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Executives

Data Leader

Transform your data and analytics strategy and pave the way for AI by upleveling data quality, trust, reliability and overall team efficiency.

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Data Quality and Trust

Sifflet makes it possible to establish trust in data across your organization thanks to real time monitoring of data quality, completeness, and accuracy.

Operational Efficiency

Increase your team’s operational efficiency. Sifflet reduces the time your data teams spend on manual quality checks and troubleshooting. It also enables proactive issue resolution before problems cause downstream systems.

Risk and Compliance Management

Manage data risk and compliance. Sifflet helps you document and monitor data access patterns and potential security risks.

Drive Innovation and Enable AI

Sifflet’s data observability platform delivers the performance you need to keep data quality and reliability at peak, paving the way for game-changing digital capabilities and products.

Augment Your Team’s Productivity and Effectiveness

Data engineers, data analysts and data scientists are critical to your business’s most strategic work. Sifflet augments their productivity by giving them back hundreds of hours spent on mundane reliability or accuracy tasks. Everyone’s more effective with data observability.

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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Data Governance - A Thought Leader's Perspective

A Guide to Assessing your Need for Data Observability

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Frequently asked questions

Can data quality monitoring alone guarantee data reliability?

Not quite. While data quality monitoring helps ensure individual datasets are accurate and consistent, data reliability goes further by ensuring your entire data system is dependable over time. That includes pipeline orchestration visibility, anomaly detection, and proactive monitoring. Pairing data quality with a robust observability platform gives you a more comprehensive approach to reliability.

How can tools like Sifflet help with data quality monitoring?

Sifflet is designed to make data quality monitoring scalable and business-aware. It offers automated anomaly detection, real-time alerts, and impact analysis so you can focus on the issues that matter most. With features like data profiling, dynamic thresholding, and low-code setup, Sifflet empowers both technical and non-technical users to maintain high data reliability across complex pipelines. It's a great fit for modern data teams looking to reduce manual effort and improve trust in their data.

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.

How does Sifflet help optimize Data as a Product initiatives?

Sifflet enhances DaaP initiatives by providing comprehensive data observability dashboards, real-time metrics, and anomaly detection. It streamlines data pipeline monitoring and supports proactive data quality checks, helping teams ensure their data products are accurate, well-governed, and ready for use or monetization.

Why is data distribution such an important part of data observability?

Great question! Data distribution gives you insight into the shape and spread of your data values, which traditional monitoring tools often miss. While volume, schema, and freshness checks tell you if the data is present and structured correctly, distribution monitoring helps you catch hidden issues like skewed categories or outlier spikes. It's a key component of any modern observability platform focused on data reliability.

How does data observability fit into a modern data platform?

Data observability is a critical layer of a modern data platform. It helps monitor pipeline health, detect anomalies, and ensure data quality across your stack. With observability tools like Sifflet, teams can catch issues early, perform root cause analysis, and maintain trust in their analytics and reporting.

Why is a data catalog essential for modern data teams?

A data catalog is critical because it helps teams find, understand, and trust their data. It centralizes metadata, making data assets searchable and understandable, which reduces duplication, speeds up analytics, and supports data governance. When paired with data observability tools, it becomes a powerful foundation for proactive data management.

How does the Sifflet and Firebolt integration improve data observability?

Great question! By integrating with Firebolt, Sifflet enhances your data observability by offering real-time metrics, end-to-end lineage, and automated anomaly detection. This means you can monitor your Firebolt data warehouse with precision and catch data quality issues before they impact the business.

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