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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

Get Further

Data Governance - A Thought Leader's Perspective

A Guide to Assessing your Need for Data Observability

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Frequently asked questions

Which ingestion tools work best with cloud data observability platforms?

Popular ingestion tools like Fivetran, Stitch, and Apache Kafka integrate well with cloud data observability platforms. They offer strong support for telemetry instrumentation, real-time ingestion, and schema registry integration. Pairing them with observability tools ensures your data stays reliable and actionable across your entire stack.

What exactly is data quality, and why should teams care about it?

Data quality refers to how accurate, complete, consistent, and timely your data is. It's essential because poor data quality can lead to unreliable analytics, missed business opportunities, and even financial losses. Investing in data quality monitoring helps teams regain trust in their data and make confident, data-driven decisions.

What role does data lineage tracking play in data observability?

Data lineage tracking is a key part of data observability because it helps you understand where your data comes from and how it changes over time. With clear lineage, teams can perform faster root cause analysis and collaborate better across business and engineering, which is exactly what platforms like Sifflet enable.

Why is data observability so important for AI and analytics initiatives?

Great question! Data observability ensures that the data fueling AI and analytics is reliable, accurate, and fresh. At Sifflet, we see data observability as both a technical and business challenge, which is why our platform focuses on data quality monitoring, anomaly detection, and real-time metrics to help enterprises make confident, data-driven decisions.

How can integration and connectivity improve data pipeline monitoring?

When a data catalog integrates seamlessly with your databases, cloud storage, and data lakes, it enhances your ability to monitor data pipelines in real time. This connectivity supports better ingestion latency tracking and helps maintain a reliable observability platform.

Why is data observability becoming so important for businesses in 2025?

Great question! As Salma Bakouk shared in our recent webinar, data observability is critical because it builds trust and reliability across your data ecosystem. With poor data quality costing companies an average of $13 million annually, having a strong observability platform helps teams proactively detect issues, ensure data freshness, and align analytics efforts with business goals.

What are some common reasons data freshness breaks down in a pipeline?

Freshness issues often start with delays in source systems, ingestion bottlenecks, slow transformation jobs, or even caching problems in dashboards. That's why a strong observability platform needs to monitor every stage of the pipeline, from ingestion latency to delivery, to ensure data reliability and timely decision-making.

How does a data observability platform help improve inventory accuracy?

A data observability platform continuously monitors inventory data using real-time metrics and anomaly detection. It compares RFID scans with POS transactions, flags inconsistencies, and tracks key inventory KPIs. This helps retailers maintain more accurate stock levels and reduce shrinkage or overstocking.

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