Prescriptive analytics: Difference between revisions

Revision as of 16:17, 10 May 2020

Revision as of 16:18, 10 May 2020

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[[File:Three Phases of Analytics.png|thumb|left|350px|Prescriptive Analytics extends beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes, and the interrelated effects of each decision]]The next phase is [[predictive analytics]]. Predictive analytics answers the question what is likely to happen. This is when historical data is combined with rules, [[algorithms]], and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. The final phase is prescriptive analytics,<ref>{{cite journal|last=Haas, Peter J., Maglio, Paul P., Selinger, Patricia G., and Tan, Wang-Chie|title=Data is Dead…Without What-If Models|journal=Proceedings of the VLDB Endowment|year=2011|volume=4|number=12}}</ref> which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option.<ref>{{cite journal|author1=Stewart, Thomas. R. |author2=McMillan, Claude, Jr. |lastauthoramp=yes |title=Descriptive and Prescriptive Models for Judgment and Decision Making: Implications for Knowledge Engineering|journal=NATO AS1 Senes, Expert Judgment and Expert Systems|year=1987|volume=F35|pages=314–318}}</ref>

Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen<!-- There is no evidence nor citation supporting this statement. Furthermore, if prescriptive analytics analytics "not only anticipates what will happen and when it will happen, but also why it will happen, then what is the role of predictive modelling, forecasting and causal modelling? -->. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy<!-- This lacks evidence and supporting citation. It does not follow that prediction accuracy improves as a result of re-predicting. --> and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.<ref>{{cite journal|last=Riabacke, Mona, Danielson, Mats, and Ekenber, Love |title=State-of-the-Art Prescriptive Criteria Weight Elicitation|journal=Advances in Decision Sciences|year=2012|volume=2012|pages=1–24|doi=10.1155/2012/276584}}</ref>

All three phases of analytics can be performed through professional services or technology or a combination. In order to scale, prescriptive analytics technologies need to be adaptive to take into account the growing volume, velocity, and variety of data that most mission critical processes and their environments may produce.

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==History==

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.