Solution by Persona

Business Teams

Data User

Take control of your decisions. Sifflet gives business users unmatched clarity and trust in their data, driving smarter actions with ease.

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Data Freshness and Reliability

Sifflet gives data users visibility into when data was last updated, and alerts when source data changes unexpectedly, so you’ll always know the status of your numbers.

Self-Service Troubleshooting

Vetting data quality has often been tough. Sifflet makes it easier and simpler to trace unusual values thanks to data lineage, and get historical context of data changes and updates.

Analysis Confidence

You’ll be able to analyze numbers with confidence thanks to knowledge of who owns and maintains different data assets and verify data accuracy before sharing insights.

Superior Insights. Check.

Sifflet makes it easier to gain strategic insights about your market, products, and customers. By ensuring the highest levels of data quality, your teams can make the best possible strategic decisions for your company, unlocking new levels of performance that help you compete in the age of AI.

Never Question Your Numbers Again.

Sifflet gives you the ultimate confidence in your data products and dashboards. By ensuring that your data is monitored and triaged night and day, you can always be sure of the freshness, accuracy, and quality of your numbers.

See Value From Day One.

Sifflet connects to hundreds of tools already in your stack and offers out-of-the-box monitors and tooling so you can start seeing value from day one.

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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How Carrefour transformed Data Quality

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Frequently asked questions

Why does AI often fail even when the models are technically sound?

Great question! AI doesn't usually fail because of bad models, but because of unreliable data. Without strong data observability in place, it's hard to detect data issues like schema changes, stale tables, or broken pipelines. These problems undermine trust, and without trust in your data, even the best models can't deliver value.

What’s Sifflet’s vision for data observability in 2025?

Our 2025 vision is all about pushing the boundaries of cloud data observability. We're focusing on deeper automation, AI-driven insights, and expanding our observability platform to cover everything from real-time metrics to predictive analytics monitoring. It's about making data operations more resilient, transparent, and scalable.

Can I trust the data I find in the Sifflet Data Catalog?

Absolutely! Thanks to Sifflet’s built-in data quality monitoring, you can view real-time metrics and health checks directly within the Data Catalog. This gives you confidence in the reliability of your data before making any decisions.

What’s coming next for dbt integration in Sifflet?

We’re just getting started! Soon, you’ll be able to monitor dbt run performance and resource utilization, define monitors in your dbt YAML files, and use custom metadata even more dynamically. These updates will further enhance your cloud data observability and make your workflows even more efficient.

What’s new with the Distribution Change monitor and how does it improve anomaly detection?

The upgraded Distribution Change monitor now focuses on tracking volume shifts between specific categories, like product lines or customer segments. This makes anomaly detection more precise by reducing noise and highlighting only the changes that truly matter. It's a smarter way to stay on top of data drift and ensure your metrics reflect reality.

How does Sifflet help detect and prevent data drift in AI models?

Sifflet is designed to monitor subtle changes in data distributions, which is key for data drift detection. This helps teams catch shifts in data that could negatively impact AI model performance. By continuously analyzing incoming data and comparing it to historical patterns, Sifflet ensures your models stay aligned with the most relevant and reliable inputs.

Why is data quality monitoring crucial for AI-readiness, according to Dailymotion’s journey?

Dailymotion emphasized that high-quality, well-documented, and observable data is essential for AI readiness. Data quality monitoring ensures that AI systems are trained on accurate and reliable inputs, which is critical for producing trustworthy outcomes.

How does Shippeo’s use of data pipeline monitoring enhance internal decision-making?

By enriching and aggregating operational data, Shippeo creates a reliable source of truth that supports product and operations teams. Their pipeline health dashboards and observability tools ensure that internal stakeholders can trust the data driving their decisions.

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