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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

How does reverse ETL improve data reliability and reduce manual data requests?

Reverse ETL automates the syncing of data from your warehouse to business apps, helping reduce the number of manual data requests across teams. This improves data reliability by ensuring consistent, up-to-date information is available where it’s needed most, while also supporting SLA compliance and data automation efforts.

How does Sifflet help optimize Data as a Product initiatives?

Sifflet enhances DaaP initiatives by providing comprehensive data observability dashboards, real-time metrics, and anomaly detection. It streamlines data pipeline monitoring and supports proactive data quality checks, helping teams ensure their data products are accurate, well-governed, and ready for use or monetization.

How does Sifflet help with monitoring data distribution?

Sifflet makes distribution monitoring easy by using statistical profiling to learn what 'normal' looks like in your data. It then alerts you when patterns drift from those baselines. This helps you maintain SLA compliance and avoid surprises in dashboards or ML models. Plus, it's all automated within our data observability platform so you can focus on solving problems, not just finding them.

What role does Sifflet’s Data Catalog play in data governance?

Sifflet’s Data Catalog supports data governance by surfacing labels and tags, enabling classification of data assets, and linking business glossary terms for standardized definitions. This structured approach helps maintain compliance, manage costs, and ensure sensitive data is handled responsibly.

What does a modern data stack look like and why does it matter?

A modern data stack typically includes tools for ingestion, warehousing, transformation and business intelligence. For example, you might use Fivetran for ingestion, Snowflake for warehousing, dbt for transformation and Looker for analytics. Investing in the right observability tools across this stack is key to maintaining data reliability and enabling real-time metrics that support smart, data-driven decisions.

Why is the traditional approach to data observability no longer enough?

Great question! The old playbook for data observability focused heavily on technical infrastructure and treated data like servers — if the pipeline ran and the schema looked fine, the data was assumed to be trustworthy. But today, data is a strategic asset that powers business decisions, AI models, and customer experiences. At Sifflet, we believe modern observability platforms must go beyond uptime and freshness checks to provide context-aware insights that reflect real business impact.

Is Forge able to automatically fix data issues in my pipelines?

Forge doesn’t take action on its own, but it does provide smart, contextual guidance based on past fixes. It helps teams resolve issues faster while keeping you in full control of the resolution process, which is key for maintaining SLA compliance and data quality monitoring.

How does Sifflet use AI to improve data observability?

At Sifflet, we're integrating advanced AI models into our observability platform to enhance data quality monitoring and anomaly detection. Marie, our Machine Learning Engineer, has been instrumental in building intelligent systems that automatically detect issues across data pipelines, making it easier to maintain data reliability in real time.

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