Validio, a data quality platform based in Sweden, emerges from stealth with $15 million

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Data quality is becoming a prominent and increasingly important part of the data science world: businesses sit on growing amounts of information, but it’s only useful if we can trust its accuracy and usability. To this end, Validio, a startup that builds tools to improve and ensure data quality—specifically with tools that allow users to clean data stored both in data warehouses and elsewhere, as well as in real time—announces a seed round to mark his exit from stealth. The Stockholm-based company has raised $15 million, funds it plans to use for business and product development, research and development, and hiring new talent.

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Lakestar — a London-based venture capital that has previously invested in companies like Facebook and Airbnb but has mainly focused on backing promising startups from Europe (it also backed Skype, Spotify, Revolut and more) — led the round with J12. and several dignitaries are also involved.

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(The list includes soccer player Zlatan Ibrahimovic, Snowflake CMO Deniz Persson, MongoDB co-founder Kevin Ryan, Neo4j co-founder Emil Eifrem, DeepMind Product Manager Mehdi Gissassy, ​​and Kim Fai Kok and Dara Gill of the Angels Collective. Framtid.)

Like many enterprise startups these days, Validio has been using the time since its founding in 2019 to quietly work on its product as well as signing up clients for live rollouts. His clients range from the usual suspects in the big data game to marketing and sales, security companies and business intelligence. Validio doesn’t reveal many names, but does mention a few: Budbee and Babyshop in e-commerce; electric scooter company Voi; and electricity startup Tibber.

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The challenge that Validio has identified and is addressing is one that CEO and co-founder Patrick Liu Tran said he faced early in his working life. A mathematical and computer genius, he graduated from high school at the age of 16 and also sped up his time at university by going to work in 2014/2015 while still a teenager advising companies on artificial intelligence projects. It was still a nascent venture in most places (to be honest, it still exists) and one of the big problems, besides the fact that few in the field were willing to go to companies to work on their problems, was the lack of integrity and quality. at work. the data they tried to use in their machine learning models, he said.

“At every company I’ve consulted, my attention has been drawn to a lack of trust in the data, so strong that people didn’t do much with it, and there were no tools that could really help with that,” he said in an interview. . He added that the first attempts to identify the problem and try to solve it (for example, the Great Expectations open source project created by the people behind superconductivity) were promising, but did not focus so much on real-time information as data in warehouses. .

“But machine learning is in streams, not in the warehouse,” he said.

In addition, they tend to be too dependent on rules that engineers and data scientists must establish, regularly monitor, and tune.

Validio’s approach is to create tools that are not entirely low code. “We build for data engineers. It’s very technical,” Tran said, slightly surprised at my question about it. “But we are focused on a smooth user experience.”

This includes using machine learning and statistical analysis to “train” the user system to more quickly find and respond to pipeline data; sets of rules that are automatically created by an engineer to use or supplement with custom rules; automatic thresholds and auto resolution capabilities and much more.

“We want to make the job of data engineers as easy as possible,” he added.

The company does not have a broader set of rules that it enforces across the platform, but it has created one tailored to individual organizations.

“Data quality” is difficult to define. What is good for one company may be bad for another,” Tran said. “Data is never perfect and companies need to start embracing that as well.” But the list of her investors (including some of those associated with strategic names) is a sign that others may well be singing the same tune with that mindset, and how Validio is concretely working to solve this problem: quality improvement tools data, but built for the real world.

There are several other companies that have identified the data quality market and are working to address it, including the creator of Great Expectations. superconducting, which raised $40 million earlier this year; along with such heavyweights as Microsoft, CACas well as Talend — but for now, Validio’s approach seems right, enough to expand the stakes in a still-young area.

“As data science teams increasingly focus on data quality, we believe Validio has a unique opportunity to become the next major global software player from Europe,” Stephen Nandi, partner at Lakestar, said in a statement. “Validio has built its platform with a unique architecture to manage data quality across data warehouses, lakes and streams on both actual data and real-time metadata. We look forward to supporting the stellar Validio team on their journey to becoming a global data infrastructure leader.”

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