Data quality automation startup Validio raises $30M – News

Stockholm-based startup Validio AB said today it has raised $30 million in funding to tackle one of the biggest problems holding back enterprises’ artificial intelligence projects: fixing the data foundation they’re built on.

Today’s round was led by Plural and saw participation from existing investors Lakestar and J12, along with angel investors Kevin Ryan, Denise Persson and Emil Eifrem. The round brings Validio’s total amount raised to $47 million. It comes after it saw an 800% increase in its annual recurring revenue last year.

The startup has built an intelligent data management platform that helps companies in industries such as financial services, manufacturing and telecommunications to monitor the health of their data, spot anomalies and track its lineage across billions of records. Chief Executive Patrik Liu Tran (pictured) told Tech Funding News that he founded the company back in 2019, having previously spent years advising some of the world’s biggest companies on how to implement the data pipelines and databases needed to power their artificial intelligence projects. “I advised leading banks and large enterprises on their AI and data strategies and saw firsthand the problem of data quality and the lack of a unified solution to solve it,” Tran said. “I consistently saw that, no matter how ambitious the project was, AI projects rarely reached production.”

According to Tran, he kept seeing the same problems repeat again and again: Ambitious projects would fail because the data was bad. Having seen it play out once too many times, he decided that he needed to do something to fix the problem once and for all.

What he realized is that the industry needed something better than the existing, largely manual ad-hoc solutions being used to try to solve brittle data challenges. “In many companies, the reality of managing data quality is writing tens of thousands of checks and manually maintaining them as data changes over time,” he explained.

Of course, taking on such enormous manual workloads isn’t feasible in the age of AI, which feeds on enormous volumes of information. That’s why Tran decided to turn to AI itself to fix the problem, through automation.

One thing that differentiates Validio’s platform is its versatility. Unlike traditional data observability tools like Monte Carlo and Collibra, which primarily serve data engineers, it helps business users and technical teams work together to identify and fix data-related issues at their source. It provides a unified, intelligent layer for monitoring data quality, lineage and cataloging that anyone can work with.

Tran said the platform only takes a few minutes to set up, and once it’s up and running, it will work independently across billions of data records, without any need for engineering teams to set up and manage fixed rules. “Due to the high degree of automation in Validio, the full-time employee count required to operate and manage data is drastically reduced,” he said. “You need 90% less people to manage data quality with Validio as opposed to traditional manual offerings.”

He claimed that Validio is also 95% faster at detecting data quality issues and resolving them than alternative solutions. That explains why customer adoption of the platform has surged, he said. Data quality issues that previously went unnoticed for months on end typically show up within minutes of the problem arising, reducing the manual checks involved by up to 95%.

With cash in the company coffers, Validio is now looking at expanding its platform aggressively, targeting customers in the U.S., the U.K. and the rest of Europe. At the same time, it’s also going to expand its engineering, sales and go-to-market teams. Ultimately, it wants to become the industry’s top platform for data reliability and establish itself as a key part of the infrastructure needed to power enterprise AI projects.

Photo: Validio