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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|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 uses algorithms and machine learning models to simulate various scenarios and predict the likely outcomes of different decisions.<ref name=":1">{{Citation |last=Soltanpoor |first=Reza |title=Prescriptive Analytics for Big Data |date=2016 |url=http://link.springer.com/10.1007/978-3-319-46922-5_19 |work=Databases Theory and Applications |volume=9877 |pages=245–256 |editor-last=Cheema |editor-first=Muhammad Aamir |access-date=2023-05-01 |place=Cham |publisher=Springer International Publishing |doi=10.1007/978-3-319-46922-5_19 |isbn=978-3-319-46921-8 |last2=Sellis |first2=Timos |editor2-last=Zhang |editor2-first=Wenjie |editor3-last=Chang |editor3-first=Lijun}}</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 name=":1" /> 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 />}}
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