Timeseer.AI: Empowering data teams with quality-augmented data. Why we invested

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Timeseer.AI: Empowering data teams with quality-augmented data. Why we invested

Jan. 25th, 2022

Timeseer.AI: Empowering data teams with quality-augmented data. Why we invested

From an entrepreneurial point of view, Timeseer’s CEO, Bert Baeck, is able to identify and satisfy emerging market needs. At the same time, he uses his VC background to consider the long-term viability of an enterprise. It was this unique combination of experience and industry insight that drove him to create his latest venture, Timeseer.AI.

“Whenever I’m reviewing a company (with my VC hat on of course),” says Bert, “I look for four fundamental elements to evaluate the potential success of a company: timing, team, market size, and product, in that order. For me, Timeseer.AI ticks all these boxes.”

These fundamental elements might sound familiar, as we have often mentioned them as being our own. And in this article, you’ll see why we couldn’t be more aligned and excited about this new investment.

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The timing

Timeseer.AI is a time-series Data Operations (DataOps) software company that aims to automate overall data orchestration throughout an organisation to deliver high-quality, on-demand data to organisational customers.

Despite the ever-increasing focus on AI, machine learning, analytics and data (MAD), and IoT, Bert still found that the main reason for AI project failure was data reliability. It therefore became clear to him that the market desperately needed a high-quality, time-series data reliability and observability platform.

“The timing is perfect,” says Bert, “because organisations that want to stay competitive and succeed in the digital transformation age are shifting to data-driven decision making and hyper automation. And data reliability is crucial to this success. In fact, a lot of prominent investors are making considerable bets in this space. In addition, the time-series data market is currently the fastest-growing industry segment.”

What’s more is that Bert was in a perfect position to come to market early, as he already had a high-quality, experienced team at his disposal from his previous TrendMiner venture.

A team of industry stalwarts

“Our Timeseer.AI team has 80 years+ of cumulative experience in this domain due to our TrendMiner roots,” says Bert. “And, most importantly, we’re a group of passionate, highly-driven, serial entrepreneurs with the know-how and motivation to make this latest venture a success. Simply put, we have the best team to pull it off.”

Besides Bert, the team of co-founders includes Thomas Dhollander, Stijn Meganck, Niels Verheijen, Yorick Bloemen and Jeroen Hoekx. As Chairman, the company boasts Jeroen Van Godtsenhoven, who was the former GM for Europe and the Middle East for C3.AI, and Frederic Hanika, Head of Corporate Development at Quest, is the board’s M&A Advisor.

An exponentially growing market

In his time as both an entrepreneur and venture capitalist, Bert has seen that even impressive-looking solutions may be too optimistic about their product-market fit. In determining the market size, one therefore has to look beyond the size of the industry, to the industry players most likely to derive value from the solution.

Timeseer.AI therefore chose to launch a solution into a big marketplace that continues to grow in tandem with exponentially-growing time-series databases.

And their instincts have been validated, as several Fortune 5000 companies have already signed on to provide Timeseer.AI with the necessary initial market traction.

The product: enabling reliable time-series analytics through improved data quality and observability

There tends to be a big discrepancy between a company’s expectations and the reality of its application of AI and ML to its datasets, mainly due to the unreliability of the input data.

“I saw,” says Bert, “during my VC days, that over 80% of companies that wanted to leverage their data and experiment with AI could not progress past the proof-of-concept stage as a result of the quality of their data. This means that companies spend many hours on creating AI projects that don’t solve problems, making them extremely expensive in man-days. Even where solutions are found, without actionable data, these products may end up not being scalable.”

Timeseer.AI is also positioned to provide a solution to the negative impact of unreliable data on businesses through a four-pronged approach:

1. Data reliability scoring and profiling

The solution uses over 30 built-in quality metrics to express the overall health of the collected time-series data, including variance drift, broken correlations, stale data, and missing values. These metrics are flexible for clients to determine their own custom metrics and quality KPIs.

2. Data monitoring and observability

All incoming data is scanned proactively and at scale. This data is then segmented, and overviews are generated of data downtime issues. Again, users are able to define data SLAs and to define quality gates.

3. Data quality optimisation, augmentation, and cleaning

This process assists clients to make the data as useful as possible to the business by allowing them to define their own augmentation logic. The solution also aligns and transforms data from different series, filters out unwanted artefacts, and keeps data volumes manageable without causing information loss.

4. Data connectivity and uniformisation

Timeseer.AI automates the burden of data integration and maps to a uniform time-series data model. This information is then organised into series sets and asset templates.

In our opinion, it makes perfect sense to focus on time-series data, as this is the fastest-growing database segment. In the past, made-to-measure or special purpose databases were in high demand, but their popularity is waning.

The quality of time-series data for a business cannot be overstated. At manufacturing level, using unreliable data can result in issues from unplanned downtime to lack of governmental compliance, and even to plant explosions.

Or, as Bert explains, “Whereas it would only cost €1 to detect data downtime, it would cost €10 to fix the problem, or cause a €100 financial impact if it hits the operational side of the business.”

Timeseer.AI could mean the difference between a data-driven business’s success or failure.

The future

Bert and his team have made their ambitions in the time-series data space well known and aim to establish and keep Timeseer.AI as the top provider in the market. This vision received a recent boost with the announcement that $6 million in seed capital has been raised from a number of investors, including Fortino.

While the original focus was on industrial manufacturing businesses, the applications of the product are potentially limitless, particularly as companies continue to scale up their AI project expenditures.

Timeseer.AI has positioned itself perfectly to contribute to the success of these projects, and we are grateful to them for choosing us to join them on their journey.

If you’re a Europe-based B2B SaaS company looking for advice and potential investment, we can help. Get in touch, and let’s get the conversation started.

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