Clean Data Is the New Shelf Advantage
Unlock accurate inventory management, optimize supply chain, improve sales recommendations, and stop revenue leakage.
Bridging Data and Decisions at Nestlé
How Nestlé aligns business and engineering using Acceldata for analytics reliability —ensuring data trust, accelerating decisions, and scaling insights across global teams.
Maximize ROI Across the Retail & CPG Value Chain
Ensure data accuracy, visibility, and reliability at every step — from sourcing to sale.
Turning Data Challenges Into Business Wins
Dominate with Data
Inventory accuracy achieved
Fewer stockout incidents
Reduction in pipeline downtime
Compliance & audit readiness
Proven Value with Measurable Outcomes
Hershey’s struggled to monitor cost, usage, and performance in their new Snowflake environment—delays in detecting anomalies like query spillages and warehouse timeouts led to weeks of inefficiency and rising expenses.
Acceldata All-in-One Enterprise Data Observability platform gave Hershey’s instant visibility into Snowflake usage and anomalies—eliminating custom tools, cutting manual effort, and enabling faster, proactive cost control.
Anomalies Detection vs several weeks
warehouses successfully onboarded into ADOC
snowflake users effectively managed with ADOC
A global retail giant operating across 2,000+ brands and 30,000+ products, faced post-migration challenges- delayed 50K+ reports, poor data freshness impacting inventory planning, incomplete supply chain data, and uncontrolled cloud costs after moving from SAP to Snowflake.
By deploying Acceldata All-in-One Enterprise Data Observability platform, the company profiled 63K tables, automated 1,000+ data quality checks, and reconciled critical data-boosting BI accuracy and cutting Snowflake costs by 35%.
reduction in cloud computing expenses
improvement in operational efficiency
automated data quality and reconciliation checks
Leverage Points in Retail/CPG Data Operations
Achieve Data Excellence with Acceldata
Accurate Inventory Management
Detect anomalies in incoming data from various distributed inventory management systems in today’s omni-channel retail environments. Identify discrepancies between inventory records and physical stock.
Build accurate demand forecast models to avoid costly stockouts and overstock. Improve data quality, detect and correct for data drift, freshness, consistency and other input parameters that impact your model strength.
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Better Customer Offer ML Models
Deliver fresh good-quality data to your customer uplift models to improve model accuracy. Ensure all aspects of Customer 360 such as demographic data, behavioral data, transactional data, are checked for quality, consistency and accuracy.
Add checks and balances in your data pipelines to deliver the right training dataset and reduce model rework and retraining costs.
Improve Customer Experience
Monitor the quality, consistency and freshness of customer data across your systems to ensure better personalized service and a seamless omni-channel experience.
Continuously monitor the quality of data used to train LLMs and detect issues such as missing values, inconsistencies, and outdated information. Thus provide more accurate and helpful responses from LLMs, enhancing customer satisfaction and lowering operational costs.
Driving Efficiency and Profitability with Data Observability in Retail
Optimize Your Supply Chain, Inventory, Customer Experience, and Revenue with High-Quality Data.
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Optimize Supply Chain
Reconcile data from sources such as suppliers, logistics partners, and distribution centers as they flow through various transformation stages in your data pipelines.
Immediately detect and alert on anomalies or failures in data processing workflows (like slow-running jobs or failures in ETL processes), minimize latency and ensure decision makers have timely and accurate data.
Detect POS Data Anomalies
Get alerted to delays in data synchronization from POS systems to central databases to prevent outdated information and bad decisions. Detect anomalies such as unexpected sales patterns, missing data from specific stores or regions, schema changes, and bad formats. Take immediate corrective actions at the source of anomalies to prevent propagation of errors into critical business processes.
Stop Revenue Leakage
Reconcile data flow between systems and tables, profile specific columns for anomalies and apply other sophisticated checks and balances.
Continuously audit and reconcile sales orders, deliveries, and invoices to prevent invoice discrepancies and bad payments. Identify inconsistencies between these datasets, such as undelivered items that were invoiced or delivered items not billed, to flag potential revenue leakage points.
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