How Shippeo Scales Data Reliability to Deliver Real-Time Supply Chain Visibility

Starting at the Core: Data That Matters

Shippeo’s business is all about visibility. To support this, the analytics team collects critical operational data straight from the heart of the company’s SaaS platform. This includes order data, GPS positions, site information, customer lists, user preferences and platform activity.

This foundational data spans every mode of transport Shippeo covers, from road and rail to ocean and air. The team ensures that this raw data is properly structured and enriched so that it can power multiple applications internally and externally.

From Raw Data to Reliable Insights

The goal is not just to store data. The goal is to make it useful.

Shippeo has developed pipelines that enrich and aggregate this operational data, transforming it into a reliable source of truth. This data then feeds into two primary use cases.

First, it powers embedded analytics for clients through Tableau dashboards. These allow customers to monitor key metrics such as delivery delays, transit time by carrier and performance issues.

Second, the data supports internal decision-making across product and operations teams, helping them build better tools and services.

✨ Quick plug: if you're new to data observability, check out our guide on to assess your need for data observability. Spoiler: it’s about more than just firefighting.

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Why Data Observability Became Essential

As with many growing data teams, Shippeo began to experience pain points that could not be ignored. Mathieu described a few critical issues they faced:

• Quality assurance was missing from key parts of the data pipeline
• Data issues were being discovered by end users, not data engineers
• Silent leaks and inconsistent metrics were eroding trust
• Some issues had direct impacts on billing and customer reporting

To address this, Shippeo adopted Sifflet to bring data observability into their stack. This was a turning point.

With Sifflet, the team introduced a structured monitoring approach:

• Raw data monitoring to track ingestion volume and freshness
• Intermediate layer checks to ensure consistency in calculations and detect regressions
• Front-facing metric validation to alert when customer-facing KPIs deviate unexpectedly

This system allowed Shippeo to detect and resolve issues faster, often before customers noticed anything was wrong.

Extending Reliability to the Customer Experience

What is most impressive about Shippeo’s approach is how it extends beyond the technical team. The Customer Experience team now uses monitoring tools built with the help of operations and analytics engineers. These tools track the freshness and volume of source data like shipment and position data.

When problems occur, such as a drop in GPS data or a sudden spike in transport orders, CX managers receive alerts. They can take action right away, informing customers and fixing issues with carrier connections. This shift has helped Shippeo maintain customer trust while improving operational responsiveness.

TL;DR?

At our recent Data Breakfast, Mathieu De Launay from Shippeo shared how his team uses Sifflet to build reliable, trustworthy data pipelines that serve both clients and internal stakeholders. Key highlights include:

• Core data is collected directly from Shippeo’s operational SaaS platform, including orders, positions and customer preferences
• Data pipelines are built to serve both internal teams and external clients
• Sifflet provides observability at every stage of the stack, from ingestion to business metrics
• The Customer Experience team now benefits from real-time alerts on data issues, improving customer satisfaction and operational efficiency

These Data Breakfast events continue to prove how valuable it is for data professionals to come together, exchange best practices and stay ahead of the curve. As the data landscape becomes more complex, data observability is not just a nice-to-have. It is a must-have for anyone serious about preventing data leaks and building trust in data products.

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