Organizations that work with large amounts of data often struggle to maintain high data quality. A study by Great Expectations, which creates open source tools for data testing, found that 77% of companies have data quality issues, and 91% believe it affects their performance. increase.
Given that, not surprisingly, business is very healthy for vendors selling data observability services and software that help organizations understand the health and state of their data. Last year, his three companies in the data observability space alone (Cribl, Monte Carlo, Coralogix) raised over $400 million in one week.
Another data observability startup landed venture capital this week, suggesting the market isn’t oversaturated yet. Sifflet. Today, the company announced it has raised €12 million (approximately $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, former Vice President of Sales and Trading at Goldman Sachs. She teamed up with her software engineer Wissem Fathallah (previously she was at Uber and Amazon) and Wajdi Fathallah to launch MVP. It has grown into a full fledged data observability product.
“Sifflet is a data observability platform aimed at helping businesses build trust in their data,” Bakouk said in an email interview with TechCrunch. “The platform sits on top of the data stack and provides 360-degree oversight of your data assets.”
Sifflet enables enterprises to collect information across various layers of the data stack, from the data ingestion stage to transformation and consumption. The platform automatically monitors your data, metadata, and data pipelines for evidence that something might be wrong, such as a sudden drop in quality.
Sifflet maintains lineage to facilitate root cause analysis for data engineers. As Bakouk explains, AI is central to this process.
“AI is being used in monitoring engines, data classification and contextual enrichment,” she said. “Our models are pre-trained on different types of datasets from different industries and dynamics, and are retrained regularly during deployment to take into account the peculiarities of our customers’ environments and It reduces training bias.”
So given the competition in the data observability space, can Sifflet reasonably compete? Its investors clearly believe it can. A more objective measure is the size of his Sifflet customer base, which Bakouk declined to disclose. But she has volunteered that Sifflet counts brands like her Carrefour, Nextbite and ShopBack among its current customers.
“Sifflet’s approach is specifically built to be inclusive to the majority of data practitioners, both technical and non-technical,” says Bakouk. “In the current economic environment where businesses face difficult decisions, data-driven decisions are the norm and data incidents are unacceptable.”
The last point is difficult to argue with. According to Gartner, poor data quality costs organizations an average of $12.9 million each year. Additionally, according to a Monte Carlo poll, a data engineer spends two days a week dealing with bad data.
“The economic slowdown is actually a big factor driving data adoption. Companies need to remove uncertainty from the equation when making difficult decisions, and data reliability is key,” says Bakouk. said Mr. “Regarding the position of the company, we are focused on capital efficiency and looking for strategic ways to grow. We were able to avoid a costly pivot.”
Paris-based Sifflet, which has raised €15 million (approximately $15.85 million) to date, plans to ramp up its market development efforts in Europe, the Middle East, Asia and the United States, while continuing to invest in products and engineering is. It currently has 28 employees and aims to more than double that number by the end of the year.
