Solution by Use Case

Democratize Data Quality

Make data quality everyone’s business

Enable real-time accessibility to data quality metrics throughout the entire organization, catering to both technical and non-technical users.

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Streamlined monitoring experience

Collect assets spanning the entire data lifecycle thanks to built-in integrations
Enable non technical users to create business-informed monitors thanks to an intuitive UI and to the Sifflet AI Assistant

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Improved information accessibility

Access assets’ health status through the Data Catalog and lineage for de-risked data self-service
Get notified of upstream incidents directly on BI tools via a browser extension

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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Frequently asked questions

What role does data observability play in preventing freshness incidents?

Data observability gives you the visibility to detect freshness problems before they impact the business. By combining metrics like data age, expected vs. actual arrival time, and pipeline health dashboards, observability tools help teams catch delays early, trace where things broke down, and maintain trust in real-time metrics.

Can Sifflet help with root cause analysis when data issues arise?

Absolutely! Sifflet’s field-level data lineage tracking lets you trace data issues from BI dashboards all the way back to source systems. Its AI agent, Sage, even recalls past incidents to suggest likely causes, making root cause analysis faster and more accurate for data engineers and analysts alike.

How does the new Custom Metadata feature improve data governance?

With Custom Metadata, you can tag any asset, monitor, or domain in Sifflet using flexible key-value pairs. This makes it easier to organize and route data based on your internal logic, whether it's ownership, SLA compliance, or business unit. It's a big step forward for data governance and helps teams surface high-priority monitors more effectively.

How did jobvalley improve data visibility across their teams?

jobvalley enhanced data visibility by implementing Sifflet’s observability platform, which included a powerful data catalog. This centralized hub made it easier for teams to discover and access the data they needed, fostering better collaboration and transparency across departments.

What are some common reasons data freshness breaks down in a pipeline?

Freshness issues often start with delays in source systems, ingestion bottlenecks, slow transformation jobs, or even caching problems in dashboards. That's why a strong observability platform needs to monitor every stage of the pipeline, from ingestion latency to delivery, to ensure data reliability and timely decision-making.

What kinds of data does Shippeo monitor to support real-time metrics?

Shippeo tracks critical operational data like order volume, GPS positions, and platform activity. With Sifflet, they monitor ingestion latency and data freshness to ensure that metrics powering dashboards and customer reports are always up to date.

Why is integration with my existing tools important for observability?

A good observability platform should fit right into your current stack. That means supporting tools like dbt, Airflow, and your cloud infrastructure. Seamless integration ensures better pipeline orchestration visibility and makes it easier to act on data issues without disrupting your workflows.

How does data observability differ from traditional data quality monitoring?

Great question! Traditional data quality monitoring focuses on pre-defined rules and tests, but it often falls short when unexpected issues arise. Data observability, on the other hand, provides end-to-end visibility using telemetry instrumentation like metrics, metadata, and lineage. This makes it possible to detect anomalies in real time and troubleshoot issues faster, even in complex data environments.

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