Solution by Use Case

Analytics Trust and Reliability

Shared Understanding. Ultimate Confidence. At Scale.

When everyone knows your data is systematically validated for quality, understands where it comes from and how it's transformed, and is aligned on freshness and SLAs, what’s not to trust?

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Always Fresh. Always Validated.

No more explaining data discrepancies to the C-suite. Thanks to automatic and systematic validation, Sifflet ensures your data is always fresh and meets your quality requirements. Stakeholders know when data might be stale or interrupted, so they can make decisions with timely, accurate data.

Automatically detect schema changes, null values, duplicates, or unexpected patterns that could comprise analysis.
Set and monitor service-level agreements (SLAs) for critical data assets.
Track when data was last updated and whether it meets freshness requirements

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Understand Your Data, Inside and Out

Give data analysts and business users ultimate clarity. Sifflet helps teams understand their data across its whole lifecycle, and gives full context like business definitions, known limitations, and update frequencies, so everyone works from the same assumptions.

Create transparency by helping users understand data pipelines, so they always know where data comes from and how it’s transformed.
Develop shared understanding in data that prevents misinterpretation and builds confidence in analytics outputs.
Quickly assess which downstream reports and dashboards are affected

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 data quality so critical for businesses today?

Great question! Data quality is essential because it directly influences decision-making, customer satisfaction, and operational efficiency. Poor data quality can lead to faulty insights, wasted resources, and even reputational damage. That's why many teams are turning to data observability platforms to ensure their data is accurate, complete, and trustworthy across the entire pipeline.

Can I use data monitoring and data observability together?

Absolutely! In fact, data monitoring is often a key feature within a broader data observability solution. At Sifflet, we combine traditional monitoring with advanced capabilities like data profiling, pipeline health dashboards, and data drift detection so you get both alerts and insights in one place.

How can poor data distribution impact machine learning models?

When data distribution shifts unexpectedly, it can throw off the assumptions your ML models are trained on. For example, if a new payment processor causes 70% of transactions to fall under $5, a fraud detection model might start flagging legitimate behavior as suspicious. That's why real-time metrics and anomaly detection are so crucial for ML model monitoring within a good data observability framework.

How does Sifflet stand out among other data observability tools?

Sifflet takes a unique approach by addressing data reliability as both an engineering and business challenge. Our observability platform offers end-to-end coverage, business context, and a collaboration layer that aligns technical teams with strategic outcomes, making it easier to maintain analytics and AI-ready data.

How does data observability improve the value of a data catalog?

Data observability enhances a data catalog by adding continuous monitoring, data lineage tracking, and real-time alerts. This means organizations can not only find their data but also trust its accuracy, freshness, and consistency. By integrating observability tools, a catalog becomes part of a dynamic system that supports SLA compliance and proactive data governance.

Why are data teams moving away from Monte Carlo to newer observability tools?

Many teams are looking for more flexible and cost-efficient observability tools that offer better business user access and faster implementation. Monte Carlo, while a pioneer, has become known for its high costs, limited customization, and lack of business context in alerts. Newer platforms like Sifflet and Metaplane focus on real-time metrics, cross-functional collaboration, and easier setup, making them more appealing for modern data teams.

How does data observability fit into a modern data platform?

Data observability is a critical layer of a modern data platform. It helps monitor pipeline health, detect anomalies, and ensure data quality across your stack. With observability tools like Sifflet, teams can catch issues early, perform root cause analysis, and maintain trust in their analytics and reporting.

Is this feature scalable for large datasets and multiple data assets?

Yes, it is! With Sifflet’s auto-coverage and observability tools, you can monitor distribution deviation at scale with just a few clicks. Whether you're working with batch data observability or streaming data monitoring, Sifflet has you covered with automated, scalable insights.

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