Analytics has profoundly altered the retail industry. The combination of real-time, predictive analytics—in conjunction with traditional historic analytic capabilities—has reconfigured the way organizations facilitate customer engagement, supply chain management, and inventory in both e-commerce and brick and mortar settings.
Smart (or Semantic) graph databases were specifically designed to accommodate the unique challenges of time-sensitive business intelligence predicated on event data that is critical to this vertical because they readily merge seemingly disconnected data at scale with an efficiency that belies the underlying complexity.
Moreover, when deployed in a Smart Data Lake™ environment, organizations are able to host all of their data in a singular platform that provides enterprise-wide insight to contextualized relationships that are not otherwise apparent, vastly enriching the results.
The advantages of graph databases for retail organizations greatly pertain to the variety of data involved. Architectural concerns frequently include combining historic customer and product data in tandem with real-time data such as event data, geographic data, weather information and more. Smart Data Lakes excel in these settings because they quickly incorporate all types of data—regardless of structure—in a continuously evolving semantic model that works with standardized meanings of terminology. The degree of context facilitated by linked enterprise data enormously impacts analytics results; numerous use cases demonstrate the merits of this approach. Popular ones include recommendation engines that immediately identify cross-selling and upselling opportunities, real-time monitoring of customer activity and product use for service and product development, and proactive maintenance and troubleshooting of systems.
These benefits also extend to physical locations. Real-time, predictive analytics are regularly deployed for sensor data generated by mobile devices for marketing in physical environments, which again requires the merging of historic data with event data. Organizations are utilizing more advanced business intellignce to determine sales trends—by region and according to specific stores—to help them proactively manage their inventory and supply chain. Aggregating this data with point of sale devices facilitates personalized advertisements for customers and a more nuanced approach to inventory management by combining disparate data in a comprehensive Smart Data Lake.
The enduring value of retail analytics in semantic graph databases lies in their ability to encompass disparate sets of data at scale with an innate understanding of the underlying relationships. Smart Data Lakes reinforce these advantages by providing a singular platform to query all data without lengthy preparation procedures.
To learn more about Smart Data Lakes, watch our recorded webinar "Smart Data Lake or Data Landfill? The Difference May Be ‘Semantic’".