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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

What role does data lineage tracking play in data discovery?

Data lineage tracking is essential for understanding how data flows through your systems. It shows you where data comes from, how it’s transformed, and where it ends up. This is super helpful for root cause analysis and makes data discovery more efficient by giving you context and confidence in the data you're using.

How does Sifflet support local development workflows for data teams?

Sifflet is integrating deeply with local development tools like dbt and the Sifflet CLI. Soon, you'll be able to define monitors directly in dbt YAML files and run them locally, enabling real-time metrics checks and anomaly detection before deployment, all from your development environment.

Why is smart alerting important in data observability?

Smart alerting helps your team focus on what really matters. Instead of flooding your Slack with every minor issue, a good observability tool prioritizes alerts based on business impact and data asset importance. This reduces alert fatigue and ensures the right people get notified at the right time. Look for platforms that offer customizable severity levels, real-time alerts, and integrations with your incident management tools like PagerDuty or email alerts.

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.

How can I better manage stakeholder expectations for the data team?

Setting clear priorities and using a centralized pipeline orchestration visibility tool can help manage expectations across the organization. When stakeholders understand what the team can deliver and when, it builds trust and reduces pressure on your team, leading to a healthier and happier work environment.

What’s the role of an observability platform in scaling data trust?

An observability platform helps scale data trust by providing real-time metrics, automated anomaly detection, and data lineage tracking. It gives teams visibility into every layer of the data pipeline, so issues can be caught before they impact business decisions. When observability is baked into your stack, trust becomes a natural part of the system.

How does the new Custom Metadata feature improve data governance?

With Custom Metadata, you can tag any asset, monitor, or domain in Sifflet using flexible key-value pairs. This makes it easier to organize and route data based on your internal logic, whether it's ownership, SLA compliance, or business unit. It's a big step forward for data governance and helps teams surface high-priority monitors more effectively.

How does Sifflet help scale dbt environments without compromising data quality?

Great question! Sifflet enhances your dbt environment by adding a robust data observability layer that enforces standards, monitors key metrics, and ensures data quality monitoring across thousands of models. With centralized metadata, automated monitors, and lineage tracking, Sifflet helps teams avoid the usual pitfalls of scaling like ownership ambiguity and technical debt.

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