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 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.

What makes Sifflet’s data lineage tracking stand out?

Sifflet offers one of the most advanced data lineage tracking capabilities out there. Think of it like a GPS for your data pipelines—it gives you full traceability, helps identify bottlenecks, and supports better pipeline orchestration visibility. It's a game-changer for data governance and optimization.

How does Sifflet support data quality monitoring for business metrics?

Sifflet uses ML-based data quality monitoring to detect anomalies in business metrics and alert users in real time. This enables both data and business teams to quickly investigate issues, perform root cause analysis, and maintain trust in their data.

What makes business-aware data observability so important?

Business-aware observability bridges the gap between technical issues and real-world outcomes. It’s not just about detecting schema changes or data drift — it’s about understanding how those issues affect KPIs, dashboards, and decisions. At Sifflet, we bring together telemetry instrumentation, data profiling, and business context so teams can prioritize incidents based on impact, not just severity. This empowers everyone, from data engineers to product managers, to trust and act on data with confidence.

How does Full Data Stack Observability help improve data quality at scale?

Full Data Stack Observability gives you end-to-end visibility into your data pipeline, from ingestion to consumption. It enables real-time anomaly detection, root cause analysis, and proactive alerts, helping you catch and resolve issues before they affect your dashboards or reports. It's a game-changer for organizations looking to scale data quality efforts efficiently.

How does Sifflet make data observability more accessible to BI users?

Great question! At Sifflet, we're committed to making data observability insights available right where you work. That’s why we’ve expanded beyond our Chrome extension to integrate directly with popular Data Catalogs like Atlan, Alation, Castor, and Data Galaxy. This means BI users can access real-time metrics and data quality insights without ever leaving their workflow.

How is Sifflet rethinking root cause analysis in data observability?

Root cause analysis is a critical part of data reliability, and we’re making it smarter. Instead of manually sifting through logs or lineage graphs, Sifflet uses AI and metadata to automate root cause detection and suggest next steps. Our observability tools analyze query logs, pipeline dependencies, and usage patterns to surface the 'why' behind incidents — not just the 'what.' That means faster triage, quicker resolution, and fewer surprises downstream.

What should I look for when choosing a data observability platform?

Great question! When evaluating a data observability platform, it’s important to focus on real capabilities like root cause analysis, data lineage tracking, and SLA compliance rather than flashy features. Our checklist helps you cut through the noise so you can find a solution that builds trust and scales with your data needs.

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