Anomalo to Speak about Scaling Data Reliability at Snowflake Summit 2022

June 14, 2022

PALO ALTO, Calif., June 14, 2022 -- Anomalo, the complete data quality platform company, today announced that it has a session titled "Scaling Data Reliability – Building an Executive Dashboard for your Data Quality Vitals" at Snowflake’s largest user conference to date - the Snowflake Summit 2022 - taking place June 13-16 in Las Vegas: https://events.snowflake.com/summit/agenda/session/901351?widget=true

Today at 1:30 p.m., Vicky Andonova, manager of applied machine learning at Anomalo, will talk about how to improve data quality by observing:

  • Data quality coverage — evaluate monitoring coverage, define gold standards and pinpoint blind spots.
  • Data arrival times — identify tables whose data doesn’t arrive on time or meet SLAs.
  • Data quality trends — a snapshot statistic is not enough to track data quality and improvements with time-series views and week-over-week changes.
  • Repeat offenders — use the ranked list of repeat offenders to define and prioritize the action items that can improve your data quality the most.

In January of this year, Anomalo announced a partnership with Snowflake to help customers trust the data they use to make decisions and build products. The combination provides customers with a way to monitor the quality of their data in any table in Snowflake’s platform without writing code, configuring rules or setting thresholds. Anomalo will showcase its data quality platform in booth 914 at the Snowflake Summit 2022.

“Snowflake provides an ideal environment for tools like Anomalo. With its ability to centralize the full set of enterprise data and its unique ability to automatically size query workloads based on their priority and urgency, Snowflake is a perfect partner in helping enterprises trust all of their important data,” said Elliot Shmukler, co-founder and CEO of Anomalo.

Anomalo and Snowflake are used by customers globally:

  • Discover Financial Services is leveraging Anomalo to quickly gain trust in their most critical data. Discover’s Chief Data and Analytics Officer Keith Toney said: “Discover is transforming and expanding how we use data as an enterprise asset to serve our customers better through advanced data analytics. We were looking for a product that would help us maintain a scalable foundation of trusted data in a fast-paced digital environment. We selected Anomalo to fully automate the basis of our data quality monitoring because their machine learning and root cause detection technology identifies late, missing or anomalous data across our petabyte-scale cloud warehouse. Our data stewards use Anomalo’s intuitive UI to tailor monitoring to their business needs. Compared to legacy solutions, Anomalo will help us detect more quality issues with just a fraction of the time invested by our team.”
  • Substack uses Anomalo to empower their small team to keep up with an ever growing collection of data. Mike Cohen, Substack’s Data Manager, said: “With a small data team at Substack, the automated checks that Anomalo provides are like having another data engineer on the team whose primary focus is to ensure data quality and integrity. With these checks, we've caught internal data and production bugs and detected the presence of bad actors internal to our system that might have otherwise gone unnoticed for long periods of time.”

About Anomalo

Anomalo helps enterprises build confidence in the data they use to make decisions and build products. Enterprises can simply connect Anomalo’s complete data quality platform to their data warehouse and begin monitoring their data in less than 5 minutes, all with minimal configuration and without a single line of code. Then, they can automatically detect and understand the root-cause of data issues, before anyone else. Anomalo is backed by Norwest Venture Partners, Foundation Capital, Two Sigma Ventures, Village Global and First Round Capital. For more information, visit https://www.anomalo.com/ or follow @anomalo_hq.

Written By
The Anomalo Team
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