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We no longer should worry about “managing data at the speed of business,” but worry more about “managing business at the speed of data.”. One of the primary drivers for the phenomenal growth in dynamic real-time dataanalytics today and in the coming decade is the Internet of Things (IoT) and its sibling the Industrial IoT (IIoT).
When data science was in its “early days” within businesses, the data scientists mostly worked offline with static sources (like databases or web-based reports) to build and test analytics models for potential deployment in the enterprise.
You can ingest and integrate data from multiple Internet of Things (IoT) sensors to get insights. However, you may have to integrate data from multiple IoT sensor devices to derive analytics like equipment health information from all the sensors based on common data elements.
One of the most promising technology areas in this merger that already had a high growth potential and is poised for even more growth is the Data-in-Motion platform called Hortonworks DataFlow (HDF). to generate key insights and actionable intelligence for predictive and prescriptiveanalytics.
The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. 4) Predictive And PrescriptiveAnalytics Tools.
Decades (at least) of business analytics writings have focused on the power, perspicacity, value, and validity in deploying predictive and prescriptiveanalytics for business forecasting and optimization, respectively. How do predictive and prescriptiveanalytics fit into this statistical framework?
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