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What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

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Frequently asked questions

Can Sifflet help with root cause analysis in complex data systems?

Absolutely! In early 2025, we're rolling out advanced root cause analysis tools designed to help you detect subtle anomalies and trace them back to their source. Whether the issue lies in your code, data, or pipelines, our observability platform will help you get to the bottom of it faster.

How can data observability help reduce data entropy?

Data entropy refers to the chaos and disorder in modern data environments. A strong data observability platform helps reduce this by providing real-time metrics, anomaly detection, and data lineage tracking. This gives teams better visibility across their data pipelines and helps them catch issues early before they impact the business.

What practical steps can companies take to build a data-driven culture?

To build a data-driven culture, start by investing in data literacy, aligning goals across teams, and adopting observability tools that support proactive monitoring. Platforms with features like metrics collection, telemetry instrumentation, and real-time alerts can help ensure data reliability and build trust in your analytics.

What are some key features to look for in an observability platform for data?

A strong observability platform should offer data lineage tracking, real-time metrics, anomaly detection, and data freshness checks. It should also integrate with your existing tools like Airflow or Snowflake, and support alerting through Slack or webhook integrations. These capabilities help teams monitor data pipelines effectively and respond quickly to issues.

How does Sifflet ensure a user-friendly experience for data teams?

We prioritize user research and apply UX principles like Jacob’s Law to design familiar and intuitive workflows. This helps reduce friction for users working with tools like our Sifflet Insights plugin, which brings real-time metrics and data quality monitoring directly into BI dashboards like Looker and Tableau.

How does Sifflet help reduce AI bias and improve model fairness?

Reducing AI bias starts with understanding your data. Sifflet’s observability platform gives you deep visibility into data sources, transformations, and quality. By tracking data lineage and applying data profiling, teams can identify and correct biased inputs before they affect model outcomes. This transparency helps build more ethical and reliable AI systems.

What challenges did Hypebeast face when transitioning to full-scale data observability?

One major challenge was shifting the company culture from being data-aware to truly data-driven. Technically, integrating new observability tools into existing infrastructures and managing the initial investment in time and resources also posed hurdles.

How can I measure whether my data is trustworthy?

Great question! To measure data quality, you can track key metrics like accuracy, completeness, consistency, relevance, and freshness. These indicators help you evaluate the health of your data and are often part of a broader data observability strategy that ensures your data is reliable and ready for business use.

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