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Retailers Improve Performance Using Event Stream Data

Streaming data is transforming how retailers operate. Retail’s evolution from brick-and-mortar to omnichannel has provided organizations with better information about customer behavior while also increasing the expectations of consumers regarding their customer experience. Online and mobile applications have become the norm and shoppers now expect real-time information about inventory levels and prices along with personalized recommendations and offers. Streaming data provides operations managers at both the brand level and store level real-time visibility into the supply chain and it can help detect and prevent inventory shrinkage. It can also be used to tie together information from the legacy systems that are needed to make many of these capabilities possible, but without the significant investment that would be needed to rewrite these systems.

Our research shows that more than nine in ten organizations (93%) consider it important to speed the flow of information and improve the responsiveness of their organizations. Even just a few years ago, capturing and evaluating this information quickly was much more challenging, but with the advent of streaming data technologies that capture and process large volumes of data in real time, organizations can quickly turn events into valuable information that improves customer experiences and the bottom line. The most common use of event streams identified in our research is for customerrelated information and processes (43%) and nearly one-third (31%) of organizations are using event streams from their supply chains.


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About the Author


David Menninger

SVP and Research Director
Ventana Research

David Menninger is responsible for the overall direction of research on data and analytics technologies at Ventana Research. He covers major areas including artificial learning and machine learning, big data, business intelligence, collaboration, data science and information management along with the additional specific research categories including blockchain, data governance, data lakes, data preparation, embedded analytics, natural language processing (NLP) and IoT.