Solution by Persona

Business Teams

Data User

Take control of your decisions. Sifflet gives business users unmatched clarity and trust in their data, driving smarter actions with ease.

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Data Freshness and Reliability

Sifflet gives data users visibility into when data was last updated, and alerts when source data changes unexpectedly, so you’ll always know the status of your numbers.

Self-Service Troubleshooting

Vetting data quality has often been tough. Sifflet makes it easier and simpler to trace unusual values thanks to data lineage, and get historical context of data changes and updates.

Analysis Confidence

You’ll be able to analyze numbers with confidence thanks to knowledge of who owns and maintains different data assets and verify data accuracy before sharing insights.

Superior Insights. Check.

Sifflet makes it easier to gain strategic insights about your market, products, and customers. By ensuring the highest levels of data quality, your teams can make the best possible strategic decisions for your company, unlocking new levels of performance that help you compete in the age of AI.

Never Question Your Numbers Again.

Sifflet gives you the ultimate confidence in your data products and dashboards. By ensuring that your data is monitored and triaged night and day, you can always be sure of the freshness, accuracy, and quality of your numbers.

See Value From Day One.

Sifflet connects to hundreds of tools already in your stack and offers out-of-the-box monitors and tooling so you can start seeing value from day one.

Sifflet’s AI Helps Us Focus on What Moves the Business

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

"Enabler of Cross Platform Data Storytelling"

"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

"Building Harmony Between Data and Business With Sifflet"

"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

" Sifflet empowers our teams through Centralized Data Visibility"

"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 allows us to find and trust our data"

"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

"A core component of our data strategy and transformation"

"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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How Carrefour transformed Data Quality

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Frequently asked questions

What is a 'Trust OS' and how does it relate to data governance?

A Trust OS is an intelligent metadata layer where data contracts are enriched with real-time observability signals. It combines lineage awareness, semantic context, and predictive validation to ensure data reliability at scale. This approach elevates data governance by embedding trust directly into the technical fabric of your data pipelines, not just documentation.

Why is the traditional approach to data observability no longer enough?

Great question! The old playbook for data observability focused heavily on technical infrastructure and treated data like servers — if the pipeline ran and the schema looked fine, the data was assumed to be trustworthy. But today, data is a strategic asset that powers business decisions, AI models, and customer experiences. At Sifflet, we believe modern observability platforms must go beyond uptime and freshness checks to provide context-aware insights that reflect real business impact.

How do Subdomains improve data observability at scale?

Subdomains help scale data observability by letting you organize your domains into a hierarchy that mirrors your org chart. This means teams can manage their own data pipeline monitoring while the platform team maintains strategic oversight. It’s a great way to improve clarity, security, and speed across your observability platform.

How does data observability differ from traditional data quality monitoring?

Great question! While data quality monitoring focuses on detecting when data doesn't meet expected thresholds, data observability goes further. It continuously collects signals like metrics, metadata, and lineage to provide context and root cause analysis when issues arise. Essentially, observability helps you not only detect anomalies but also understand and fix them faster, making it a more proactive and scalable approach.

Why should I care about metadata management in my organization?

Great question! Metadata management helps you understand what data you have, where it comes from, and how it’s being used. It’s a critical part of data governance and plays a huge role in improving data discovery, trust, and overall data reliability. With the right metadata strategy, your team can find the right data faster and make better decisions.

What is Flow Stopper and how does it help with data pipeline monitoring?

Flow Stopper is a powerful feature in Sifflet's observability platform that allows you to pause vulnerable pipelines at the orchestration layer before issues reach production. It helps with proactive data pipeline monitoring by catching anomalies early and preventing downstream damage to your data systems.

How does Sifflet support data documentation in Airflow?

Sifflet centralizes documentation for all your data assets, including DAGs, models, and dashboards. This makes it easier for teams to search, explore dependencies, and maintain strong data governance practices.

What role does metadata play in a data observability platform?

Metadata provides context about your data, such as who created it, when it was modified, and how it's classified. In a data observability platform, strong metadata management enhances data discovery, supports compliance monitoring, and ensures consistent, high-quality data across systems.

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