m Predictive analytics involves using statistical techniques and machine learning algorithms to analyze historical data and make forecasts about future events. The risks include data privacy issues, potential biases in data leading to inaccurate predictions, and over - reliance on automated systems<ref></ref>
Predictive analytics isinvolves ausing setstatistical oftechniques [[businessand intelligence]]machine (BI)learning technologiesalgorithms thatto uncoversanalyze relationshipshistorical data and patternsmake withinforecasts largeabout volumesfuture ofevents. dataThe thatrisks caninclude bedata usedprivacy toissues, predictpotential behaviorbiases andin events.data Unlikeleading otherto BIinaccurate technologiespredictions, predictiveand analyticsover is forward-looking,usingreliance paston eventsautomated tosystems. anticipateExtending the future.Value of Your Data Warehousing Investment<ref name=":4">{{Citecite web |lastlast1=EckersonSingh |firstfirst1=Wayne,Mayurendra WPratap |datetitle=2007Mr |titleurl=Predictive Analyticshttps://thecodework.com/data-engineering-and-analytics/ Extending|website=TheCodeWork the|access-date=4 ValueNovember of Your Data Warehousing Investment2024}}</ref>. |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 name="Siegel 2013"/> 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>
