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

How do I ensure SLA compliance during a cloud migration?

Ensuring SLA compliance means keeping a close eye on metrics like throughput, resource utilization, and error rates. A robust observability platform can help you track these metrics in real time, so you stay within your service level objectives and keep stakeholders confident.

What makes Sifflet's approach to data quality unique?

At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.

What role does data lineage tracking play in managing complex dbt pipelines?

Data lineage tracking is essential when your dbt projects grow in size and complexity. Sifflet provides a unified, metadata-rich lineage graph that spans your entire data stack, helping you quickly perform root cause analysis and impact assessments. This visibility is crucial for maintaining trust and transparency in your data pipelines.

What is data lineage and why does it matter for modern data teams?

Data lineage is the process of mapping the journey of data from its origin to its final destination, including all the transformations it undergoes. It's essential for data pipeline monitoring and root cause analysis because it helps teams quickly identify where data issues originate, saving time and reducing stress under pressure.

What makes Sifflet’s Data Catalog different from built-in catalogs like Snowsight or Unity Catalog?

Unlike tool-specific catalogs, Sifflet serves as a 'Catalog of Catalogs.' It brings together metadata from across your entire data ecosystem, providing a single source of truth for data lineage tracking, asset discovery, and SLA compliance.

How does a data catalog improve data reliability and governance?

A well-managed data catalog enhances data reliability by capturing metadata like data lineage, ownership, and quality indicators. It supports data governance by enforcing access controls and documenting compliance requirements, making it easier to meet regulatory standards and ensure trustworthy analytics across the organization.

What are the main differences between ETL and ELT for data integration?

ETL (Extract, Transform, Load) transforms data before storing it, while ELT (Extract, Load, Transform) loads raw data first, then transforms it. With modern cloud storage, ELT is often preferred for its flexibility and scalability. Whichever method you choose, pairing it with strong data pipeline monitoring ensures smooth operations.

How does Sifflet support diversity and innovation in the data observability space?

Diversity and innovation are core values at Sifflet. We believe that a diverse team brings a wider range of perspectives, which leads to more creative solutions in areas like cloud data observability and predictive analytics monitoring. Our culture encourages experimentation and continuous learning, making it a great place to grow.

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