Prescriptive analytics: Difference between revisions

Revision as of 06:21, 27 May 2020

Revision as of 23:35, 1 June 2020

Line 1:

{{Multiple issues|

{{refimprove|date=June 2012}}

{{notability|date=June 2012}}

}}

'''Prescriptive analytics''' is the third and final phase of [[business analytics]], which also includes descriptive and [[Predictive analytics|predictive]] analytics.<ref>{{cite journal|author1=Evans, James R.|author2=Lindner, Carl H. |lastauthoramp=yes |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|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|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}}</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 |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 }}</ref>

Line 17 ⟶ 12:

==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.

Line 23 ⟶ 17:

==Applications in Oil and Gas==

[[File:Key Questions Prescriptive Analytics software answers for oil and gas producers.png|thumb|right|450px|Key Questions Prescriptive Analytics software answers for oil and gas producers]]Energy is the largest industry in the world ($6 trillion in size). The processes and decisions related to oil and natural gas exploration, development and production generate large amounts of data. Many types of captured data are used to create models and images of the Earth’s structure and layers 5,000 - 35,000 feet below the surface and to describe activities around the wells themselves, such as depositional characteristics, machinery performance, oil flow rates, reservoir temperatures and pressures.<ref>{{cite journal|last= Basu, Atanu|title= How Prescriptive Analytics Can Reshape Fracking in Oil and Gas Fields|journal= Data-Informed|date=November 2012}}</ref> Prescriptive analytics software can help with both locating and producing hydrocarbons<ref>{{cite journal|last= Basu, Atanu |title= How Data Analytics Can Help Frackers Find Oil |journal= Datanami|date=December 2013}}</ref> by taking in seismic data, well log data, production data, and other related data sets to prescribe specific recipes for how and where to drill, complete, and produce wells in order to optimize recovery, minimize cost, and reduce environmental footprint.<ref>{{cite journal|last= Mohan, Daniel |title= Machines Prescribing Recipes from 'Things,' Earth, and People |journal =Oil & Gas Investor|date=August 2014}}</ref>

===Unconventional Resource Development===

[[File:Varied datasets.png|thumb|right|450px|Examples of structured and unstructured data sets generated and by the oil and gas companies and their ecosystem of service providers that can be analyzed together using Prescriptive Analytics software]]With the value of the end product determined by global commodity economics, the basis of competition for operators in upstream E&P is the ability to effectively deploy capital to locate and extract resources more efficiently, effectively, predictably, and safely than their peers. In unconventional resource plays, operational efficiency and effectiveness is diminished by reservoir inconsistencies, and decision-making impaired by high degrees of uncertainty. These challenges manifest themselves in the form of low recovery factors and wide performance variations.