Solution by Use Case

Data governance

Proactive access, quality and control

Empower data teams to detect and address issues proactively by providing them with tools to ensure data availability, usability, integrity, and security.

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De-risked data discovery

Ensure proactive data quality thanks to a large library of OOTB monitors and a built-in notification system
Gain visibility over assets’ documentation and health status on the Data Catalog for safe data discovery
Establish the official source of truth for key business concepts using the Business Glossary
Leverage custom tagging to classify assets

Structured data observability platform

Tailor data visibility for teams by grouping assets in domains that align with the company’s structure
Define data ownership to improve accountability and smooth collaboration across teams

Secured data management

Safeguard PII data securely through ML-based PII detection

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 impact did Sifflet have on fostering a data-driven culture at Meero?

Sifflet’s intuitive UI and real-time data observability dashboards empowered even non-technical users at Meero to understand data health. This transparency helped build trust in data and promoted a stronger data-driven culture across the organization.

How does Sifflet support data quality monitoring at scale?

Sifflet makes data quality monitoring scalable with features like auto-coverage, which automatically generates monitors across your datasets. Whether you're working with Snowflake, BigQuery, or other platforms, you can quickly reach high monitoring coverage and get real-time alerts via Slack, email, or MS Teams to ensure data reliability.

How did implementing a data observability platform impact Hypebeast’s operations?

After adopting Sifflet’s observability platform, Hypebeast saw a 204% improvement in data quality, a 178% increase in data product delivery, and a 75% boost in ad hoc request speed. These gains translated into faster, more reliable insights and better collaboration across departments.

Why is semantic quality monitoring important for AI applications?

Semantic quality monitoring ensures that the data feeding into your AI models is contextually accurate and production-ready. At Sifflet, we're making this process seamless with tools that check for data drift, validate schema, and maintain high data quality without manual intervention.

What does Full Data Stack Observability mean?

Full Data Stack Observability means having complete visibility into every layer of your data pipeline, from ingestion to business intelligence tools. At Sifflet, our observability platform collects signals across your entire stack, enabling anomaly detection, data lineage tracking, and real-time metrics collection. This approach helps teams ensure data reliability and reduce time spent firefighting issues.

What makes Sifflet different from other data observability platforms like Monte Carlo or Anomalo?

Sifflet stands out by offering a unified observability platform that combines data cataloging, monitoring, and data lineage tracking in one place. Unlike tools that focus only on anomaly detection or technical metrics, Sifflet brings in business context, empowering both technical and non-technical users to collaborate and ensure data reliability at scale.

What kind of visibility does a data observability platform provide?

A robust data observability platform like Sifflet gives you end-to-end visibility into your data ecosystem. This includes data freshness checks, schema changes, lineage tracking, and anomaly detection. It's like having a complete map of your data journey, helping you proactively manage quality and trust in your analytics.

What is the Model Context Protocol (MCP), and why is it important for data observability?

The Model Context Protocol (MCP) is a new interface standard developed by Anthropic that allows large language models (LLMs) to interact with tools, retain memory, and access external context. At Sifflet, we're excited about MCP because it enables more intelligent agents that can help with data observability by diagnosing issues, triggering remediation tools, and maintaining context across long-running investigations.

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Proactive access, quality and control