Prescriptive analytics: Difference between revisions

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{{Cleanup bare URLs|date=September 2022}}

{{short description|Form of business analytics offering future decision options}}

'''Prescriptive analytics''' is a form of [[business analytics]] which suggests decision options for how to take advantage of a future opportunity or mitigate a future risk, and shows the implication of each decision option. It enables an enterprise to consider "the best course of action to take" in the light of information derived from [[Descriptive statistics|descriptive]] and [[predictive analytics]].<ref name="basu">{{cite journal |last=Basu, |first=Atanu, [|year=2019 |title=Five pillars of prescriptive analytics success |url=https://pubsonline.informs.org/do/10.1287/LYTX.2013.02.07/full/ ''Five|journal=The pillars of prescriptive analytics success''], ''Analytics'', MarchJourney |doi=10.1287/ April LYTX.2013,.02.07 accessed 3 December|s2cid=240957300|url-access=subscription 2022}}</ref>

==Overview==

Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and 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:0">http://www.analytics-magazine.org/november-december-2010/54-the-analytics-journey{{citeCite journal |lastlast1=Lustig,Basu Irv,|first1=Atanu [[Brenda|last2=Brown L.|first2=Scott Dietrich|Dietrich,last3=Worth Brenda]],|first3=Tim Johnson,|date=2019-10-25 Christer,|title=Predictive andanalytics Dziekan,in field service Christopher|titleurl=http://pubsonline.informs.org/do/10.1287/LYTX.2010.06.03/full/ |journal=The Analytics Journey |journallanguage=Analyticsen |datedoi=Nov–Dec 10.1287/lytx.2010.06.03|s2cid=242347282 |url-access=subscription }}</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=ArchivedGartner copyterms Prescriptive Analytics as the "Final Frontier" of Analytic Capabilities &#124; Globys.com |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 for how to take advantage of the results of descriptive and predictive phases.

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 of what is likely to happen. This is where 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>

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|doi-access=free}}</ref> which goes beyond predicting future outcomes but 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 |year=1987 |title=Descriptive and Prescriptive Models for Judgment and Decision Making: Implications for Knowledge Engineering |journal=Expert Judgment and Expert Systems |volume=NATO AS1 Subseries 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 incorporates both [[structured data|structured]] and [[unstructured data]], and uses a combination of advanced analytic techniques and disciplines to predict, prescribe, and adapt. It 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. Effective prescriptive analytics utilises 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> Basu suggests that without hybrid data input, the benefits of prescriptive analytics are limited.<ref name=basu />{{efn|Atanu Basu is the CEO and president of Ayata.<ref name=basu />}}

Prescriptive analytics notuses onlyalgorithms anticipatesand whatmachine willlearning happenmodels andto whensimulate itvarious willscenarios happen,and butpredict alsothe whylikely itoutcomes willof happendifferent decisions.<!ref name=":1">{{Citation |last1=Soltanpoor |first1=Reza |title=Prescriptive Analytics for Big Data |date=2016 |url=http://link.springer.com/10.1007/978-3-319-46922-5_19 There|work=Databases isTheory noand evidenceApplications nor|volume=9877 citation|pages=245–256 supporting|editor-last=Cheema this|editor-first=Muhammad statementAamir |access-date=2023-05-01 |place=Cham |publisher=Springer International Publishing |doi=10.1007/978-3-319-46922-5_19 Furthermore,|isbn=978-3-319-46921-8 if|last2=Sellis prescriptive|first2=Timos analytics|series=Lecture Notes in Computer Science |editor2-last=Zhang |editor2-first=Wenjie |editor3-last=Chang |editor3-first=Lijun|url-access=subscription }}</ref> It then suggests the best course of action based on the desired outcome and the constraints of the situation. Prescriptive analytics "not only anticipates what will happen and when it will happen, but also why it will happen,.<ref thenname=":1" 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 incorporates both [[structured data|structured]] and [[unstructured data]], and uses a combination of advanced analytic techniques and disciplines to predict, prescribe, and adapt. It 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. Effective prescriptive analytics utilises 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> Basu suggests that without hybrid data input, the benefits of prescriptive analytics are limited.<ref name="basu" />{{efn|Atanu Basu is the CEO and president of Ayata.<ref name=basu />}}

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}}

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==History==

While the term prescriptive analytics was first coined by [[IBM]],<ref name="LustigEtAl:0" /> and was later [[trademark]]ed by Texas-based company Ayata,<ref>[https://ayata.com/ Ayata], accessed 4 December 2022</ref><ref name=tm>{{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.<ref>{{cnCite web |date=December2012-03-07 2022|title=IBM100 - TAKMI: Bringing Order to Unstructured Data |url=http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/takmi/ |archive-url=https://web.archive.org/web/20120403013240/http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/takmi/ |url-status=dead |archive-date=April 3, 2012 |access-date=2023-05-01 |website=www-03.ibm.com |language=en-US}}</ref>

Ayata's trade mark was cancelled in 2018.<ref name=tm />

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===Oilfield Equipment Maintenance===

In the realm of oilfield equipment maintenance, Prescriptive Analytics can optimize configuration, anticipate and prevent unplanned downtime, optimize field scheduling, and improve maintenance planning.<ref>{{cite journal |last=Presley, Jennifer |title=ESP for ESPs |journal=Exploration & Production |date=July 1, 2013}}</ref> According to [[General Electric]], there are more than 130,000 electric submersible pumps (ESP's) installed globally, accounting for 60% of the world's oil production.<ref>{{cite web | url=http://www.ge-energy.com/products_and_services/products/electric_submersible_pumping_systems/ | title=Electric Submersible Pumping Systems &#124; GE Energy }}</ref> Prescriptive Analytics has been deployed to predict when and why an ESP will fail, and recommend the necessary actions to prevent the failure.<ref>{{cite journal |last=Wheatley, Malcolm |title=Underground Analytics |journal=DataInformed |date=May 29, 2013}}</ref>

In the area of [[health, safety and environment]], prescriptive analytics can predict and preempt incidents that can lead to reputational and financial loss for oil and gas companies.

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* [https://pubsonline.informs.org/magazine/analytics INFORMS' bi-monthly, digital magazine on the analytics profession]

* Menon, Jai [https://www.youtube.com/watch?v=VtETirgVn9c "Why Data Matters: Moving Beyond Prediction"] IBM

* [https://www.gopeaks.org/ Global Openlabs for Performance-Enhancement Analytics and Knowledge System (GoPeaks)]

[[Category:Types of analytics]]

[[Category:Big data|analytics]]

[[Category:Business intelligence terms]]

[[Category:Business terms]]

[[Category:Formal sciences]]

[[Category:Health care]]