Solution by Use Case

Cost Observability

Cost-efficient data pipelines

Pinpoint cost inefficiencies and anomalies thanks to full-stack data observability.

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Data asset optimization

Leverage lineage and Data Catalog to pinpoint underutilized assets
Get alerted on unexpected behaviors in data consumption patterns

Proactive data pipeline management

Proactively prevent pipelines from running in case a data quality anomaly is detected

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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Frequently asked questions

What exactly is data freshness, and why does it matter so much in data observability?

Data freshness refers to how current your data is relative to the real-world events it's meant to represent. In data observability, it's one of the most critical metrics because even accurate data can lead to poor decisions if it's outdated. Whether you're monitoring financial trades or patient records, stale data can have serious business consequences.

How does data observability support better data quality management?

Data observability plays a key role by giving teams real-time visibility into the health of their data pipelines. With observability tools like Sifflet, you can monitor data freshness, detect anomalies, and trace issues back to their root cause. This allows you to catch and fix data quality issues before they impact business decisions, making your data more reliable and your operations more efficient.

How does Sifflet help with monitoring data distribution?

Sifflet makes distribution monitoring easy by using statistical profiling to learn what 'normal' looks like in your data. It then alerts you when patterns drift from those baselines. This helps you maintain SLA compliance and avoid surprises in dashboards or ML models. Plus, it's all automated within our data observability platform so you can focus on solving problems, not just finding them.

How can data observability help prevent missed SLAs and unreliable dashboards?

Data observability plays a key role in SLA compliance by detecting issues like ingestion latency, schema changes, or data drift before they impact downstream users. With proper data quality monitoring and real-time metrics, you can catch problems early and keep your dashboards and reports reliable.

How is Etam using data observability to support its 2025 strategy?

Etam is leveraging data observability as a foundational element of its 2025 data strategy. With Sifflet’s observability platform, the team can monitor data quality, detect issues early, and ensure data reliability, which helps them move faster and with more confidence across the business.

How is Sifflet using AI to improve data observability?

We're leveraging AI to make data observability smarter and more efficient. Our AI agent automates monitor creation and provides actionable insights for anomaly detection and root cause analysis. It's all about reducing manual effort while boosting data reliability at scale.

Why is collaboration important in building a successful observability platform?

Collaboration is key to building a robust observability platform. At Sifflet, our teams work cross-functionally to ensure every part of the platform, from data lineage tracking to real-time metrics collection, aligns with business goals. This teamwork helps us deliver a more comprehensive and user-friendly solution.

What are some common consequences of bad data?

Bad data can lead to a range of issues including financial losses, poor strategic decisions, compliance risks, and reduced team productivity. Without proper data quality monitoring, companies may struggle with inaccurate reports, failed analytics, and even reputational damage. That’s why having strong data observability tools in place is so critical.

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