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.

Take a Tour

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

Discover more title goes here

Still have a question in mind ?

Frequently asked questions

How does Sifflet help ensure SLA compliance and data reliability?

Sifflet supports SLA compliance by continuously monitoring key data quality metrics and surfacing issues before they impact business decisions. With automated anomaly detection, real-time alerts, and root cause analysis, our observability platform helps teams maintain data reliability and stay ahead of potential SLA breaches.

Can Datadog help with root cause analysis during incidents?

Yes, Datadog is excellent for root cause analysis, especially with its Bits AI SRE feature. This AI-powered assistant automatically investigates incidents by analyzing telemetry data like logs, metrics, and traces, then suggests likely causes and next steps. It’s a major boost for incident response automation and helps reduce mean time to resolution (MTTR).

What does Sifflet's recent $12.8M Series A funding mean for the future of data observability?

Great question! This funding round, led by EQT Ventures, allows us to double down on our mission to make data more reliable and trustworthy. With this investment, we're expanding our data observability platform, enhancing real-time monitoring capabilities, and growing our presence in EMEA and the US.

Why is a centralized Data Catalog important for data reliability and SLA compliance?

A centralized Data Catalog like Sifflet’s plays a key role in ensuring data reliability and SLA compliance by offering visibility into asset health, surfacing incident alerts, and providing real-time metrics. This empowers teams to monitor data pipelines proactively and meet service level expectations more consistently.

Will there be live demonstrations of Sifflet’s observability platform?

Absolutely! Our team will be offering hands-on demos that showcase how our observability tools integrate into your workflows. From real-time metrics to data quality monitoring, you’ll get a full picture of how Sifflet boosts data reliability across your stack.

How does Sifflet help with end-to-end data observability?

Sifflet enhances end-to-end data observability by allowing you to declare any asset in your data stack, including custom applications and scripts. This ensures full visibility into your data pipelines and supports comprehensive data lineage tracking and root cause analysis.

Is Sifflet suitable for non-technical users who want to contribute to data quality?

Yes, and that’s one of the things we’re most excited about! Sifflet empowers non-technical users to define custom monitoring rules and participate in data quality efforts without needing to write dbt code. It’s all part of building a culture of shared responsibility around data governance and observability.

What are the key features to look for in a data observability platform?

When evaluating an observability platform, look for strong data lineage tracking, real-time metrics collection, anomaly detection capabilities, and broad integrations across your data stack. Features like field-level lineage, ease of setup, and user-friendly dashboards can make a big difference too. At Sifflet, we believe observability should empower both technical and business users with the context they need to trust and act on data.

Read all FAQs