Enforcing Data Governance

Solution by Use Case

Stop relying on stale documentation and manual spot-checks. Automate your governance enforcement with business-aware observability.

See Sifflet in Action!

Automated Tagging and Catalog Sync

Bring in tags directly from your Data Catalog (like Atlan) or Snowflake Object tags to apply active monitoring to governed assets.

Synchronize official descriptions, PII classifications, and business terms automatically across your ecosystem.
Create monitoring policies based on catalog tags rather than manually configuring individual tables.
Ensure your data catalog reflects the actual state of your data, transforming it from a static wiki into an operational control plane.

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Compliance Drift Monitoring

Be alerted when data movement, schema changes, or data quality metrics violate internal governance standards or regulatory requirements.

Catch data quality issues before they become material audit findings or trigger regulatory fines.
Monitor critical data elements (CDEs) continuously to prove compliance with frameworks like GDPR, SOX, or Basel III.
Automatically flag missing owners or unmonitored assets that expose the business to governance and security risk.

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Metadata Enrichment & Documentation

Understand the metadata and business context of your data to ensure decentralized teams are operating with a shared view of reality.

Layer business usage and ownership directly over technical signals so teams know exactly what assets matter most.
Empower data stewards to manage data health without needing to write complex engineering scripts.
Enforce data contracts at the source to prevent upstream teams from silently breaking downstream governance rules.

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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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Still have a question in mind ?

Frequently asked questions

How can data teams prioritize what to monitor in complex environments?

Not all data is created equal, so it's important to focus data quality monitoring efforts on the assets that drive business outcomes. That means identifying key dashboards, critical metrics, and high-impact models, then using tools like pipeline health dashboards and SLA monitoring to keep them reliable and fresh.

How is Etam using data observability to support its 2025 strategy?

Etam is leveraging data observability as a foundational element of its 2025 data strategy. With Sifflet’s observability platform, the team can monitor data quality, detect issues early, and ensure data reliability, which helps them move faster and with more confidence across the business.

How does the improved test connection process for Snowflake observability help teams?

The revamped 'Test Connection' process for Snowflake observability now provides detailed feedback on missing permissions or policy issues. This makes setup and troubleshooting much easier, especially during onboarding. It helps ensure smooth data pipeline monitoring and reduces the risk of refresh failures down the line.

What’s the first step when building a modern data team from scratch?

The very first step is to set clear objectives that align with your company’s level of data maturity and business needs. This means involving stakeholders from different departments and deciding whether your focus is on exploratory analysis, business intelligence, or innovation through AI and ML. These goals will guide your choices in data stack, platform, and hiring.

Why is data lineage important for GDPR compliance?

Data lineage is essential for GDPR because it helps you trace personal data from source to destination. This means you can see where PII is stored, how it flows through your data pipelines, and which reports or applications use it. With this visibility, you can manage deletion requests, audit data usage, and ensure data governance policies are enforced consistently.

How can observability platforms help with compliance and audit logging?

Observability platforms like Sifflet support compliance monitoring by tracking who accessed what data, when, and how. We help teams meet GDPR, NERC CIP, and other regulatory requirements through audit logging, data governance tools, and lineage visibility. It’s all about making sure your data is not just stored safely but also traceable and verifiable.

Why is technology critical to scaling data governance across teams?

Technology automates key governance tasks such as data classification, access control, and telemetry instrumentation. With the right tools, like a data observability platform, organizations can enforce policies at scale, detect anomalies automatically, and integrate governance into daily workflows. This reduces manual effort and ensures governance grows with the business.

What makes Sifflet stand out when it comes to data reliability and trust?

Sifflet shines in data reliability by offering real-time metrics and intelligent anomaly detection. During the webinar, we saw how even non-technical users can set up custom monitors, making it easy for teams to catch issues early and maintain SLA compliance with confidence.

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