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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

Why is data observability so important for modern data teams?

Great question! Data observability is essential because it gives teams full visibility into the health of their data pipelines. Without it, small issues can quickly snowball into major incidents, like broken dashboards or faulty machine learning models. At Sifflet, we help you catch problems early with real-time metrics and proactive monitoring, so your team can focus on creating insights, not putting out fires.

How does Sifflet help reduce alert fatigue for data teams?

Sifflet filters alerts based on business criticality, so teams aren’t overwhelmed by noise. By aligning alerts with business context, it ensures only the most impactful issues get escalated. This smarter approach to pipeline error alerting helps teams focus on what truly matters and reduces unnecessary interruptions.

How can data lineage tracking help with root cause analysis?

Data lineage tracking shows how data flows through your systems and how different assets depend on each other. This is incredibly helpful for root cause analysis because it lets you trace issues back to their source quickly. With Sifflet’s lineage capabilities, you can understand both upstream and downstream impacts of a data incident, making it easier to resolve problems and prevent future ones.

How does Full Data Stack Observability help improve data quality at scale?

Full Data Stack Observability gives you end-to-end visibility into your data pipeline, from ingestion to consumption. It enables real-time anomaly detection, root cause analysis, and proactive alerts, helping you catch and resolve issues before they affect your dashboards or reports. It's a game-changer for organizations looking to scale data quality efforts efficiently.

Why might a company need more than just data quality monitoring?

While data quality monitoring is essential, many enterprises need broader observability that includes pipeline health, infrastructure performance, and downstream usage. Platforms like Sifflet provide this full-stack visibility, helping teams achieve SLA compliance, streamline incident response, and ensure data reliability throughout the entire lifecycle.

How does Sifflet make data observability more accessible to BI users?

Great question! At Sifflet, we're committed to making data observability insights available right where you work. That’s why we’ve expanded beyond our Chrome extension to integrate directly with popular Data Catalogs like Atlan, Alation, Castor, and Data Galaxy. This means BI users can access real-time metrics and data quality insights without ever leaving their workflow.

How do JOIN strategies affect query execution and data observability?

JOINs can be very resource-intensive if not used correctly. Choosing the right JOIN type and placing conditions in the ON clause helps reduce unnecessary data processing, which is key for effective data observability and real-time metrics tracking.

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.

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