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Belgium-based Timeseer.ai, a startup helping enterprises maintain time-series data quality, has emerged from stealth with $6 million in funding led by Crane Venture Partners, with participation from Smartfin Capital, Fortino Capital, LRM, and Innovation Fund. Bert Baeck, the cofounder and CEO of the company, says they plan to use the fresh funds to further develop their platform and expand into new markets.
With the advent of IoT, organizations around the world have become heavily dependent on connected machines and systems to boost their growth and optimize productivity. A mid-sized manufacturing company is estimated to have up to 100,000 sensors, while a refinery has up to five times more, with every single sensor producing a data point every couple of seconds. These data points, indexed in time order, can be described as time-series data. They help track change over time (like output/performance) and enable enterprises to conduct downstream analytics and machine learning to optimize their business.
However, in cases of large-scale operation, such data can also go bad – due to issues ranging from sensor misalignment and drift to battery fault – and end up affecting the whole AI project, without the team knowing. This can trigger unplanned downtime, government compliance issues, and even safety concerns.
Timeseer.ai’s data observability platform
To solve this challenge, Baeck, a former venture capitalist, teamed up with serial entrepreneurs Niels Verheijen, Thomas Dhollander, Stijn Meganck, Jeroen Hoekx, and Yorick Bloemen and started Timeseer.ai in 2020. The startup offers an AI-powered observability platform that uses over 30 quality metrics (built-in and user-defined) to show the overall health of a time-series database and detects issues such as variance drift, broken correlations, stale data, missing values, and anomalies.
“Enterprises (using this solution) can monitor their data proactively over time and see if the quality meets specific SLAs,” Baeck told Venturebeat. He emphasized that the platform detects anomalies in real-time and also brings the ability to optimize data quality by imputing missing values, filtering out unwanted artifacts, and managing the overall volume of information, among other things.
At present, there are multiple companies in the data observability space, including renowned giants such as Monte Carlo, BigEye, and Soda.io. However, Timeseer says that all these players focus primarily on broader relational data (which allows for sorting and querying according to multiple different columns, keys, and indexes), and not more specific time-series data.
The only other company looking at this space is Israel-based Aperio Data, Baeck added.
“It is a difficult data type to handle, and causality plays a role here. Many data quality expressions (metrics) are special purpose-built for time-series and are not relevant for relational data. Additionally, vendors in the data quality space have developed tools that are not fit for the purpose of framing time-series data (which carries artifacts that do not exist elsewhere),” he said in a Medium blog post.
Reduce data incidents by ten times
With this solution, Timeseer claims to reduce the number of time-series data quality incidents hitting operations by up to ten times. The company has already roped in more than a dozen Fortune 5000 companies dealing with operational data from sensors, including players in the manufacturing, chemicals, F&B, and utility space.
“A plastics manufacturer in Europe has 300,000 sensors across 25 manufacturing sites … Due to a human error, a sensor was calibrated wrongly. This led to the sensor sending values that deviated from the truth, leading to the process running suboptimal for months until the issues were detected. Timeseer.AI proved it could have detected these issues proactively, saving significantly in both monetary (off-spec product and energy usage) as in resources (unnecessary root cause analyses),” Baeck said.
Moving ahead, the cofounder expects to get more companies to use its platform and establish Timeseer as a thought leader in the time-series data reliability space. He also plans to advance the platform, making it available in other verticals where time-series data is generated.
“We started with industrial manufacturing because we have the network via our previous company Trendminer. The time-series market is huge, and time-series databases are the fastest-growing segment in the database segment,” he said.
The market for IoT, which is the primary driver of time-series data, is expected to touch $520 billion by 2022. According to Accenture, industrial IoT alone could contribute $14.2 trillion to world output by 2030. The demand for observability solutions to detect data issues is only expected to increase as companies continue to double down AI projects.
A recent report from MIT Sloan School of Management and BCG suggested that businesses are already pumping upwards of $50 billion annually on AI adoption.
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