After adopting Sifflet’s observability platform, Hypebeast saw a 204% improvement in data quality, a 178% increase in data product delivery, and a 75% boost in ad hoc request speed. These gains translated into faster, more reliable insights and better collaboration across departments.

Carrefour’s approach combines no-code self-service tools with as-code automation, making it easy for both technical and non-technical users to adopt. This balance, along with incremental implementation and cultural emphasis on data quality, supports scalable observability across the organization.

Great question! With the launch of Data-Quality-as-Code v2, Sifflet has made it much easier to create and manage monitors at scale. Whether you prefer working programmatically or through the UI, our platform now offers smoother workflows and standardized threshold settings for more intuitive data quality monitoring.

Yes, and that’s one of the things we’re most excited about! Sifflet empowers non-technical users to define custom monitoring rules and participate in data quality efforts without needing to write dbt code. It’s all part of building a culture of shared responsibility around data governance and observability.

Unstructured data, like text, images, and audio, is growing rapidly due to AI adoption and IoT expansion. This makes data quality monitoring more complex but also more essential. Tools that can profile and validate unstructured data are key to maintaining high-quality datasets for both traditional and AI-driven applications.

Data observability is crucial because it ensures your data is reliable, trustworthy, and ready for decision-making. It sits at the top of the modern data stack and helps teams detect issues like data drift, schema changes, or freshness problems before they impact downstream analytics. A strong observability platform like Sifflet gives you peace of mind and helps maintain data quality across all layers.

Great question! AI and ML systems rely on massive volumes of data to make decisions, and any flaw in that data gets amplified at scale. Data reliability ensures that your models are trained and operate on accurate, complete, and timely data. Without it, you risk cascading failures, poor predictions, and even regulatory issues. That’s why data observability is essential to proactively monitor and maintain reliability across your pipelines.

Our 2025 vision is all about pushing the boundaries of cloud data observability. We're focusing on deeper automation, AI-driven insights, and expanding our observability platform to cover everything from real-time metrics to predictive analytics monitoring. It's about making data operations more resilient, transparent, and scalable.