As companies increasingly leverage data-driven insights to drive innovation and maintain their competitive edge, it’s important that this data is accurate and reliable. With Monte Carlo and Snowflake’s strategic partnership, teams can finally trust their data through end-to-end Data Observability and automated lineage of their entire data ecosystem, all the way down to field-level values. 

Has your CEO ever told you that the numbers in a report you showed her looked way off?

Has a customer ever called out errors in your product’s dashboards? 

What about a rise in a field’s null rate that went unnoticed for days or weeks because there wasn’t a noticeable change in your KPIs?

If you answered yes to any of these questions, you’re not alone. Data downtime, in other words, periods of time when data is missing, inaccurate, or otherwise erroneous, is an all-too-familiar reality for even the best data teams, costing them millions of dollars in wasted revenue and up to 50 percent of the data engineering team’s time that could otherwise be spent moving the needle for the business. 

Image courtesy of Monte Carlo.

To help companies trust their data, Monte Carlo is excited to announce our partnership with Snowflake by bringing our automated Data Observability Platform to the Data Cloud. Data teams relying on Snowflake to derive critical insights about their business can now leverage the power of automated anomaly detection, monitoring, and end-to-end lineage to prevent bad data from affecting downstream consumers.

Achieving reliable Snowflake pipelines with data observability

The Snowflake – Monte Carlo integration makes it easy for data organizations to identify, resolve, and prevent data downtime and data quality issues before they impact the business, giving companies confidence in the reliability of their data and the products they power. 

Image courtesy of Monte Carlo.

With this partnership, Snowflake customers can now: 

  • Achieve end-to-end Data Observability without writing any code. Get end-to-end data observability for your Snowflake data pipelines with a 20-minute, no-code implementation process. Access out-of-the-box visibility into data Freshness, Volume, Distribution, Schema, and Lineage just by plugging Monte Carlo into your Data Cloud.
  • Know when data breaks, as soon as it happens. Monte Carlo continuously monitors your Snowflake assets and proactively alerts stakeholders to data issues. Monte Carlo’s machine learning-first approach gives data teams broad coverage for common data issues with minimal configuration, and business-context-specific checks layered on top ensure you’re covered at each stage of your data pipeline.
  • Find the root cause of data quality issues, fast. Monte Carlo gives teams a single pane of glass to investigate data issues, drastically reducing time to resolution. By bringing all information and context for your pipelines into one place, teams spend less time firefighting data issues and more time improving the business.
  • Immediately understand the business impact of bad data. End-to-end lineage for your pipelines from the point it enters Snowflake (or further upstream!) down to the analytics and business intelligence layer, data teams are able to assess the business impact of their data issues, ultimately preventing decision making based on bad data.
  • Prevent data downtime. Give your teams full visibility into your Snowflake pipelines and how they impact downstream reports and dashboards to make more informed development decisions. With Monte Carlo, teams can better manage breaking changes to ELTs and BI assets by knowing what will be impacted and who to notify.

In addition to supporting existing mutual customers, Monte Carlo provides end-to-end, automated coverage for teams migrating from their legacy stacks to the Snowflake Data Cloud Platform. Moreover, Monte Carlo’s security-first approach to Data Observability ensures that data never leaves your Snowflake environment.

What Monte Carlo and Snowflake customers have to say

The Zebra

The Zebra, a leading insurance comparison site, makes it easy for consumers across the United States to compare and save on car and home insurance quotes. The company provides over 1,800 car insurance products from more than 200 national insurers, a monumental feat that relies on real-time decision making powered by data from disparate third-party vendors and partners. The Zebra’s data team uses Snowflake and Monte Carlo to ensure that their data pipelines are performant and reliable. 

“As a data leader, there’s nothing worse than not being able to trust your data,” said Shea Spalding, Head of Data Governance at The Zebra. “The combination of Snowflake’s data cloud and Monte Carlo’s data observability platform helps ensure that our data is accessible and fresh at each stage of the data life cycle. With this stack, my team can better collaborate to resolve issues, work towards data trust, and build better products for our customers.” 

Mindbody

As the world’s leading wellness services marketplace, Mindbody is a company with data at heart. Alex Soria, VP of Data & Analytics, is leading the charge with a team of over 25 data scientists, business intelligence analysts, and data engineers responsible for ensuring that the insights powering their product is fresh and reliable.

As the world’s leading wellness services marketplace, Mindbody is a company with data at heart. Alex Soria, VP of Data & Analytics, is leading the charge with a team of over 25 data scientists, business intelligence analysts, and data engineers responsible for ensuring that the insights powering their product is fresh and reliable.

“Data-driven decision making is a huge priority for Mindbody, but our analytics are only as reliable as the data that informs them,” said Alex. “We are working with Monte Carlo to monitor and alert for abnormalities in our Snowflake pipelines, such as null values and duplicate data, that would otherwise go unnoticed until it’s too late. By partnering with Snowflake and Monte Carlo, my team can achieve more powerful analytics and full data reliability.” 

Collaborative Imaging

Collaborative Imaging is a radiologist-owned alliance dedicated to improving the lives of physicians and patients everywhere through technology. With over 18,000 patients seen per day and nearly 1M radiological exams read per month, the alliance is charting a course for the future of this industry by giving providers the solutions and processes necessary to better serve their communities. 

To power their business, Collaborative Imaging needs fast analytics powered by clean and reliable data, which is no easy feat given the size and scope of their distributed data ecosystem. To gain greater visibility into the health of their data at each stage of its life cycle, they needed a way to understand how data flows into their data warehouse, as well as be alerted when changes occur in schema or distribution of the many data sources which are ingested into their Snowflake data warehouse. Monte Carlo empowered them to troubleshoot and resolve issues before downstream data consumers are impacted.

“Collaborative Imaging’s analytics platform helps physicians and patients connect the dots between disparate data points in their healthcare journey, but our insights are only as reliable as the data feeding our system. Dirty data is a landmark of healthcare analytics, and a lot of the work we do revolves around cleaning and making sense of this data to put it into one repository,” said Jacob. “Combined with Snowflake’s powerful Data Cloud and Monte Carlo’s Data Observability Platform, we can resolve these problems before they reach the business. My executives are happy and I can trust our data.”

Want to learn more about how data observability can help you trust your data? Reach out to Monte Carlo and check out the Data Downtime Blog.