Prescriptive Analytics - Revision history

BPeat at 20:36, 5 January 2026

2026-01-05T20:36:22Z

BPeat at 16:04, 3 September 2023

2023-09-03T16:04:49Z

BPeat at 16:02, 3 September 2023

2023-09-03T16:02:34Z

BPeat: /* Generative AI & Prescriptive Analytics */

2023-07-10T19:03:57Z

‎Generative AI & Prescriptive Analytics

BPeat at 19:00, 10 July 2023

2023-07-10T19:00:17Z

BPeat at 18:55, 10 July 2023

2023-07-10T18:55:34Z

BPeat: /* Use Cases */

2023-07-10T18:42:01Z

‎Use Cases

BPeat: /* Use Cases */

2023-07-10T18:35:28Z

‎Use Cases

BPeat: /* Use Cases */

2023-07-10T18:33:58Z

‎Use Cases

BPeat at 18:28, 10 July 2023

2023-07-10T18:28:37Z

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 * [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Operations]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
-* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]\
+* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
 * [https://www.sas.com/en_us/insights/analytics/prescriptive-analytics.html Prescriptive Analytics | SAS]
 * [https://www.ibm.com/analytics/prescriptive-analytics Prescriptive Analytics | IBM]

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=ai+Prescriptive+analytics&qft=interval%3d%228%22 ...Bing News]
-* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]]
+* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Operations]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]\

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=ai+Prescriptive+analytics&qft=interval%3d%228%22 ...Bing News]
-* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
+* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]\

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 * <b>Improving predictive models</b>: augment existing data sets by generating additional samples, which can help improve the accuracy and generalizability of predictive models used in prescriptive analytics.
 * <b>Personalization</b>: create personalized recommendations or actions for individual users or customers. By learning the preferences and behaviors of users, generative models can generate tailored solutions that are more likely to be effective and lead to desired outcomes.
 <youtube>piyP6J6SEjI</youtube>

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 [[Generative AI]] can be used in prescriptive analytics to create potential solutions, optimize decision-making, and simulate various scenarios. Prescriptive analytics is the process of using data and analytics techniques to recommend actions that can lead to desired outcomes or optimize specific objectives. [[Generative AI]] models can generate new data samples based on the patterns and structures learned from existing data. Here are some ways [[generative AI]] can be used in prescriptive analytics:
-* <b>Generating potential solutions</b>: create a range of potential solutions for a given problem. These solutions can then be evaluated and ranked based on their effectiveness in achieving the desired outcome, allowing decision-makers to choose the best course of action[3].
+* <b>Generating potential solutions</b>: create a range of potential solutions for a given problem. These solutions can then be evaluated and ranked based on their effectiveness in achieving the desired outcome, allowing decision-makers to choose the best course of action.
-* <b>Optimizing decision-making</b>: explore the solution space of complex problems and identify optimal or near-optimal solutions. By simulating various scenarios and evaluating their outcomes, generative AI can help decision-makers make more informed choices and optimize their decisions[4].
+* <b>Optimizing decision-making</b>: explore the solution space of complex problems and identify optimal or near-optimal solutions. By simulating various scenarios and evaluating their outcomes, generative AI can help decision-makers make more informed choices and optimize their decisions.
-* <b>Simulating scenarios</b>: generate synthetic data that represents different scenarios or conditions. This data can then be used to test and validate prescriptive analytics models, enabling organizations to better understand the potential impact of their decisions under various circumstances[5].
+* <b>Simulating scenarios</b>: generate synthetic data that represents different scenarios or conditions. This data can then be used to test and validate prescriptive analytics models, enabling organizations to better understand the potential impact of their decisions under various circumstances.
-* <b>Improving predictive models</b>: augment existing data sets by generating additional samples, which can help improve the accuracy and generalizability of predictive models used in prescriptive analytics[6].
+* <b>Improving predictive models</b>: augment existing data sets by generating additional samples, which can help improve the accuracy and generalizability of predictive models used in prescriptive analytics.
-* <b>Personalization</b>: create personalized recommendations or actions for individual users or customers. By learning the preferences and behaviors of users, generative models can generate tailored solutions that are more likely to be effective and lead to desired outcomes[7].
+* <b>Personalization</b>: create personalized recommendations or actions for individual users or customers. By learning the preferences and behaviors of users, generative models can generate tailored solutions that are more likely to be effective and lead to desired outcomes.

