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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

How does Sifflet support real-time metrics and alerting within a data platform?

Sifflet collects and monitors real-time metrics like data freshness, schema changes, and volume anomalies. With dynamic thresholding and real-time alerts via Slack or email, teams can respond quickly and keep their analytics platform running smoothly.

How is Etam using data observability to support its 2025 strategy?

Etam is leveraging data observability as a foundational element of its 2025 data strategy. With Sifflet’s observability platform, the team can monitor data quality, detect issues early, and ensure data reliability, which helps them move faster and with more confidence across the business.

How does Sifflet enhance data governance for my organization?

Sifflet supports data governance by allowing you to classify assets with tags and labels, define business terms in a shared glossary, and track data lineage. These features help ensure consistent definitions and safe handling of sensitive data across your stack.

What role did data observability play in improving Meero's data reliability?

Data observability was key to Meero's success in maintaining reliable data pipelines. By using Sifflet’s observability platform, they could monitor data freshness, schema changes, and volume anomalies, ensuring their data remained trustworthy and accurate for business decision-making.

How does data observability help ensure SLA compliance for data products?

Data observability plays a big role in SLA compliance by continuously monitoring data freshness, quality, and availability. With tools like Sifflet, teams can set alerts and track metrics that align with their SLAs, ensuring data products meet business expectations consistently.

How did Dailymotion use data observability to support their shift to a product-oriented data platform?

Dailymotion embedded data observability into their data ecosystem to ensure trust, reliability, and discoverability across teams. This shift allowed them to move from ad hoc data requests to delivering scalable, analytics-driven data products that empower both engineers and business users.

Can historical data access really boost data consumer confidence?

Absolutely! When data consumers can see historical performance through data observability dashboards, it builds transparency and trust. They’re more likely to rely on your data if they know it’s been consistently accurate and well-maintained over time.

What are some common consequences of bad data?

Bad data can lead to a range of issues including financial losses, poor strategic decisions, compliance risks, and reduced team productivity. Without proper data quality monitoring, companies may struggle with inaccurate reports, failed analytics, and even reputational damage. That’s why having strong data observability tools in place is so critical.

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