Data Observability For Manufacturing
Achieve precise reporting, gain an accurate customer view, enhance customer models, maximize revenue, and more.
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Leverage Points in Manufacturing Data Operations
Achieve Data Excellence with Acceldata
Accurate Inventory Management
Detect anomalies in incoming data from various distributed inventory management systems, retailers, and distributors. Identify discrepancies between inventory records and physical stock.
Build accurate demand forecast models to avoid costly out-of-stock, shelf out-of-stock and excess inventory. 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 Sales Recommendation Engine
Ensure your Sales Recommendation Engine operates on accurate and complete data. Identify data processing discrepancies across your systems and proactively detect anomalies, such as low volume on incoming files, partial/incorrect data, missing data, deviations in POS information, and other low-fidelity signals that can impact your SRE and lead to revenue loss.
Optimize Supply Chain
Reconcile data from sources such as logistics partners, distribution centers, and receiving docks as they flow through various 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.
Efficient Energy Management/ESG Reports
Track data quality, data anomalies, and data lineage to ensure energy consumption data is accurate and consistent as it flows from sensors to analytics platforms.
Continuously monitor for issues like data drift, missing data, schema drift, and data inconsistencies from disparate data sources that feed into ESG reporting pipelines.
Maintain lineage of emissions data from sensors to the final ESG reports, ensuring transparency and compliance with environmental regulations.
Sensor/IoT Data Analytics
Identify and flag sensor data anomalies, such as sudden spikes or drops in readings. Leverage AI based anomaly detection to compare incoming data distributions against historical baselines.
Track data flow and processing times throughout the pipeline, identifying performance bottlenecks or delays due to network congestion and other factors.
Cloud Migration
Reconcile data as it moves from SAP Hana to CSV/Parquet to Snowflake Staging Area to Snowflake tables. Supports both bulk and incremental upload strategies.
Optionally apply quality checks, identify duplicates, format errors and such as per your migration strategy.
Post migration, optimize Snowflake query performance, storage, and compute resources to maximize ROI on Snowflake spend.
Ready to start your
data observability journey?
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