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A prominent U.S.-based fast-moving consumer goods (FMCG) company, specializing in packaged foods, faced challenges in integrating real-time data streams into their existing data warehouse structured in a traditional relational database. The data included sales transactions, supply chain updates, and customer feedback collected across various points of sale and online platforms.
They’re goal was to enable real-time decisioning by dynamically integrating diverse data streams into the data warehouse, resulting in operational efficiency and agility to market dynamics.
Implementation of Gen AI solutions on Azure. Azure services included Azure Data Factory (for data orchestration), Azure Databricks (running GenAI models and data processing) and Azure Synapse Analytics: (data warehouse). GenAI models were developed using Databricks to dynamically interpret incoming data streams and automate schema mapping in Synapse Analytics, where new dimensions are created or modified real-time as new data types are introduced.
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- Performance improvement Data availability reduced from hours to minutes and query performance improved by 50% due to more efficient data structures.
- Improved Decision Making Real-time insights permitted rapid response to market demands and supply chain challenges.
- Financial Impact Estimated 20% increase in operational efficiency, reducing costs associated with delayed responses and stock inefficiencies.
Challenges
1. Latency
2. Inflexibility
3. Data freshness
Benefits
1. Data availability near real-time
2. Improve process efficiency
The adoption of GenAI for dynamic data integration in Microsoft Azure transformed the company’s ability to act on real-time data, significantly enhancing operational efficiencies and market responsiveness. Improving technical capabilities of data handling resulted in financial gains through better resource management and customer satisfaction.
Infusing traditional operating models and legacy technologies with Gen AI, increases productivity while reducing costs .