Whistle raises funds to expand its data observability platform

Whistle raises funds to expand its data observability platform

Whistle raises funds to expand its data observability platform

Organizations dealing with large amounts of data often struggle to ensure that the data remains of high quality. According to a survey by Great Expectations, which creates open-source tools to test data, 77% of companies have data quality issues and 91% believe it impacts their performance.

In light of this, unsurprisingly, business has been pretty healthy for vendors selling data observability services and software, which help an organization understand the health and condition of their data. Last year, in the space of a week, three companies alone in the field of data observability – 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 has raised €12 million (~$12.7 million) in a Series A funding round led by EQT Ventures with participation from existing investors. .

Whistle was founded in June 2021 by Salma Bakouk, former Goldman Sachs vice president in the sales and trading department. She partnered with software engineers Wissem Fathallah (previously at Uber and Amazon) and Wajdi Fathallah to launch an MVP, which has evolved into a full-fledged data observability product.

“Whistle is a data observability platform aimed at helping companies build trust in their data,” Bakouk told TechCrunch in an email interview. “Its platform sits on top of the data stack, providing 360-degree monitoring of data assets.”

With Whistle, companies can collect insights across different layers of their data stack, from data ingestion stages to transformation and consumption. The platform automatically monitors data, metadata, and data pipelines for evidence that something is wrong, such as a sudden drop in quality.

Whistle maintains a lineage to make it easier for data engineers to perform root cause analysis. As Bakouk explains, AI is at the heart of this process.

“AI is used in our monitoring engines, data classification and context enrichment,” she said. “Our models are pre-trained based on various types of datasets from different industries and dynamics and retrain regularly when deployed to account for the particularities of the customer’s environment and mitigate any training bias. .”

So, given the competition in the data observability space, can Whistle reasonably compete? Its investors clearly believe it is possible. A more objective measure is the size of Whistle’s customer base, but Bakouk wouldn’t disclose that. She said, however, that Whistle counts brands like Carrefour, Nextbite and ShopBack among its current customers.

“Sifflet’s approach is specifically designed to be inclusive of the majority of data practitioners, both technical and non-technical,” Bakouk said. “In today’s economic environment, where businesses are faced with tough decisions, data-driven decision-making is the norm and data-related incidents are simply not tolerated.”

It is difficult to dispute this last point. According to Gartner, poor data quality costs businesses an average of $12.9 million per year. Additionally, data engineers spend two days a week battling bad data, according to a Monte Carlo survey.

“A slowing economy is actually a great catalyst for data adoption. Businesses need to take uncertainty out of the equation when making tough decisions and data reliability is key,” Bakuk said. “When it comes to the company’s position, we value capital efficiency and look for strategic ways to grow. Having a very clear product vision from day one has allowed us to be focused.” and fast on execution and avoid costly pivots.

Paris-based Whistle, which has raised €15m (~$15.85m) to date, plans to ramp up its go-to-market efforts in Europe, the Middle East, Asia and the US and to continue to invest in products and engineering. It currently has 28 employees and aims to more than double that number by the end of the year.


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