DATA OBSERVABILITY FOR RETAIL

Maximizing Retail Performance with Data Observability

How top retailers leverage reliable data to drive omnichannel success

The Retail Data Imperative

Modern retailers are navigating an increasingly complex digital landscape—SKU-level transactions, real-time pricing, omnichannel inventory, and customer behavior insights.

Yet, unreliable data leads to blind spots with serious consequences:

The $1.77 Trillion Blind Spot

Overstocks, stockouts, and mismatched demand signal a data crisis. Retailers are losing trillions globally, not because they lack data, but because they can’t trust it. Without visibility into data health, even the most sophisticated inventory systems fail to deliver.

When Data Fails, Inventory Piles Up

Forecasting without reliable, up-to-date inputs leads to costly misfires. One error multiplies across SKUs, stores, and markets. The result? Dead stock, wasted marketing spend, and operational inefficiency on a global scale.

Too Late Is Too Costly

By the time teams notice a broken pipeline or a reporting inconsistency, revenue has already taken a hit, and so has customer trust.
Reactive tools can’t keep up with real-time commerce. What retailers need is a way to spot issues before they cascade.

Meanwhile, retail media networks (like Carrefour & Sainsbury’s) are monetizing clean, actionable data at scale. To stay competitive, retailers must turn their data into a strength.

The Solution: AI-Powered Data Observability

Sifflet empowers retail leaders to detect issues proactively, ensure data reliability, and unlock operational excellence—across every touchpoint.

USE CASE #1

Inventory & Supply Chain Optimization

‍The challenge: Thousands of SKUs. Multiple channels. Constant volatility.

‍The Sifflet edge: Real-time tracking, automated data checks, and anomaly detection help prevent stockouts and costly errors before they impact revenue.

USE CASE #2

Pricing & Promotions Accuracy

‍The challenge: Inconsistent pricing across platforms leads to lost margins and customer frustration.

‍The Sifflet edge: Continuous pricing validation across all systems ensures promotional integrity and customer trust.

USE CASE #3

Omnichannel Customer Experience

‍The challenge: Data silos cause fragmented profiles and disconnected experiences.

‍The Sifflet edge: A unified view of customer data enables personalization and stronger loyalty programs.

USE CASE #4

AI-Powered Demand Forecasting

‍The challenge: Outdated forecasting models miss real-world volatility.

‍The Sifflet edge: ML learns from historical sales, competitor pricing, and external signals to fine-tune demand planning.

Proactive Data Reliability at Scale

ML-powered, event-driven observability detects issues before they impact revenue, ensuring real-time reliability across thousands of pipelines, even in complex enterprise environments.

Seamless Integration Across Your Retail Stack

Sifflet connects effortlessly with your ERP, POS, CRM, e-commerce, and analytics tools, breaking down data silos and enabling a unified view across all operations.

Empowering Every Team: from Data to Business

Designed for both technical and non-technical users, Sifflet transforms raw data into clear, actionable insights, so your teams can make smarter decisions, faster.

Looking for more?

How Carrefour Transformed Data Quality & Efficiency Across 8 Countries with Sifflet

Discover how Carrefour managed to cover over 800+ data assets, see 80% efficiency gains, and streamlined their operations with Sifflet.

Retailers Are Deploying Data Observability to Avoid Revenue Loss.

For retailers, getting an accurate picture of inventory and sales performance has become harder than ever. With millions of dollars in revenue at stake, more and more of them are turning to data observability to help match customer demand to stock.

How they do it

Hypebeast x Sifflet: Revolutionizing Fashion with Data Observability

How Hypebeast improved data quality, improved efficiency and strengthened collaboration by implementing Sifflet.

Tell me more

Let’s fix the $9.7B problem before it’s yours.

Retail data shouldn’t be a liability. With Sifflet, it’s your secret weapon.
Say goodbye to guesswork: say hello to reliable insights.

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 data observability differ from traditional data quality monitoring?

Great question! While data quality monitoring focuses on detecting when data doesn't meet expected thresholds, data observability goes further. It continuously collects signals like metrics, metadata, and lineage to provide context and root cause analysis when issues arise. Essentially, observability helps you not only detect anomalies but also understand and fix them faster, making it a more proactive and scalable approach.

Can open-source ETL tools support data observability needs?

Yes, many open-source ETL tools like Airbyte or Talend can be extended to support observability features. By integrating them with a cloud data observability platform like Sifflet, you can add layers of telemetry instrumentation, anomaly detection, and alerting. This ensures your open-source stack remains robust, reliable, and ready for scale.

What makes Carrefour’s approach to observability scalable and effective?

Carrefour’s approach combines no-code self-service tools with as-code automation, making it easy for both technical and non-technical users to adopt. This balance, along with incremental implementation and cultural emphasis on data quality, supports scalable observability across the organization.

Why are data consumers becoming more involved in observability decisions?

We’re seeing a big shift where data consumers—like analysts and business users—are finally getting a seat at the table. That’s because data observability impacts everyone, not just engineers. When trust in data is operationalized, it boosts confidence across the business and turns data teams into value creators.

Can Sifflet help with data quality monitoring directly from the Data Catalog?

Absolutely! Sifflet integrates data quality monitoring into its Data Catalog, allowing users to define and view data quality checks right alongside asset metadata. This gives teams real-time insights into data reliability and helps build trust in the assets they’re using for decision-making.

Why is data observability essential for building trusted data products?

Great question! Data observability is key because it helps ensure your data is reliable, transparent, and consistent. When you proactively monitor your data with an observability platform like Sifflet, you can catch issues early, maintain trust with your data consumers, and keep your data products running smoothly.

Why is an observability layer essential in the modern data stack, according to Meero’s experience?

For Meero, having an observability layer like Sifflet was crucial to ensure end-to-end visibility of their data pipelines. It allowed them to proactively monitor data quality, reduce downtime, and maintain SLA compliance, making it an indispensable part of their modern data stack.

What should I look for in a data lineage tool?

When choosing a data lineage tool, look for easy integration with your data stack, a user-friendly interface for both technical and non-technical users, and complete visibility from data sources to storage. These features ensure effective data observability and support your broader data governance efforts.

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Still have questions?