Solution by Persona

Modern Data Governance Starts with Observability

Data Governance Leader

You’re not just checking compliance boxes. You’re enabling safe, scalable data use across the organization. Sifflet gives governance leaders full visibility, control, and automation, so policies stick, risks shrink, and data stays trusted no matter how fast things move.

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Stronger Compliance, Less Manual Work

Automated cataloging, lineage, and audit trails make it easier to meet regulatory requirements without drowning in spreadsheets or manual updates. Sifflet keeps your governance up to date and always inspection-ready.

Fewer Blind Spots Across the Data Stack

With Sifflet, governance teams get full visibility across ingestion, transformation, and consumption, including shadow data and undocumented assets. You can finally govern what’s actually in use, not just what’s documented.

Governance That Scales With the Business

Sifflet integrates directly into your data workflows, so policies and controls scale alongside your teams and infrastructure. Whether you're onboarding new domains or expanding your stack, governance stays aligned and under control.

A Catalog That’s Actually Alive

Most catalogs go out of date the moment they’re published. Sifflet’s catalog is powered by automated metadata ingestion across your full stack: Snowflake, Databricks, dbt, Tableau, and more. You get real-time visibility into schema changes, freshness, usage, and ownership. No more chasing people for updates. No more flying blind.

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Lineage That Goes Beyond Tables

Sifflet provides deep, column-level lineage with full context, not just technical dependencies, but business impact. You can trace an issue from a broken pipeline to a downstream dashboard, and instantly see which KPIs, reports, or teams are affected. This makes policy enforcement, impact analysis, and root-cause resolution fast and reliable.

Governance Built Into the Workflow

Sifflet turns governance from a reactive process into a built-in feature of everyday data ops. You can tag sensitive assets, certify trusted datasets, monitor for violations, and set up alerts, all from inside the platform. No need to bolt governance onto the side. It’s already baked in, and it scales with your data.

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 should I look for in a modern data discovery tool?

Look for features like self-service discovery, automated metadata collection, and end-to-end data lineage. Scalability is key too, especially as your data grows. Tools like Sifflet also integrate data observability, so you can monitor data quality and pipeline health while exploring your data assets.

What is “data-quality-as-code”?

Data-quality-as-code (DQaC) allows you to programmatically define and enforce data quality rules using code. This ensures consistency, scalability, and better integration with CI/CD pipelines. Read more here to find out how to leverage it within Sifflet

How does Sifflet use AI to enhance data observability?

Sifflet uses AI not just for buzzwords, but to genuinely improve your workflows. From AI-powered metadata generation to dynamic thresholding and intelligent anomaly detection, Sifflet helps teams automate data quality monitoring and make faster, smarter decisions based on real-time insights.

What kind of usage insights can I get from Sifflet to optimize my data resources?

Sifflet helps you identify underused or orphaned data assets through lineage and usage metadata. By analyzing this data, you can make informed decisions about deprecating unused tables or enhancing monitoring for critical pipelines. It's a smart way to improve pipeline resilience and reduce unnecessary costs in your data ecosystem.

What is the difference between data monitoring and data observability?

Great question! Data monitoring is like your car's dashboard—it alerts you when something goes wrong, like a failed pipeline or a missing dataset. Data observability, on the other hand, is like being the driver. It gives you a full understanding of how your data behaves, where it comes from, and how issues impact downstream systems. At Sifflet, we believe in going beyond alerts to deliver true data observability across your entire stack.

How does data observability help control cloud costs?

Data observability shines a light on hidden inefficiencies like redundant queries or unused pipelines. By using observability to track resource utilization and detect anomalies in compute usage, one financial services firm cut their Snowflake spend by 40%. It turns cloud cost management from guesswork into a data-driven process.

Is there a data observability platform that supports both business and technical users?

Yes, Sifflet is designed to be accessible for both business stakeholders and data engineers. It offers intuitive interfaces for no-code monitor creation, context-rich alerts, and field-level data lineage tracking. This democratizes data quality monitoring and helps teams across the organization stay aligned on data health and pipeline performance.

How does data ingestion relate to data observability?

Great question! Data ingestion is where observability starts. Once data enters your system, observability platforms like Sifflet help monitor its quality, detect anomalies, and ensure data freshness. This allows teams to catch ingestion issues early, maintain SLA compliance, and build trust in their data pipelines.

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