Enterprise AI is slowing down due to poor data quality. Gartner points to data quality and availability as the main obstacles to AI adoption. An MIT study found that 95% of AI projects never reach production. This problem persists in regulated industries such as banking.
Stockholm-based Validio tackles the data foundation on which AI relies. Its smart enterprise data management platform helps companies in finance, manufacturing, and telecom automatically monitor data, spot anomalies, and track data lineage across billions of records.
To support its mission, Validio just raised a $30 million Series A funding round led by Plural, with existing investors Lakestar and J12 joining, along with angel investors Kevin Ryan, Denise Persson, and Emil Eifrem.
This brings the company’s total funding to $47 million after an 800% increase in annual recurring revenue last year.
Treat data as a genuine business-critical asset
Patrik Liu Tran launched Validio back in 2019, after years spent advising global giants on AI and data. Again and again, he saw the same story: ambitious AI projects falling flat because the data just wasn’t up to scratch. After watching this play out one too many times, he knew the market needed something better than patchwork tools or DIY fixes that never scale.
Patrick shares with us, “The motivation came from direct experience. Before founding Validio in 2019, 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. I consistently saw that, no matter how ambitious the project was, AI projects rarely reached production.”
“The complexity of ensuring high-quality data in large organisations, with many stakeholders involved, required something more than manual, ad-hoc solutions. 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. With growing volume, velocity, and complexity of data, it’s simply not possible for humans alone to keep up. It convinced me that a solution to fix data quality at scale was the missing piece for enterprises to unlock the value in their data,” Patrick continues.
Validio’s platform provides three main features: automated monitoring, AI anomaly detection, and complete data lineage and cataloguing. It can be set up in minutes, unlike older tools that take months. It works independently across billions of records without requiring big engineering teams to manage fixed rules.
“This means the speed to get Validio up and running can be measured in days, whereas the competitors require several quarters or even years. Due to the high degree of automation in Validio, the FTE count required to operate and manage data is drastically reduced. You need 90% less people to manage data quality with Validio as opposed to traditional manual offerings on the market,” elaborates Patrick.
He adds, “The AI automation also makes detection of data quality issues and issue resolution 95% faster than alternative solutions. This is extremely important, since DQ issues in production do have a big impact if not detected or fixed in a timely manner.”
Customers have seen results. Data problems that used to go unnoticed until month-end reports now show up within minutes, reducing manual checks by up to 95%. In one example, data lineage mapping that took eight months to set up was ready to use in just one day.
What sets Validio apart is its cross-functional design. Unlike traditional data observability tools that primarily serve engineering teams, this platform helps business and technical teams work together to fix problems at their source rather than passing them through separate groups.
While competitors like Monte Carlo, Atlan, and Collibra focus on narrow slices or specific users, Validio offers a unified, intelligent layer for data quality, lineage, and cataloguing: built for AI across the whole enterprise.
What’s next?
After securing Series A funding, Validio is working on expanding its market presence in the US, UK, and Northern Europe. It is improving its smart platform and growing its senior team.
The bigger goal is to become the leading platform for enterprise data reliability, becoming the key infrastructure that sets AI leaders apart from those stuck in endless pilots.