← Older revision		Revision as of 18:55, 10 July 2023
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		+	= Generative AI & Prescriptive Analytics =
		+	[[Generative AI]] can be used in prescriptive analytics to create potential solutions, optimize decision-making, and simulate various scenarios. Prescriptive analytics is the process of using data and analytics techniques to recommend actions that can lead to desired outcomes or optimize specific objectives. [[Generative AI]] models can generate new data samples based on the patterns and structures learned from existing data. Here are some ways [[generative AI]] can be used in prescriptive analytics:
		+
		+	* <b>Generating potential solutions</b>: create a range of potential solutions for a given problem. These solutions can then be evaluated and ranked based on their effectiveness in achieving the desired outcome, allowing decision-makers to choose the best course of action[3].
		+	* <b>Optimizing decision-making</b>: explore the solution space of complex problems and identify optimal or near-optimal solutions. By simulating various scenarios and evaluating their outcomes, generative AI can help decision-makers make more informed choices and optimize their decisions[4].
		+	* <b>Simulating scenarios</b>: generate synthetic data that represents different scenarios or conditions. This data can then be used to test and validate prescriptive analytics models, enabling organizations to better understand the potential impact of their decisions under various circumstances[5].
		+	* <b>Improving predictive models</b>: augment existing data sets by generating additional samples, which can help improve the accuracy and generalizability of predictive models used in prescriptive analytics[6].
		+	* <b>Personalization</b>: create personalized recommendations or actions for individual users or customers. By learning the preferences and behaviors of users, generative models can generate tailored solutions that are more likely to be effective and lead to desired outcomes[7].

← Older revision		Revision as of 18:42, 10 July 2023
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	* <b>Manufacturing Industry</b>: manufacturers can use prescriptive analytics to optimize their production processes, reduce costs, and improve product quality. For example, prescriptive analytics can help manufacturers to identify bottlenecks in their production processes and develop targeted interventions to improve efficiency.		* <b>Manufacturing Industry</b>: manufacturers can use prescriptive analytics to optimize their production processes, reduce costs, and improve product quality. For example, prescriptive analytics can help manufacturers to identify bottlenecks in their production processes and develop targeted interventions to improve efficiency.
	* <b>Government</b>: can help government agencies to make more informed decisions that improve their services and operations. However, implementing prescriptive analytics in the public sector can be challenging due to the complexity of government operations and the need to balance competing interests. Prescriptive analytics can help government agencies to make impartial decisions, as AI processes data quickly and more accurately than a human could. This means human biases and emotion won't creep into decisions. For example Government agencies can use prescriptive analytics to optimize their emergency management operations; helping the agency identify the best evacuation routes during a natural disaster, allocate resources to areas that are most in need, and predict the likelihood of future disasters based on historical data.		* <b>Government</b>: can help government agencies to make more informed decisions that improve their services and operations. However, implementing prescriptive analytics in the public sector can be challenging due to the complexity of government operations and the need to balance competing interests. Prescriptive analytics can help government agencies to make impartial decisions, as AI processes data quickly and more accurately than a human could. This means human biases and emotion won't creep into decisions. For example Government agencies can use prescriptive analytics to optimize their emergency management operations; helping the agency identify the best evacuation routes during a natural disaster, allocate resources to areas that are most in need, and predict the likelihood of future disasters based on historical data.
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		+
		+	<youtube>Gmv9GxlvTMA</youtube>
		+	<youtube>ae273jGk7uY</youtube>
		+	<youtube>clHxDQPHIeE</youtube>
		+	<youtube>_ZlCIXu1F3Q</youtube>

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 * <b>Financial Services Industry</b>: companies can use prescriptive analytics to optimize their investment strategies, reduce risk, and improve customer satisfaction. For example, prescriptive analytics can help financial services companies to identify the best investment opportunities based on market trends and customer behavior.
 * <b>Manufacturing Industry</b>: manufacturers can use prescriptive analytics to optimize their production processes, reduce costs, and improve product quality. For example, prescriptive analytics can help manufacturers to identify bottlenecks in their production processes and develop targeted interventions to improve efficiency.
-* <b>Government</b>: can help government agencies to make more informed decisions that improve their services and operations. However, implementing prescriptive analytics in the public sector can be challenging due to the complexity of government operations and the need to balance competing interests. Prescriptive analytics can help government agencies to make impartial decisions, as AI processes data quickly and more accurately than a human could. This means human biases and emotion won't creep into decisions.  For example Government agencies can use prescriptive analytics to optimize their emergency management operations. For example, prescriptive analytics can help emergency management agencies to identify the best evacuation routes during a natural disaster, allocate resources to areas that are most in need, and predict the likelihood of future disasters based on historical data.
+* <b>Government</b>: can help government agencies to make more informed decisions that improve their services and operations. However, implementing prescriptive analytics in the public sector can be challenging due to the complexity of government operations and the need to balance competing interests. Prescriptive analytics can help government agencies to make impartial decisions, as AI processes data quickly and more accurately than a human could. This means human biases and emotion won't creep into decisions.  For example Government agencies can use prescriptive analytics to optimize their emergency management operations; helping the agency identify the best evacuation routes during a natural disaster, allocate resources to areas that are most in need, and predict the likelihood of future disasters based on historical data.

