Pacsun is engaging in real-time, data-driven interactions with customers.
Published March 12, 2024
While early science fiction shows like “Buck Rogers” (1939) and “The Fly” (1950) depicted teleportation technology, it was Star Trek’s transporter room that made real-time living matter transfer a classical sci-fi trope. While we haven’t built technology that enables real-time matter transfer yet, modern science is pursuing concepts like superposition and quantum teleportation to facilitate information transfer across any distance at speeds faster than light. Thanks, Albert Einstein!
No need to wait for these future technologies to arrive. Data practitioners today are already using real-time data pipelines to enable a broad set of use cases ranging from website optimization to reactive and predictive fulfillment and delivery routing. Modern data flows including iPaaS and ETL services can achieve millisecond latencies, moving useful data into downstream apps almost instantaneously. The advent of generative AI is massively increasing the uses and value of real-time data for predictive software applications and analytics.
From Batch Processing to Streaming
Batch processing of data is the established paradigm – a function of practical limits on storage and processing power dating back to punch card computing. With the advent of cloud computing, moving from batch to real-time or “in-stream” processing has become practical and even affordable. Data streaming is now a driver of new business capabilities and a source of competitive advantage. Real-time data streaming can enable businesses to optimize decisions and actions in seconds rather than minutes, hours, or days.
Shifting from batch to real-time streaming data transfer can serve to unify disparate and potentially redundant data flows that previously served operational (e.g., payment processing) and analytical (e.g., BI dashboard) work. In the retail industry, applications for real-time data range from responding instantly to shopper behavior to flagging and resolving operational exceptions as they occur.
Read the full article on the DataVersity.