Anomalo has expanded its platform that monitors the quality of structured data in data warehouses and data lakes to monitor unstructured text. Anomalo’s unstructured capability makes it possible for enterprises to discover, curate, leverage and ingest high volumes of text data without the risk of using low quality data, which is especially critical for Generative AI (GenAI) applications. This new feature is currently in private beta.

With Anomalo’s new unstructured capability, unstructured text documents can be curated and evaluated for data quality around various document and document collection characteristics, including document length, duplicates, topics, tone, language, abusive language, PII and sentiment. Users are able to quickly evaluate the quality of a document collection and identify issues in individual documents, dramatically reducing the time needed to curate, profile and leverage high-value unstructured text data.

Elliot Shmukler, co-founder and CEO of Anomalo, said: “It’s been well known that higher quality data leads to better data products, including traditional dashboards and machine learning models. The same is true in the world of Generative AI, where the quality of the text used to fine-tune or prompt the model via RAG could be the difference between a high performing application and one that is at best underwhelming and at worst, a privacy and compliance risk. We’re supporting data teams in using high quality data for all of their critical initiates and with our new unstructured text monitoring capability, to support their Generative AI efforts as well.”

Anomalo’s new unstructured text capability expands its robust platform that uses AI to automatically detect data issues and understand their root-causes before anyone else, allowing teams to resolve any hiccups with their data before making decisions, running operations or powering models.