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

What’s a real-world example of Dailymotion using real-time metrics to drive business value?
One standout example is their ad inventory forecasting tool. By embedding real-time metrics into internal tools, sales teams can plan campaigns more precisely and avoid last-minute scrambles. It’s a great case of using data to improve both accuracy and efficiency.
Who are some of the companies using Sifflet’s observability tools?
We're proud to work with amazing organizations like St-Gobain, Penguin Random House, and Euronext. These enterprises rely on Sifflet for cloud data observability, data lineage tracking, and proactive monitoring to ensure their data is always AI-ready and analytics-friendly.
How does Sifflet support both technical and business teams?
Sifflet is designed to bridge the gap between data engineers and business users. It combines powerful features like automated anomaly detection, data lineage, and context-rich alerting with a no-code interface that’s accessible to non-technical teams. This means everyone—from analysts to execs—can get real-time metrics and insights about data reliability without needing to dig through logs or write SQL. It’s observability that works across the org, not just for the data team.
Can Flow Stopper work with tools like Airflow and Snowflake?
Absolutely! Flow Stopper supports integration with popular tools like Airflow for orchestration and Snowflake for storage. It can run anomaly detection and data validation rules mid-pipeline, helping ensure data quality as it moves through your stack.
What kind of metadata can I see for a Fivetran connector in Sifflet?
When you click on a Fivetran connector node in the lineage, you’ll see key metadata like source and destination, sync frequency, current status, and the timestamp of the latest sync. This complements Sifflet’s existing metadata like owner and last refresh for complete context.
Can observability tools help with GDPR-related incident response?
Absolutely! Observability tools can support GDPR compliance by enabling faster incident response automation. If there's a data breach, you need to notify users and authorities within 72 hours. Real-time alerts, telemetry instrumentation, and logs management help your team detect issues quickly, understand the impact, and take action to stay compliant.
How can a data observability tool help when my data is often incomplete or inaccurate?
Great question! If you're constantly dealing with missing values, duplicates, or inconsistent formats, a data observability platform can be a game-changer. It provides real-time metrics and data quality monitoring, so you can detect and fix issues before they impact your reports or decisions.
How does data quality monitoring help improve data reliability?
Data quality monitoring is essential for maintaining trust in your data. A strong observability platform should offer features like anomaly detection, data profiling, and data validation rules. These tools help identify issues early, so you can fix them before they impact downstream analytics. It’s all about making sure your data is accurate, timely, and reliable.
Still have questions?