← Older revision		Revision as of 18:33, 10 July 2023
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	* <b>Financial Services Industry</b>: companies can use prescriptive analytics to optimize their investment strategies, reduce risk, and improve customer satisfaction. For example, prescriptive analytics can help financial services companies to identify the best investment opportunities based on market trends and customer behavior.		* <b>Financial Services Industry</b>: companies can use prescriptive analytics to optimize their investment strategies, reduce risk, and improve customer satisfaction. For example, prescriptive analytics can help financial services companies to identify the best investment opportunities based on market trends and customer behavior.
	* <b>Manufacturing Industry</b>: manufacturers can use prescriptive analytics to optimize their production processes, reduce costs, and improve product quality. For example, prescriptive analytics can help manufacturers to identify bottlenecks in their production processes and develop targeted interventions to improve efficiency.		* <b>Manufacturing Industry</b>: manufacturers can use prescriptive analytics to optimize their production processes, reduce costs, and improve product quality. For example, prescriptive analytics can help manufacturers to identify bottlenecks in their production processes and develop targeted interventions to improve efficiency.
		+	* <b>Government</b>: can help government agencies to make more informed decisions that improve their services and operations. However, implementing prescriptive analytics in the public sector can be challenging due to the complexity of government operations and the need to balance competing interests. Prescriptive analytics can help government agencies to make impartial decisions, as AI processes data quickly and more accurately than a human could. This means human biases and emotion won't creep into decisions. For example Government agencies can use prescriptive analytics to optimize their emergency management operations. For example, prescriptive analytics can help emergency management agencies to identify the best evacuation routes during a natural disaster, allocate resources to areas that are most in need, and predict the likelihood of future disasters based on historical data.

← Older revision		Revision as of 18:28, 10 July 2023
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−	Prescriptive ~~analytics~~ is an advanced form of analytics that uses machine learning and artificial intelligence to recommend actions that optimize a specific business objective. Unlike [[Predictive Analytics]], which only predicts future outcomes based on historical data, prescriptive analytics goes a step further by identifying the best course of action to achieve the defined objective(s). AI can support prescriptive analytics by analyzing large amounts of data to identify patterns and relationships that are not immediately apparent to humans, developing predictive models that forecast future outcomes based on historical data, optimizing complex systems that have many interdependent variables, and making real-time decisions based on changing conditions. Prescriptive analytics employs AI, ML, and advanced algorithms to specify the desired outcome(s) and optimize the right sequence of actions to achieve it. The AI can explore new lines of thought to propose unseen solutions that are out of reach of the human mind. Using machine learning, AI determines by itself the rules that best suit the data to reach the predefined objective. Prescriptive analytics can help organizations make decisions based on highly analyzed facts rather than jump to under-informed conclusions based on instinct. It can help prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers.	+	<b>Prescriptive Analytics</b> is an advanced form of analytics that uses machine learning and artificial intelligence to recommend actions that optimize a specific business objective. Unlike [[Predictive Analytics]], which only predicts future outcomes based on historical data, prescriptive analytics goes a step further by identifying the best course of action to achieve the defined objective(s). AI can support prescriptive analytics by analyzing large amounts of data to identify patterns and relationships that are not immediately apparent to humans, developing predictive models that forecast future outcomes based on historical data, optimizing complex systems that have many interdependent variables, and making real-time decisions based on changing conditions. Prescriptive analytics employs AI, ML, and advanced algorithms to specify the desired outcome(s) and optimize the right sequence of actions to achieve it. The AI can explore new lines of thought to propose unseen solutions that are out of reach of the human mind. Using machine learning, AI determines by itself the rules that best suit the data to reach the predefined objective. Prescriptive analytics can help organizations make decisions based on highly analyzed facts rather than jump to under-informed conclusions based on instinct. It can help prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers.

	= Achieving Prescriptive Analytics =		= Achieving Prescriptive Analytics =