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Prescriptive analytics incorporates both structured and unstructured data, and uses a combination of advanced analytic techniques and disciplines to predict, prescribe, and adapt. While the term prescriptive analytics was first coined by IBM<ref name="LustigEtAl"/> and later trademarked by Ayata,<ref>{{Cite web | url=http://trademarks.justia.com/852/06/prescriptive-analytics-85206495.html | title=PRESCRIPTIVE ANALYTICS Trademark - Registration Number 4032907 - Serial Number 85206495 :: Justia Trademarks}}</ref> the underlying concepts have been around for hundreds of years. The technology behind prescriptive analytics synergistically combines hybrid [[data]], business rules with [[mathematical model]]s and [[computational model]]s. The data inputs to prescriptive analytics may come from multiple sources: internal, such as inside a corporation; and external, also known as environmental data. The data may be structured, which includes numbers and categories, as well as [[unstructured data]], such as texts, images, sounds, and videos. Unstructured data differs from [[structured data]] in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation.<ref>{{cite book|last=Inmon|first=Bill|author2=Nesavich, Anthony|title=Tapping Into Unstructured Data|year=2007|publisher=Prentice-Hall|isbn=978-0-13-236029-6}}</ref> More than 80% of the world's data today is unstructured, according to IBM.
In addition to this variety of data types and growing data volume, incoming data can also evolve with respect to velocity, that is, more data being generated at a faster or a variable pace. Business rules define the [[business process]] and include objectives constraints, preferences, policies, best practices, and boundaries. Mathematical models and computational models are techniques derived from mathematical sciences, computer science and related disciplines such as applied statistics, machine learning, [[operations research]], [[natural language processing]], [[computer vision]], pattern recognition, image processing, [[speech recognition]], and signal processing. The correct application of all these methods and the verification of their results implies the need for resources on a massive scale including human, computational and temporal for every Prescriptive Analytic project. In order to spare the expense of dozens of people, high performance machines and weeks of work one must consider the reduction of resources and therefore a reduction in the accuracy or reliability of the outcome. The preferable route is a reduction that produces a probabilistic result within acceptable limits.{{citation needed|date=May 2020}}
==Applications in Oil and Gas==
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