Data quality is a crucial issue for organizations dealing with large amounts of data, as a whopping 77% of companies have data quality issues, with 91% believing it affects their performance, according to a survey by Great Expectations. Consequently, vendors offering data observation services and software are thriving, helping organizations keep tabs on their data’s health and status.
In 2022, the data observables market raised over $400 million in a single week, with companies like Cribble, Monte Carlo, and Coralologics securing funding. This growth indicates that the market has plenty of room for new players, as evidenced by Sifflet, a data supervision startup that recently raised €12 million (~$12.7 million) in a Series A funding round led by EQT Ventures, with existing investors participating.
Sifflet was founded in June 2021 by Salma Bakouk, a former Goldman Sachs vice president in the sales and trading department. Bakouk joined forces with software engineers Wissem Fatallah (previously at Uber and Amazon) and Wajdi Fatallah, launching an MVP that evolved into a fully-fledged data visualization product.
Sifflet aims to help businesses build trust in their data, providing a data observability platform that sits atop the data stack, offering 360-degree oversight of data assets. Bakouk stated in a TechCrunch interview that the platform is geared towards businesses that want to “detect, prevent and repair data errors” and that it can “quickly and easily scale with your data requirements.”
Sifflet is revolutionizing the way organizations deal with their data quality issues. With 77% of companies having data quality issues and 91% believing it affects their performance, it’s no wonder the data observation space is booming. In fact, Sifflet has secured a €12 million ($12.7 million) investment in a Series A funding round led by EQT Ventures, and the Paris-based startup already has big-name customers, including Carrefour and NextBite.
Sifflet’s AI-powered platform offers 360-degree oversight of data assets, monitoring data, metadata, and data pipelines for any signs of trouble. Its pre-trained models are regularly updated to minimize training bias, and the platform automatically detects drops in data quality. This is especially important in today’s economy, where companies need to rely on data-driven decision-making and can’t tolerate data incidents. In fact, poor data quality costs organizations an average of $12.9 million annually, according to Gartner.
Sifflet is growing fast and has big plans to scale up its go-to-market efforts across the globe. With a laser-sharp product vision, Sifflet aims to double its number of employees by the end of the year and continue investing in product and engineering. So, can Sifflet compete in the crowded data visualization space? Its investors and big-name customers seem to think so.