Realtime Data Analytics (Big Data), Strategy, Approach & Implementation
Abstract
In order to provide nearly instantaneous insights, real-time data analytics processes and analyses data as it comes in. It necessitates a systematic approach that includes setting goals, locating data sources, and picking relevant technology. In particular, this study looks at how real-time data analytics may improve inventory tracking, lower stockout rates, and lessen overstock scenarios in US retail chains. Quantitative data from 100 retail chains were evaluated using a mixed-methods study approach, both before and after the use of analytics tools like AWS Kinesis and Apache Kafka. Important indicators such as the frequency of stockouts, instances of overstocking, and inventory turnover rates demonstrated notable improvements: overstocking decreased by 66.7%, stockouts decreased by 60%, and inventory turnover rates increased by 50%. Variations in processing speed, data accuracy, scalability, and user satisfaction were found when analytics solutions were compared.
Copyright (c) 2018 Ramakumar Soundarapandian

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