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

What role does data lineage tracking play in volume monitoring?

Data lineage tracking is essential for root cause analysis when volume anomalies occur. It helps you trace where data came from and how it's been transformed, so if a volume drop happens, you can quickly identify whether it was caused by a failed API, upstream filter, or schema change. This context is key for effective data pipeline monitoring.

What are some of the latest technologies integrated into Sifflet's observability tools?

We've been exploring and integrating a variety of cutting-edge technologies, including dynamic thresholding for anomaly detection, data profiling tools, and telemetry instrumentation. These tools help enhance our pipeline health dashboard and improve transparency in data pipelines.

Can I learn about real-world results from Sifflet customers at the event?

Yes, definitely! Companies like Saint-Gobain will be sharing how they’ve used Sifflet for data observability, data lineage tracking, and SLA compliance. It’s a great chance to hear how others are solving real data challenges with our platform.

How is data volume different from data variety?

Great question! Data volume is about how much data you're receiving, while data variety refers to the different types and formats of data sources. For example, a sudden drop in appointment data is a volume issue, while a new file format causing schema mismatches is a variety issue. Observability tools help you monitor both dimensions to maintain healthy pipelines.

What’s next for data observability at Sifflet?

We’re focused on solving the next generation of challenges, like hybrid environments, end-to-end data lineage tracking, and scaling data trust. Whether it's batch data observability or real-time pipeline monitoring, our mission is to help organizations build resilient, transparent, and future-proof data stacks.

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.

Why is data observability important when using ETL or ELT tools?

Data observability is crucial no matter which integration method you use. With ETL or ELT, you're moving and transforming data across multiple systems, which can introduce errors or delays. An observability platform like Sifflet helps you track data freshness, detect anomalies, and ensure SLA compliance across your pipelines. This means fewer surprises, faster root cause analysis, and more reliable data for your business teams.

What is data governance and why does it matter for modern businesses?

Data governance is a framework of policies, roles, and processes that ensure data is accurate, secure, and used responsibly across an organization. It brings clarity and accountability to data management, helping teams trust the data they use, stay compliant with regulations, and make confident decisions. When paired with data observability tools, governance ensures data remains reliable and actionable at scale.

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