Solution by Use Case

Monetization of Data

Big Data. %%Big Potential.%%

Sell data products that meet the most demanding standards of data reliability, quality and health.

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Identify Opportunities

Monetizing data starts with identifying your highest potential data sets. Sifflet can highlight patterns in data usage and quality that suggest monetization potential and help you uncover data combinations that could create value.

Deep dive into patterns around data usage to identify high-value data sets through usage analytics
Determine which data assets are most reliable and complete

Ensure Quality and Operational Excellence

It’s not enough to create a data product. Revenue depends on ensuring the highest levels of reliability and quality. Sifflet ensures quality and operational excellence to protect your revenue streams.

Reduce the cost of maintaining your data products through automated monitoring
Prevent and detect data quality issues before customers are impacted
Empower rapid response to issues that could affect data product value
Streamline data delivery and sharing processes

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

Why does great design matter in data observability platforms?

Great design is essential in data observability platforms because it helps users navigate complex workflows with ease and confidence. At Sifflet, we believe that combining intuitive UX with a visually consistent UI empowers Data Engineers and Analysts to monitor data quality, detect anomalies, and ensure SLA compliance more efficiently.

Will Sifflet cover any upcoming trends in data observability?

For sure! Our CEO, Salma Bakouk, will be speaking about the top data trends to watch in 2025, including how GenAI and advanced anomaly detection are shaping the future of observability platforms. You’ll walk away with actionable insights for your data strategy.

How does the new Fivetran integration enhance data observability in Sifflet?

Great question! With our new Fivetran integration, Sifflet now provides visibility into your data's journey even before it reaches your data platform. This means you can track data from its source through Fivetran connectors all the way downstream, offering truly end-to-end data observability.

What does Full Data Stack Observability mean?

Full Data Stack Observability means having complete visibility into every layer of your data pipeline, from ingestion to business intelligence tools. At Sifflet, our observability platform collects signals across your entire stack, enabling anomaly detection, data lineage tracking, and real-time metrics collection. This approach helps teams ensure data reliability and reduce time spent firefighting issues.

What trends are driving the demand for centralized data observability platforms?

The growing complexity of data products, especially with AI and real-time use cases, is driving the need for centralized data observability platforms. These platforms support proactive monitoring, root cause analysis, and incident response automation, making it easier for teams to maintain data reliability and optimize resource utilization.

What new dbt metadata can I now see in Sifflet?

You’ll now find key dbt metadata like the last execution timestamp and status directly within the dataset catalog and asset pages. This makes real-time metrics and pipeline health monitoring more accessible and actionable across your observability platform.

Why is data observability a crucial part of the modern data stack?

Data observability is essential because it ensures data reliability across your entire stack. As data pipelines grow more complex, having visibility into data freshness, quality, and lineage helps prevent issues before they impact the business. Tools like Sifflet offer real-time metrics, anomaly detection, and root cause analysis so teams can stay ahead of data problems and maintain trust in their analytics.

What role do Common Table Expressions (CTEs) play in query optimization?

CTEs help simplify complex queries by breaking them into manageable parts. This boosts readability and performance, making it easier to identify issues during root cause analysis and enhancing your data quality monitoring efforts.

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