How is prescriptive analytics best defined?

Prescriptive analytics is best defined as optimizing outcomes by applying results from descriptive and predictive analytics. This type of analytics goes beyond merely understanding what has happened (descriptive analytics) or what is likely to happen in the future (predictive analytics). Instead, it focuses on providing actionable recommendations based on the identified patterns and predictions.

In prescriptive analytics, data from past performance is analyzed alongside predictive models to generate recommendations about what actions can be taken to achieve desired outcomes. This approach utilizes algorithms and machine learning techniques to optimize decision-making processes in various fields such as healthcare, finance, and supply chain management. It addresses the question of "what should we do?" rather than just understanding past events or predicting future occurrences.

The other definitions do not capture the essence of prescriptive analytics. While standard reporting, summarizing data into concise reports, and using past results to dictate future strategies are components of data analysis, they do not encompass the proactive nature of prescriptive analytics in actively guiding decisions and actions for optimal results.