Anomalo x Unilever
Ensure trusted, high-quality data with Anomalo—seamlessly integrated into your Unilever infrastructure. As Unilever focuses on operational efficiency, portfolio optimization, and market expansion, data integrity is more critical than ever.
Anomalo’s AI-driven automation detects anomalies, reducing manual oversight and ensuring accurate, analytics-ready data. Our seamless integration with Unilever’s data platforms enhances efficiency, optimizes processes, and supports cost-saving initiatives.
By maintaining data integrity at scale, Anomalo empowers Unilever to make data-driven decisions on brand performance, market trends, and expansion opportunities. With automated anomaly detection, Unilever can proactively address data issues, ensuring confidence in its analytics and strategic initiatives.
Enhance your data operations with AI-powered, scalable, and cost-efficient data quality monitoring—only with Anomalo.
Solution
Out-of-the-Box Data Quality at Scale
Detect issues across your most critical datasets without writing any rules. Anomalo’s ML-powered monitoring surfaces anomalies, freshness gaps, schema changes, and more with minimal configuration.
Built for Your Modern Data Stack
Seamlessly integrate with data platforms like Databricks, Snowflake, and Google BigQuery, as well as data catalogs like Atlan and Alation, so you can monitor data quality directly where you already work.
Easy Procurement on Marketplace
Anomalo is available on both Google Cloud Marketplace and Snowflake Marketplace, making it fast and simple for you to purchase, deploy, and apply credits toward committed cloud spend.
Automated Root Cause Analysis
Instantly understand why a data issue occurred with automated root cause insights that help your engineering and data teams fix problems faster.
Designed for the Entire Data Team
Anomalo empowers data stewards, analysts, and engineers with customizable monitors, alerting, and governance workflows, so quality doesn’t rely on a single team.
Financial-Grade Precision
Anomalo helps Fortune 500 financial services leaders catch revenue-impacting issues like missing transactions, reconciliation mismatches, and delayed data feeds—before they impact downstream reports or models.