Pushing event analytics to the edge


In my last post, we examined the growing importance of event stream processing to predictive and prescriptive analytics. In the example we discussed, we looked at how all the event streams from point-of-sale systems from multiple retail locations are absorbed at a centralized point for analysis. Yet the beneficiaries of those analytic results are not limited to central administrators. Yes, it is true that real-time event data can influence enterprise-wide forecasting and planning. But in essence, the more immediate opportunities for value occur at the warehouses, the logistics managers and the retail sites – not at the hub, per se, but rather the edges.

This presents a slightly different model for event stream processing that layers the analyses based on the type of value that can be derived at different points in the networked enterprise. Streaming event data within the retail location can be analyzed for real-time restocking. Streaming event data across regional stores can help identify warehouse efficiencies and streamline regional inventory management. Streaming traffic and weather data within the region will help reduce delivery costs yet improve timely delivery. And accumulating all the data at the centralized location feeds forecasting and planning for the entire organization.

White paper cover for Understanding Data Streams in IoT
Learn how to use event stream processing to manage the Internet of Things.

In this paradigm, event stream processing should not be limited to the centralized point. Rather, a hierarchy of the network should be modeled to identify two key concepts: where there is potential for benefits from localized analysis, and where that analysis can take place. I believe that in most scenarios, there will be sufficient computing power available at each level in the hierarchy for the event stream processing and analytics to be pushed to that level – even (and maybe especially) at the point of data generation itself.

This suggests that from a technology perspective there is value in integrating an event stream processing engine at every point in the enterprise network. And this brings us somewhat full-circle in relation to what I mentioned at the beginning of my previous post: The emerging value of the Internet of Things. This will be the topic of the my next (and third) post in this series.


About Author

David Loshin

President, Knowledge Integrity, Inc.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at b-eye-network.com and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at mdmbook.com . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at loshin@knowledge-integrity.com.

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