GALERIES LAFAYETTE

Fiabilisez vos données e-commerce avec Sifflet!

Dans un environnement omnicanal comme celui des Galeries Lafayette, la fiabilité des données e-commerce est un levier stratégique.
Entre taux de conversion, suivi des ventes, données de caisse, ou performance des campagnes, chaque point de friction dans la chaîne de données peut affecter les décisions business et l’expérience client.
Avec Sifflet, vous détectez les anomalies avant qu’elles n’impactent vos KPIs.

Pourquoi utiliser Sifflet

Equipez vos équipes data et business avec le meilleur outil possible de data observabilité

Monitoring Automatique

Surveillez en continu vos pipelines et jeux de données critiques pour détecter les anomalies avant qu'elles n'affectent vos KPIs.

Alerting Intelligent Multicanal

Recevez des alertes ciblées sur Slack, email ou autres outils, pour mobiliser les bonnes équipes au bon moment.

Gouvernance intégrée et accessible aux métiers

Offrez à vos équipes une vue claire sur la qualité, la fraîcheur et la traçabilité des données – sans dépendre des experts techniques.

CAS d'USAGE 1

Monitoring des ventes et taux de conversion

Client : We Casa
L’équipe acquisition utilise Sifflet pour surveiller en temps réel les performances du tunnel de vente.

  • En cas d’anomalie sur le taux de conversion ou sur le volume de ventes, des alertes sont envoyées automatiquement aux équipes data et marketing.

Le Résultat? Réactivité accrue, détection rapide des incidents de tracking ou bugs techniques.

Sifflet ai assistant illustration
CAS D'USAGE 2

Vérification des tickets de caisse

Client : Bonpoint
Sifflet permet de vérifier que chaque ticket de caisse émis en boutique remonte bien dans les systèmes centraux.

  • Des règles personnalisées détectent les écarts ou données manquantes.

Le résultat? Visibilité sur la santé opérationnelle de chaque magasin, fiabilité des reportings.

Sifflet troubleshoot illustration
CAS D'USAGE 3

Suivi des données tierces (Amazon, etc.)

Client : Penguin Random House
Grâce aux monitors automatisés, Penguin vérifie la fraîcheur, la complétude et la structure des données issues de ses partenaires.

  • Les jeux de données critiques (inventaire, commandes, ventes, trafic) sont sous contrôle.

Le Résultat? Moins de risques liés aux données fournisseurs, meilleure fiabilité des dashboards Power BI.

Sifflet driving illustration
CAS D'USAGE 4

Renforcement de la confiance métier

Sifflet permet aux équipes marketing, e-commerce ou finance :

  • D’identifier l’origine d’un chiffre
  • De mesurer la qualité des données utilisées
  • D’être alertées en temps réel en cas d’anomalie

Le Résultat? Moins de temps à douter des chiffres, plus de temps pour prendre des décisions.

sifflet datacatalog

Fiabilisez vos KPIs stratégiques e-commerce

Assurez la qualité de vos métriques clés : taux de conversion, ventes, paniers, inventaire… pour des reportings sans zones d’ombre.

Offrez aux équipes métier des données de confiance, en temps réel

Faites gagner en autonomie vos équipes marketing, e-commerce ou finance avec une donnée documentée, fraîche et exploitable.

Réduisez les incidents avant qu'ils n'aient un impact business

Anticipez les problèmes de données grâce à une détection proactive et évitez les mauvaises décisions ou les pertes de revenus.

On y va?

Sifflet+Galeries Lafayette, ca pourrait être la collab' du siècle, non?

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

Frequently asked questions

How does SQL Table Tracer handle different SQL dialects?
SQL Table Tracer uses Antlr4 with semantic predicates to support multiple SQL dialects like Snowflake, Redshift, and PostgreSQL. This flexible parsing approach ensures accurate lineage extraction across diverse environments, which is essential for data pipeline monitoring and distributed systems observability.
Why are traditional data catalogs no longer enough for modern data teams?
Traditional data catalogs focus mainly on metadata management, but they don't actively assess data quality or track changes in real time. As data environments grow more complex, teams need more than just an inventory. They need data observability tools that provide real-time metrics, anomaly detection, and data quality monitoring to ensure reliable decision-making.
How does Sifflet support data quality monitoring for large organizations?
Sifflet is built to scale. It supports automated data quality monitoring across hundreds of assets, as seen with Carrefour Links monitoring over 800 data assets in 8+ countries. With dynamic thresholding, schema change detection, and real-time metrics, Sifflet ensures SLA compliance and consistent data reliability across complex ecosystems.
What benefits does end-to-end data lineage offer my team?
End-to-end data lineage helps your team perform accurate impact assessments and faster root cause analysis. By connecting declared and built-in assets, you get full visibility into upstream and downstream dependencies, which is key for data reliability and operational intelligence.
What should a solid data quality monitoring framework include?
A strong data quality monitoring framework should be scalable, rule-based and powered by AI for anomaly detection. It should support multiple data sources and provide actionable insights, not just alerts. Tools that enable data drift detection, schema validation and real-time alerts can make a huge difference in maintaining data integrity across your pipelines.
What are the main challenges of implementing Data as a Product?
Some key challenges include ensuring data privacy and security, maintaining strong data governance, and investing in data optimization. These areas require robust monitoring and compliance tools. Leveraging an observability platform can help address these issues by providing visibility into data lineage, quality, and pipeline performance.
What is data ingestion and why is it so important for modern businesses?
Data ingestion is the process of collecting and loading data from various sources into a central system like a data lake or warehouse. It's the first step in your data pipeline and is critical for enabling real-time metrics, analytics, and operational decision-making. Without reliable ingestion, your downstream analytics and data observability efforts can quickly fall apart.
How do modern storage platforms like Snowflake and S3 support observability tools?
Modern platforms like Snowflake and Amazon S3 expose rich metadata and access patterns that observability tools can monitor. For example, Sifflet integrates with Snowflake to track schema changes, data freshness, and query patterns, while S3 integration enables us to monitor ingestion latency and file structure changes. These capabilities are key for real-time metrics and data quality monitoring.
Still have questions?