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

Why is a centralized Data Catalog important for data reliability and SLA compliance?

A centralized Data Catalog like Sifflet’s plays a key role in ensuring data reliability and SLA compliance by offering visibility into asset health, surfacing incident alerts, and providing real-time metrics. This empowers teams to monitor data pipelines proactively and meet service level expectations more consistently.

How does Sifflet enhance data governance for my organization?

Sifflet supports data governance by allowing you to classify assets with tags and labels, define business terms in a shared glossary, and track data lineage. These features help ensure consistent definitions and safe handling of sensitive data across your stack.

How can I prevent schema changes from breaking my data pipelines?

You can prevent schema-related breakages by using data observability tools that offer real-time schema drift detection and alerting. These tools help you catch changes early, validate against data contracts, and maintain SLA compliance across your data pipelines.

What are some best practices for ensuring data quality during transformation?

To ensure high data quality during transformation, start with strong data profiling and cleaning steps, then use mapping and validation rules to align with business logic. Incorporating data lineage tracking and anomaly detection also helps maintain integrity. Observability tools like Sifflet make it easier to enforce these practices and continuously monitor for data drift or schema changes that could affect your pipeline.

How does Shippeo ensure data reliability across its supply chain platform?

Shippeo uses Sifflet’s data observability platform to monitor every stage of their data pipelines. By implementing raw data monitoring, intermediate layer checks, and front-facing metric validation, they catch issues early and maintain trust in their real-time supply chain visibility tools.

How does data lineage tracking help when something breaks?

Data lineage tracking is a lifesaver when you’re dealing with broken dashboards or bad reports. It maps your data’s journey from source to consumption, so when something goes wrong, you can quickly see what downstream assets are affected. This is key for fast root cause analysis and helps you notify the right business stakeholders. A good observability platform will give you both technical and business lineage, making it easier to trace issues back to their source.

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 metrics should I track to assess the health of AI systems?

To assess AI health, track metrics like Mean Time to Detection (MTTD), Mean Time to Resolution (MTTR), and data freshness checks. These metrics, combined with robust data pipeline monitoring and anomaly scoring, give you a clear view into model performance and governance effectiveness over time.

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