Data Science - Prescriptive Strategy
Prescriptive strategy helps an organization evaluate different scenarios and seeks to evaluate decision options and determine the best course of action to achieve specific goals. Smart organizations invest in a team of highly educated and trained data scientists to use sophisticated data science techniques, learning algorithms and simulations for crunching relevant smart data and applying probability theory. The data science team works directly with organization leaders to design a prescriptive strategy for evaluating scenarios and making optimal decisions.
High performance compute and learning algorithms applied to diverse data sources has made prescriptive strategy feasible and affordable for most organizations. Data science techniques include artificial intelligence, machine learning, bayesian probability, monte carlo simulations, game theory, decision trees and decision-analysis methods. Mathematical models are techniques derived from mathematical sciences and related disciplines including applied statistics, machine learning, operations research, and natural language processing. Business rules define the business process and include constraints, preferences, policies, best practices and boundaries.
Prescriptive strategy not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the likely implication of each decision option. In practice, learning algorithms continually and automatically process new data to provide better decision options. Prescriptive strategy automatically synthesizes data, mathematical sciences and business rules - used by data scientists to apply probability theory and suggest optimal decision options.