What is a data operations center?
Learn More
What is a data operations center?
Learn More
Our Blog
.png)
When no one owns Data Operations end to end, incidents repeat and trust erodes. See why standardizing Data Operations is critical for reliable, on-time data.
Learn about the data lineage challenges and how data pipeline traceability can provide a complete understanding of the data's journey to improve quality and reliability.
Here’s the uncomfortable truth: the way most organizations manage data incidents in ServiceNow is broken. Pantomath's Somesh Saxena gives his thoughts.
.png)
As part of this investment, Pantomath is deepening its integration with the Snowflake AI Data Cloud. Learn more...
In this Digital CxO Insights video interview, Mike Vizard talks to Pantomath CEO Somesh Saxena about why data observability is now an essential requirement for driving digital business transformation in the age of artificial intelligence (AI).
Pantomath CEO, Somesh Saxena, was interviewed by Authority Magazine as part of their “How To Effectively Leverage Data To Take Your Company To The Next Level” series. Read about his insights and take on the topic.
The World’s Leading Companies Trust Pantomath
CIOs and CDOs are blindsided by massive hidden costs in their data investments — and they’re not what you'd expect.
Funding accelerates the company’s mission to eliminate manual data operations by combining real-time monitoring, traceability and agentic AI to boost enterprise data reliability at scale.
Automated data operations are far more than just a way to boost efficiency. They're the very foundation for harnessing the power of AI and the journey doesn't start with tools and agents, it starts with giving agents access to reliable, trustworthy data – 100% of the time.
The partnership between Alation and Pantomath now integrates native metadata-driven data quality capabilities with end-to-end pipeline observability, creating a foundation for trusted data ecosystems.
This collaboration builds on the newly launched Alation Data Quality (DQ) Agent, further strengthening the ability of enterprises to govern, observe, and trust their data across the entire ecosystem.
While much attention focuses on data quality issues and pipeline failures, the simple failure of a job to start can trigger catastrophic downstream effects.
