costing your team and your company more than you know. Our Data Quality Value Calculator can help you estimate the potential savings by deploying a data observability platform.

Estimate your savings →

Build or buy?

The next big question is if your team should build data observability capabilities in-house, or choose to purchase a data observability platform. There are four key considerations as you make this decision.

Learn more →

What are my decision criteria?

If you’ve decided to buy, your next step is to assemble an evaluation committee and establish requirements to inform which vendor you should select. Here’s a resource to give you a head start.

Get the data observability checklist →