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Most catalogs go out of date the moment they’re published.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

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.

How does Sifflet help identify performance bottlenecks in dbt models?

Sifflet's dbt runs tab offers deep insights into model execution, cost, and runtime, making it easy to spot inefficiencies. You can also use historical performance data to set up custom dashboards and proactive monitors. This helps with capacity planning and ensures your data pipelines stay optimized and cost-effective.

What makes business-aware data observability so important?

Business-aware observability bridges the gap between technical issues and real-world outcomes. It’s not just about detecting schema changes or data drift — it’s about understanding how those issues affect KPIs, dashboards, and decisions. At Sifflet, we bring together telemetry instrumentation, data profiling, and business context so teams can prioritize incidents based on impact, not just severity. This empowers everyone, from data engineers to product managers, to trust and act on data with confidence.

Is this integration useful for teams focused on data governance and compliance?

Yes, it really is! With enhanced lineage and metadata tracking from source to destination, the Fivetran integration supports better data governance. It helps ensure transparency, traceability, and SLA compliance across your data ecosystem.

Where can I find Sifflet at Big Data LDN 2024?

You can find the Sifflet team at Booth Y640 during Big Data LDN on September 18-19. Stop by to learn more about our data observability platform and how we’re helping organizations like the BBC and Penguin Random House improve their data reliability.

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.

Why is data observability becoming essential for data-driven companies?

As more businesses rely on data to drive decisions, ensuring data reliability is critical. Data observability provides transparency into the health of your data assets and pipelines, helping teams catch issues early, stay compliant with SLAs, and ultimately build trust in their data.

How do Service Level Indicators (SLIs) help improve data product reliability?

SLIs are a fantastic way to measure the health and performance of your data products. By tracking metrics like data freshness, anomaly detection, and real-time alerts, you can ensure your data meets expectations and stays aligned with your team’s SLA compliance goals.

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