Predictive analytics: Difference between revisions

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{{More citations needed|date=June 2011}}

'''Predictive analytics''', or '''predictive AI''', encompasses a variety of [[Statistics|statistical]] techniques from [[data mining]], [[Predictive modelling|predictive modeling]], and [[machine learning]] that analyze current and historical facts to make [[prediction]]s about future or otherwise unknown events.<ref name=":52">{{Cite web |title=To predict or not to Predict |url=https://mccoy-partners.com/updates/to-predict-or-not-to-predict |access-date=2022-05-05 |website=mccoy-partners.com}}</ref>

In business, predictive models exploit [[Pattern detection|patterns]] found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding [[decision-making]] for candidate transactions.<ref>{{Cite book |last=Coker |first=Frank |title=Pulse: Understanding the Vital Signs of Your Business (1st ed.) |___location=Bellevue, WA |publisher=Ambient Light Publishing |year=2014 |isbn=978-0-9893086-0-1 |pages=30, 39, 42, more}}</ref>

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{{generalize-section|date=December 2024}}

Predictive analytics is a set of business intelligence (BI) technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behavior and events. Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future.<ref name=":4">{{Cite web |last=Eckerson |first=Wayne, W |date=2007 |title=Predictive Analytics. Extending the Value of Your Data Warehousing Investment |url=http://download.101com.com/pub/tdwi/files/pa_report_q107_f.pdf}}</ref> Predictive analytics statistical techniques include [[data modeling]], [[machine learning]], [[Artificial intelligence|AI]], [[deep learning]] algorithms and [[data mining]]. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.<ref>{{Cite book |last=Finlay |first=Steven |title=Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods (1st ed.) |publisher=[[Palgrave Macmillan]] |year=2014 |isbn=978-1137379276 |___location=Basingstoke |pages=237 |language=English}}</ref> The core of predictive analytics relies on capturing relationships between [[explanatory variable]]s and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.<ref name=":52" />

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from [[forecasting]]. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions."<ref>{{Cite book |last=Siegel |first=Eric |title=Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (1st ed.) |publisher=[[Wiley (publisher)|Wiley]] |year=2013 |isbn=978-1-1183-5685-2 |language=English}}</ref> In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into [[prescriptive analytics]] for decision optimization.<ref>{{Cite book |last=Spalek |first=Seweryn |title=Data Analytics in Project Management |publisher=Taylor & Francis Group, LLC |year=2019 |language=English}}</ref>

== Analytical techniques ==

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=== Machine learning ===

{{Main|Machine learning}}

Machine learning can be defined as the ability of a machine to learn and then mimic human behavior that requires intelligence. This is accomplished through artificial intelligence, algorithms, and models.<ref>{{Cite web |title=Machine learning, explained |url=https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained |access-date=2022-05-06 |website=MIT Sloan |date=21 April 2021 |language=en}}</ref>

==== Autoregressive Integrated Moving Average (ARIMA) ====

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=== Child protection ===

Some child welfare agencies have started using predictive analytics to flag high risk cases.<ref>{{Cite web |last=Reform |first=Fostering |date=2016-02-03 |title=New Strategies Long Overdue on Measuring Child Welfare Risk |url=https://imprintnews.org/blogger-co-op/new-strategies-long-overdue-measuring-child-welfare-risk/15442 |access-date=2022-05-03 |website=The Imprint |language=en-US}}</ref> For example, in [[Hillsborough County, Florida]], the child welfare agency's use of a predictive modeling tool has prevented abuse-related child deaths in the target population.<ref>{{Cite journal |date=2016 |title=Within Our Reach: A National Strategy to Eliminate Child Abuse and Neglect Fatalities |url=https://www.acf.hhs.gov/sites/default/files/documents/cb/cecanf_final_report.pdf |archive-url=https://web.archive.org/web/20210614092123/https://www.acf.hhs.gov/sites/default/files/documents/cb/cecanf_final_report.pdf |url-status=dead |archive-date=June 14, 2021 |journal=Commission to Eliminate Child Abuse and Neglect Fatalities}}</ref>

=== Predicting outcomes of legal decisions ===

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=== Portfolio, product or economy-level prediction ===

Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. For example, a retailer might be interested in predicting store-level demand for inventory management purposes. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These types of problems can be addressed by predictive analytics using time series techniques (see below). They can also be addressed via machine learning approaches which transform the original time series into a feature vector space, where the learning algorithm finds patterns that have predictive power.<ref>{{Cite journal |last=Dhar |first=Vasant |date=May 6, 2011 |title=Prediction in financial markets: The case for small disjuncts |url=https://dl.acm.org/doi/10.1145/1961189.1961191 |journal=ACM Transactions on Intelligent Systems and Technology |language=en |volume=2 |issue=3 |pages=1–22 |doi=10.1145/1961189.1961191 |s2cid=11213278 |issn=2157-6904|url-access=subscription }}</ref><ref>{{Cite journal |last1=Dhar |first1=Vasant |last2=Chou |first2=Dashin |last3=Provost |first3=Foster |date=2000-10-01 |title=Discovering Interesting Patterns for Investment Decision Making with GLOWER ◯-A Genetic Learner Overlaid with Entropy Reduction |journal=Data Mining and Knowledge Discovery |volume=4 |issue=4 |pages=251–280 |doi=10.1023/A:1009848126475 |s2cid=1982544 |issn=1384-5810}}</ref>

=== Underwriting ===

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* {{cite book |last1=Guidère |first1=Mathieu |last2=Howard |first2=N |last3=Argamon |first3=Sh. |author3-link=Shlomo Argamon |title=Rich Language Analysis for Counterterrorism |___location=Berlin, London, New York |publisher=Springer-Verlag |year=2009 |isbn=978-3-642-01140-5}}

* {{cite book |last=Mitchell |first=Tom |title=Machine Learning |___location=New York |publisher=[[McGraw-Hill]] |year=1997 |isbn=0-07-042807-7}}

* {{cite book |last=Siegel |first=Eric |title=Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die |publisher=[[John Wiley & Sons]] |year=2016 |isbn=978-1119145677}}

* {{cite book |last=Tukey |first=John |title=Exploratory Data Analysis |___location=New York |publisher=Addison-Wesley |year=1977 |isbn=0-201-07616-0 |url-access=registration |url=https://archive.org/details/exploratorydataa00tuke_0}}

{{refend}}

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[[Category:Types of analytics]]

[[Category:Predictive analytics|*]]

[[Category:Management cybernetics]]