Prescriptive analytics: Difference between revisions

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Revision as of 04:02, 4 September 2022

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'''Prescriptive analytics''' is the third and final phase of [[business analytics]], which also includes descriptive and [[Predictivepredictive analytics|predictive]] analytics.<ref>{{cite journal|author1=Evans, James R.|author2=Lindner, Carl H. |name-list-style=amp |title=Business Analytics: The Next Frontier for Decision Sciences|journal=Decision Line|date=March 2012|volume=43|issue=2}}</ref><ref name="LustigEtAl">http://www.analytics-magazine.org/november-december-2010/54-the-analytics-journey{{cite journal|last=Lustig, Irv, [[Brenda L. Dietrich|Dietrich, Brenda]], Johnson, Christer, and Dziekan, Christopher|title=The Analytics Journey|journal=Analytics|date=Nov–Dec 2010}}</ref>

Referred to as the "final frontier of analytic capabilities,"<ref>{{Cite web |url=https://www.globys.com/2013/06/gartner-terms-prescriptive-analytics-%E2%80%9Cfinal-frontier%E2%80%9D-analytic-capabilities |title=Archived copy |access-date=2014-10-29 |archive-url=https://web.archive.org/web/20160402140918/http://globys.com/2013/06/gartner-terms-prescriptive-analytics-%E2%80%9Cfinal-frontier%E2%80%9D-analytic-capabilities |archive-date=2016-04-02 |url-status=dead }}</ref> prescriptive analytics entails the application of [[mathematical sciences|mathematical]] and [[computational science]]s and suggests decision options to take advantage of the results of descriptive and predictive analytics. The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today.<ref>{{cite journal|last=Davenport, Tom |title=The three '..tives' of business analytics; predictive, prescriptive and descriptive|journal=CIO Enterprise Forum|date=November 2012}}</ref> Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting – such as [[sales]], [[marketing]], [[Business operations|operations]], and [[finance]] – uses this type of post-mortem analysis.

[[File:Three Phases of Analytics.png|thumb|right|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|last1=Haas|first1=Peter J.|author1-link=Peter J. Haas (computer scientist)|last2=Maglio|first2=Paul P.|last3=Selinger|first3=Patricia G.|author3-link=Patricia Selinger|last4=Tan|first4=Wang-Chie|issue=12|journal=Proceedings of the VLDB Endowment|title=Data is Dead…Without What-If Models|volume=4|year=2011|pages=1486–1489|doi=10.14778/3402755.3402802|s2cid=6239043}}</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. |name-list-style=amp |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 |last1=Riabacke |first1=Mona |last2=Danielson |first2=Mats |last3=Ekenberg |first3=Love |title=State-of-the-Art Prescriptive Criteria Weight Elicitation |journal=Advances in Decision Sciences |date=30 December 2012 |volume=2012 |pages=1–24 |doi=10.1155/2012/276584 |doi-access=free }}</ref>

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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.

One criticism of prescriptive analytics is that its distinction from [[predictive analytics]] is ill-defined and therefore ill-conceived.<ref>{{cite journal|last=Bill Vorhies|url=http://www.predictiveanalyticsworld.com/patimes/prescriptive-versus-predictive-analytics-distinction-without-difference/ |title=Prescriptive versus Predictive Analytics – A Distinction without a Difference?|journal=Predictive Analytics Times|date=November 2014}}</ref> [[File:Components of Prescriptive Analytics.png|thumb|right|The scientific disciplines that comprise Prescriptive Analytics]]

==History==