Data Observability
For Retail
Reconcile POS & Inventory to eliminate stock outs, deliver better customer offer models with superior data quality
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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monitor the quality of data used to train LLMs and detect issues such as missing values, inconsistencies, and outdated information.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.
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Get alerted to delays in data synchronization from POS systems to central databases to prevent outdated information and bad decisions.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.
Ready to start your
data observability journey?
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