Solution by Persona

Business Teams

Data User

Take control of your decisions. Sifflet gives business users unmatched clarity and trust in their data, driving smarter actions with ease.

Launch Product Tour

Data Freshness and Reliability

Sifflet gives data users visibility into when data was last updated, and alerts when source data changes unexpectedly, so you’ll always know the status of your numbers.

Self-Service Troubleshooting

Vetting data quality has often been tough. Sifflet makes it easier and simpler to trace unusual values thanks to data lineage, and get historical context of data changes and updates.

Analysis Confidence

You’ll be able to analyze numbers with confidence thanks to knowledge of who owns and maintains different data assets and verify data accuracy before sharing insights.

Superior Insights. Check.

Sifflet makes it easier to gain strategic insights about your market, products, and customers. By ensuring the highest levels of data quality, your teams can make the best possible strategic decisions for your company, unlocking new levels of performance that help you compete in the age of AI.

Never Question Your Numbers Again.

Sifflet gives you the ultimate confidence in your data products and dashboards. By ensuring that your data is monitored and triaged night and day, you can always be sure of the freshness, accuracy, and quality of your numbers.

See Value From Day One.

Sifflet connects to hundreds of tools already in your stack and offers out-of-the-box monitors and tooling so you can start seeing value from day one.

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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How Carrefour transformed Data Quality

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Frequently asked questions

Can data lineage help with regulatory compliance like GDPR?

Absolutely. Governance lineage, a key type of data lineage, tracks ownership, access controls, and data classifications. This makes it easier to demonstrate compliance with regulations like GDPR and SOX by showing how sensitive data is handled across your stack. It's a critical component of any data governance strategy and helps reduce audit preparation time.

Will Sifflet cover any upcoming trends in data observability?

For sure! Our CEO, Salma Bakouk, will be speaking about the top data trends to watch in 2025, including how GenAI and advanced anomaly detection are shaping the future of observability platforms. You’ll walk away with actionable insights for your data strategy.

Why is data quality management so important for growing organizations?

Great question! Data quality management helps ensure that your data remains accurate, complete, and aligned with business goals as your organization scales. Without strong data quality practices, teams waste time troubleshooting issues, decision-makers lose trust in reports, and systems make poor choices. With proper data quality monitoring in place, you can move faster, automate confidently, and build a competitive edge.

How does the new Custom Metadata feature improve data governance?

With Custom Metadata, you can tag any asset, monitor, or domain in Sifflet using flexible key-value pairs. This makes it easier to organize and route data based on your internal logic, whether it's ownership, SLA compliance, or business unit. It's a big step forward for data governance and helps teams surface high-priority monitors more effectively.

How does Sifflet’s observability platform help reduce alert fatigue?

We hear this a lot — too many alerts, not enough clarity. At Sifflet, we focus on intelligent alerting by combining metadata, data lineage tracking, and usage patterns to prioritize what really matters. Instead of just flagging that something broke, our platform tells you who’s affected, why it matters, and how to fix it. That means fewer false positives and more actionable insights, helping you cut through the noise and focus on what truly impacts your business.

Can Sifflet detect anomalies in my data pipelines?

Yes, it can! Sifflet uses machine learning for anomaly detection, helping you catch unexpected changes in data volume or quality. You can even label anomalies to improve the model's accuracy over time, reducing alert fatigue and improving incident response automation.

Who should be responsible for managing data quality in an organization?

Data quality management works best when it's a shared responsibility. Data stewards often lead the charge by bridging business needs with technical implementation. Governance teams define standards and policies, engineering teams build the monitoring infrastructure, and business users provide critical domain expertise. This cross-functional collaboration ensures that quality issues are caught early and resolved in ways that truly support business outcomes.

How can I keep passive metadata accurate and useful over time?

To maintain high-quality passive metadata, Sifflet recommends a mix of automated ingestion and manual curation. Connect your data sources, standardize tagging, build a business glossary, and schedule regular reviews. This helps ensure your data profiling and data validation rules stay aligned with evolving business needs.

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