Data observability platform emerges from stealth with $6M

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Belgium-based, a startup that helps enterprises maintain time-series data quality, has emerged from stealth with ફ 6 million in funding from Crane Venture Partners in partnership with Smartfin Capital, Fortino Capital, LRM and Innovation Fund. Bert Beck, co-founder and CEO of the company, says they plan to use the new funds to further develop their platform and expand into new markets.

With the advent of IoT, organizations around the world have become increasingly dependent on connected machines and systems to accelerate their growth and optimize productivity. The medium-sized manufacturing company is estimated to have about 100,000 sensors, while the refinery has five times more, with each sensor generating data points every two seconds. These data points can be described as time-series data, indexed in chronological order. They help track change over time (such as output / performance) and enable enterprises to use downstream analytics and machine learning to optimize their business.

However, in the case of large-scale operations, such data can also be bad – due to problems ranging from sensor misalignment and drift to battery fault – and affect the entire AI project without the team knowing. This can trigger unplanned downtime, government compliance issues and safety concerns.’s Data Observability Platform

To address this challenge, Beck teamed up with former venture capitalists, serial entrepreneurs Niels Verheijen, Thomas Dholander, Stegen Megank, Jeroen Hoax, and Yorik Bloman, and launched in 2020. Startups offer AI-powered observation platforms. Uses over 30 quality metrics (built-in and user-defined) to show the overall health of a time-series database and detects problems such as deviation drift, broken correlations, stale data, missing values, and discrepancies.

“Businesses (using this solution) can actively monitor their data over time and see if the quality meets certain SLAs,” Beck told VentureBeat. He emphasized that the platform detects discrepancies in real-time and, among other things, the ability to optimize data quality by imposing missing values, filtering out unwanted artifacts and managing the overall volume of information.

Currently, there are multiple companies in the data observability space, including famous giants such as Monte Carlo, BigEye and However, Timesseer says that all of these players focus primarily on broad relational data (which allows sorting and querying according to multiple different columns, keys, and indexes), and not on more precise time-series data.

Beck adds that the only other company looking at the space is Israel-based Aperio Data.

“It’s a difficult data type to handle, and causation plays a role here. Many data quality expressions (metrics) are built specifically for time-series and are not related to relational data. Furthermore, vendors in the data quality space have developed such tools. Which is not suitable for the purpose of creating time-series data (which carries artifacts that do not exist elsewhere), “he said in a media blog post.

Decrease data events ten times

Timesir Data Observability Platform

Above: How Timesir’s data observability solution fits into the enterprise tech stack.

With this solution, Timesir claims that the number of time-series data quality events will be reduced tenfold. The company has already worked with more than a dozen Fortune 5000 companies dealing with operational data from sensors, including players from the manufacturing, chemicals, F&B and utility space.

“A plastic manufacturer in Europe has 300,000 sensors at 25 production sites … due to a human error, the sensors were incorrectly calibrated. TimeSer.AI has proven that it can actively detect these issues, saving significant resources (unnecessary root cause analysis) as well as resources in both financial (off-spec product and energy consumption), “Beck said. Said.

The way forward

Going forward, Cofounder expects more companies to use its platform and establish Timesier as a thoughtful leader in the time-series data reliability space. It also plans to move the platform forward, making it available in other verticals where time-series data is generated.

“We started with industrial production because we have a network through our previous company Trendminer. The time-segment market is huge, and the time-segment database is the fastest growing segment in the database segment, ”he said.

The market for IoT, the primary driver of time-series data, is expected to touch $ 520 billion by 2022. According to Accenture, industrial IoT could contribute $ 14.2 trillion to world production by 2030. Problems are expected to increase as companies continue to double the AI ​​project, demanding observable solutions for finding data.

A recent report from the MIT Sloan School of Management and BCG suggests that businesses are already pumping in over $ 50 billion annually to adopt AI.


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