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

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


Frequently asked questions
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.
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.
What makes Sifflet's data catalog more useful for data discovery?
Sifflet's data catalog is enriched with metadata, schema versions, usage stats, and even health status indicators. This makes it easy for users to search, filter, and understand data assets in context. Plus, it integrates seamlessly with your data sources, so you always have the most up-to-date view of your data ecosystem.
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.
What makes Sifflet's architecture unique for secure data pipeline monitoring?
Sifflet uses a cell-based architecture that isolates each customer’s instance and database. This ensures that even under heavy usage or a potential breach, your data pipeline monitoring remains secure, reliable, and unaffected by other customers’ activities.
Why is data observability so important for modern data teams?
Great question! Data observability is essential because it gives teams full visibility into the health of their data pipelines. Without it, small issues can quickly snowball into major incidents, like broken dashboards or faulty machine learning models. At Sifflet, we help you catch problems early with real-time metrics and proactive monitoring, so your team can focus on creating insights, not putting out fires.
What role does data lineage play in incident management and alerting?
Data lineage provides visibility into data dependencies, which helps teams assign, prioritize, and resolve alerts more effectively. In an observability platform like Sifflet, this means faster incident response, better alert correlation, and improved on-call management workflows.
What makes Carrefour’s approach to observability scalable and effective?
Carrefour’s approach combines no-code self-service tools with as-code automation, making it easy for both technical and non-technical users to adopt. This balance, along with incremental implementation and cultural emphasis on data quality, supports scalable observability across the organization.