Sifflet raises cash to expand its data observability platform

Organizations dealing with large amounts of data often struggle to ensure that data remains high-quality. According to a survey from Great Expectations, which creates open source tools for data testing, 77% of companies have data quality issues and 91% believe that it’s impacting their performance.

In light of that, unsurprisingly, business has been quite healthy for vendors that sell data observability services and software, which help an organization understand the health and state of their data. Last year, in the span of one week, three companies alone in the data observability space — Cribl, Monte Carlo and Coralogix — raised more than $400 million.

Suggesting that the market isn’t oversaturated yet, another data observability startup secured venture capital this week: Sifflet. Today the company announced that it raised €12 million (~$12.7 million) in a Series A funding round led by EQT Ventures with participation from existing investors.

Sifflet was founded in June 2021 by Salma Bakouk, an ex-Goldman Sachs VP in the sales and trading department. She teamed up with software engineers Wissem Fathallah (previously at Uber and Amazon) and Wajdi Fathallah to launch an MVP, which grew into a fully fledged data observability product.

“Sifflet is a data observability platform aimed at helping businesses build trust in their data,” Bakouk told TechCrunch in an email interview. “Its platform sits above the data stack, providing a 360-degree oversight of the data assets.”

Using Sifflet, companies can collect information across 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 amiss, like a sudden drop in quality.

Sifflet maintains a lineage to make it easier for data engineers to conduct root cause analyses. As Bakouk explains, AI is central to this process.

“AI is used in our monitoring engines, data classification and context enrichment,” she said. “Our models are pre-trained based on diverse types of data sets from different industries and dynamics and re-train 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 Sifflet reasonably compete? Its investors clearly believe that it can. A more objective measure is the size of Sifflet’s customer base, but Bakouk wouldn’t disclose this. She did volunteer, however, that Sifflet counts brands like Carrefour, Nextbite and ShopBack among its current clients.

“Sifflet’s approach is specifically built to be inclusive toward the majority of data practitioners, both technical and non-technical,” Bakouk said. “In the current economic environment, where companies 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 that last point. According to Gartner, poor data quality costs organizations an average of $12.9 million every year. Moreover, data engineers spend two days per week firefighting bad data, a poll from Monte Carlo found.

“The slowdown in the economy is actually a great catalyst to data adoption. Companies have to remove uncertainty from the equation when making difficult decisions and data reliability is key,” Bakouk said. “On company position, we value capital efficiency and look for strategic ways to grow. The fact that we had a laser-sharp product vision from day one allowed us to be focused and quick on execution and avoid costly pivots.”

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

Sifflet raises cash to expand its data observability platform by Kyle Wiggers originally published on TechCrunch