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.

June 2, 2025
Pantomath and Alation Expand Partnership to Deliver Comprehensive Data Trust

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.

May 8, 2025
Modernizing Healthcare’s Data Backbone: How Franciscan Health is Building a More Reliable Future

Learn how Franciscan Health is building a more reliable future with data.

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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
Data Quality Roles and Responsibilities | The Data Reliability RACI

Learn about who should share ownership and responsibility for data quality and reliability in an organization from Craig Smith, Pantomath's VP of Customer Experience.

Operator
Data Reliability
The Pantomath Data Quality Framework

Data quality and reliability is paramount to the success of modern-day organizations. Data quality issues can lead to significant business disruptions, poor decision-making with bad data, and overall lack of trust in data. Learn about Pantomath's unique approach to data quality.

Builder
Data Reliability
Creating an Enterprise Approach to Data Quality and Data Reliability

Learn how a data leader at a large enterprise created a purpose-built platform for data teams to address bad data through end-to-end data observability and cross-platform pipeline traceability for ultimate data reliability.

Operator
Data Reliability

Automated data operations across your data stack