Organizations dealing with large amounts of data often struggle to ensure that the data remains of high quality. according to a survey According to Great Expectations, which creates open source tools for testing data, 77% of companies have data quality issues and 91% believe it is affecting their performance.
In light of that, unsurprisingly, business has been pretty healthy for vendors who sell data-observing software and services, which help an organization understand the health and state of its data. Last year, in the span of a week, just three companies in the data observation space (Cribl, Monte Carlo, and Coralogix) raised more than $400 million.
Suggesting that the market is not yet oversaturated, another data observability startup secured venture capital this week: Whistle. Today, the company announced that it raised €12 million (~$12.7 million) in a Series A funding round led by EQT Ventures with the participation of existing investors.
Sifflet was founded in June 2021 by Salma Bakouk, a former Goldman Sachs vice president in the sales and trading department. She teamed up with software engineers Wissem Fathallah (formerly at Uber and Amazon) and Wajdi Fathallah to launch an MVP, which evolved into a full data observability product.
“Sifflet is a data observation platform meant to help companies build trust in their data,” Bakouk told TechCrunch in an email interview. “Their platform sits on top of the data stack, providing 360-degree monitoring of data assets.”
With Sifflet, companies can collect information at different layers of their data stack, from the data ingestion stages to transformation and consumption. The platform automatically monitors data, metadata, and data pipelines for evidence that something might be wrong, such as a sudden drop in quality.
Sifflet maintains a lineage of making it easy for data engineers to perform root cause analysis. As Bakouk explains, AI is central to this process.
“AI is used in our monitoring engines, data classification and context enrichment,” he said. “Our models are pre-trained based on various types of data sets from different industries and dynamics, and are regularly re-trained when deployed to account for the particulars of the client environment and mitigate any training bias.”
So given the competition in the data observability space, can Sifflet reasonably compete? His investors clearly believe he can. A more objective measure is the size of Sifflet’s customer base, but Bakouk declined to disclose this. However, he said Sifflet counts brands like Carrefour, Nextbite and ShopBack among its current clients.
“Sifflet’s approach is specifically designed to be inclusive of the majority of data professionals, both technical and non-technical,” Bakouk said. “In today’s economic environment, where businesses are faced with difficult decisions, data-driven decision making is the norm and data incidents are simply not tolerated.”
It’s hard to argue with this last point. According to Gartner, poor data quality cost organizations an average of $12.9 million each year. In addition, data engineers spend two days a week fighting bad data, a survey of Monte Carlo found.
“The slowdown in the economy is actually a huge catalyst for data adoption. Businesses need to take uncertainty out of the equation when making tough decisions and data reliability is key,” Bakouk said. “In the company’s position, we value capital efficiency and look for strategic ways to grow. The fact that we had a laser-sharp vision of the product from day one allowed us to focus and be quick to execute and avoid costly changes.”
Paris-based Sifflet, which has raised €15 million (~$15.85 million) to date, plans to increase its merchandising efforts in Europe, the Middle East, Asia and the US and continue to invest in products and engineering. It currently has 28 employees and is aiming to more than double that number by the end of the year.
