From Batch Analytics To Real-Time Decision Intelligence An Event-Driven Architecture For Modern Retail Enterprises

Abstract

Retail analytics has historically been organized around periodic data movement: transactions are collected, transformed in scheduled pipelines, aggregated into warehouses, and surfaced through reports or dashboards. This model remains appropriate for many strategic and financial workloads, but it becomes increasingly inadequate when the economic value of a decision decays within seconds or minutes. Stock-outs, digital-session behavior, fraud signals, fulfillment exceptions, pricing anomalies, inventory movements, and delivery disruptions often require action before the next batch cycle completes.

This article proposes a Retail Event-to-Decision Architecture (REDA) for moving from batch-centric analytics toward governed real-time decision intelligence. REDA separates six capabilities: event generation, stream processing, real-time context, decisioning, action orchestration, and outcome learning. The article introduces a Decision Latency Budget (DLB) to determine the maximum end-to-end delay a use case can tolerate, and a Decision Half-Life concept to prevent the common architectural mistake of streaming every workload regardless of economic value.

The framework argues that modern retail architecture should not replace batch analytics with streaming. It should unify batch and event processing around shared business semantics and data products, while placing each decision on the lowest-latency path justified by its time value. Current 2026 platform developments—including continuous queries, reverse ETL to event buses, unified real-time intelligence, and increasing adoption of streaming for agentic AI—make this architecture increasingly practical. The paper concludes that the competitive advantage of real-time retail does not come from processing events faster in isolation; it comes from shortening the complete path from business signal to trusted action and measured outcome.