Data Leader
Transform your data and analytics strategy and pave the way for AI by upleveling data quality, trust, reliability and overall team efficiency.
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 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. "
"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."
"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"
"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."
"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."
Get Further
Data Governance - A Thought Leader's Perspective
Hear it from Dan Power, a Thought Leader and MD in Data Governance at State Street Asset Management
A Guide to Assessing your Need for Data Observability
Every data-driven team dreams of creating "fireworks" with their insights. But without control, the "fire" of the modern data stack can spark chaos.Discover if you're fighting fires or igniting fireworks:
Frequently asked questions
Data-quality-as-code (DQaC) allows you to programmatically define and enforce data quality rules using code. This ensures consistency, scalability, and better integration with CI/CD pipelines. Read more here to find out how to leverage it within Sifflet
Yes, Sifflet leverages AI to enhance data observability with features like anomaly detection and predictive insights. This ensures your data systems remain resilient and can support advanced analytics and AI-driven initiatives. Have a look at how Sifflet is leveraging AI for better data observability here
AI enhances data observability with advanced anomaly detection, predictive analytics, and automated root cause analysis. This helps teams identify and resolve issues faster while reducing manual effort. Have a look at how Sifflet is leveraging AI for better data observability here
Data observability ensures data governance policies are adhered to by tracking data usage, quality, and lineage. It provides the transparency needed for accountability and compliance. Read more here.
Yes! While smaller organizations may have fewer data pipelines, ensuring data quality and reliability is equally important for making accurate decisions and scaling effectively. What really matters is the data stack maturity and volume of data. Take our test here to find out if you really need data observability.
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