For many years companies have been building data warehouses and data marts for reporting and analysis with only a handful of professionals doing data mining and statistical analysis. However, the arrival of big data shone a spotlight on predictive and advanced and we are now at the point where they are considered strategic the boardroom. Today data science is mainstream but still mostly focused on machine learning on data stored centrally. However the demand to analyse low latency streaming data and the emergence of the Internet of Things (IoT) is leading many to ask why is it that we have to analyse all data at the centre? Why not at the edge, closer to where the data is being generated? With so much data being generated and much more to come, would pushing analytics into the network not scale better? This session looks at why companies now need to develop models and rules centrally but deploy them anywhere all the way out into the network. It looks at why edge analytics is fundamental to being able to scale to manage IoT and how streaming data and distributed execution of an integrated suite of analytics can enable the ‘always on’ intelligent business.
The explosion of data and things that are emitting it
Prevention and opportunity - use cases for streaming analytics
Why do we have to move all data to the centre before analyzing it?
Fast data and fast action edge analytics - develop centrally and deploy anywhere
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