Tamr, the only AI-native master data management (MDM) solution, today announced results for one of its strongest years to date — reporting performance for the fiscal year ended Jan. 31, 2026. The company:
- Delivered 102% year-over-year growth in direct SaaS revenue, driven by expanding enterprise adoption and a significant increase in average contract value (ACV).
- Achieved 97% gross revenue retention and 109% net revenue retention, reflecting strong renewal rates and deeper customer investment.
- Grew its SaaS customer base by 49%, with new customer additions up 70% year-over-year.
“Across industries, organizations are moving beyond AI experimentation, but AI in production is a different game than AI in a pilot,” said Anthony Deighton, Tamr CEO. “To get the most value from AI systems, the data behind them has to be unified, accurate and always up-to-date. Our record growth shows the power of our AI-native approach to data mastering — helping companies move beyond rigid, rules-based approaches to build trusted, connected foundations that keep up with the demands of their business.”
In FY26, Tamr processed billions of records across customer environments globally, reflecting the scale at which organizations rely on the platform to power critical systems and AI initiatives. In addition, API web requests increased nearly 3x year-over-year, as more and more enterprise systems count on Tamr for operational needs, retrieving and updating trusted entity data in real time.
Tamr in Action
EIDR (Entertainment Identifier Registry) — a nonprofit association that provides universal identifiers for millions of films, television episodes, and other audiovisual content across the global entertainment industry — recently selected Tamr to modernize how it verifies and deduplicates content records.
“We assign unique identifiers so content can be shared and reconciled across studios, streamers, and distributors worldwide, making automation and accuracy foundational to our mission,” said Hollie Choi, managing director of EIDR. “Given the variability of incoming metadata and the global footprint of the companies we support, thoughtful automation is essential. By adopting Tamr’s AI and machine learning technology, we are modernizing our data mastering approach, reducing human intervention, and increasing precision across large and complex datasets. Early results demonstrate meaningful improvements in efficiency and data quality, which will allow EIDR to grow without compromising reliability.”
