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This in turn is followed by a discussion of leveraging clinical big data for analytical advantages and highlighting the potential importance of prescriptive analytics with potential applications that might arise from these types of analyses.
The common types of big data analytics include predictive, diagnostic, descriptive, and prescriptive analytics.
In contrast, the lack of prescriptive analytics is evident from the results.
Furthermore, business analytics extends to include predictive and prescriptive analytics to generate responsive action plans.
Yehiav believes the challenge of sifting through IoT data can be overcome with prescriptive analytics.
Yehiav describes prescriptive analytics in three easy steps: "Step one, collect the data.
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With predictive and prescriptive crisis analytics, we can obtain accurate advance information on developing disasters in an easy-to-understand and reliable way, such that the affected community can accept and act on the data.
In my opinion the combined use of structured and unstructured data is the only viable path towards development of predictive and prescriptive uses of analytics.
A secondary objective is to highlight the potential benefits of predictive and prescriptive clinical big data analytics.
In clear prose, Corburn provides a "descriptive, analytic, and prescriptive understanding of local environmental-health knowledge" through what he calls "street science" (p. 217).
Finally, there is prescriptive models which are the high level of analytics in smart grid, because they affect marketing, engagement strategies and the decisions to make [22].
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