Our Blog

October 15, 2025
95% of AI Pilots Fail, and Bad Data is the Culprit

Shashank Saxena, Pantomath CEO, unpacks what’s really behind AI’s 95% failure rate — and why it has nothing to do with model accuracy.

July 3, 2024
Pantomath named in Gartner 2024 Guide for Data Observability

Gartner has released it's Market Guide for Data Observability Tools for 2024

June 13, 2024
Startups Are Crucial to a Successful Cincinnati Future

Cincinnati's startup scene is exploding, with a healthy year-over-year growth in new businesses. This surge is fueled by a unique blend of factors – a seasoned workforce from Fortune 500 companies, a vibrant entrepreneurial spirit, and a city actively fostering innovation.

The World’s Leading Companies Trust Pantomath

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Data in Motion Series, Part 1: What is Data in Motion?

Traditional platforms treat data as a static resource which no longer works for today's data demands. Learn more about the importance of monitoring BOTH data at rest and data in motion.

Builder
Observability & Traceability
Why Data Observability is Essential for Generative AI (GenAI)

Discover the real-world benefits and how companies can optimize GenAI applications by implementing robust data observability practices.

Strategist
AI Readiness
Why Most Data Observability Platforms Fall Short

In boardrooms across the globe, executives are grappling with a painful truth: the data tools they've invested in aren't delivering on their promises. Learn why in this blog.

Strategist
Observability & Traceability
The 5 Most Common Data Management Pitfalls

There are five common mistakes that teams often make on their journey toward data observability. Learn how to avoid them!

Operator
Incident Management
10 Essential Metrics for Effective Data Observability

You can’t simply implement data observability and then hope for the best. Learn about the top 10 essential metrics to make your business thrive.

Builder
Observability & Traceability
The Evolution of Data Quality: Why Data Observability is Key

Data quality has a long history, dating back at least to mainframes in the 1960s. Learn about the path Data Quality has taken, and where it's headed.

Strategist
Data Reliability

Automated data operations across your data stack