Open AccessDemystifying Prescriptive Analytics Frameworks and Techniques
Author(s) -
Sridhar Lakshmanan,
M. Sornam,
Jimmie Flores
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f4546.049620
Subject(s) - analytics , big data , data science , computer science , software analytics , predictive analytics , data analysis , business analytics , domain (mathematical analysis) , semantic analytics , cultural analytics , business intelligence , management science , knowledge management , data mining , artificial intelligence , engineering , software , mathematics , software development , business , mathematical analysis , semantic web , business model , semantic web stack , software construction , marketing , programming language , business analysis
Big data analytics refers often a very complex process to examine the large and varied data sets to provide the organization to take smarter decisions and better results. Big data analytics is a form of advanced analytics including predictive, prescriptive models and statistical algorithms. The prescriptive analytics is a later stage of the Big data analytics which is not just anticipating what the event will happen as in the predictive analytics but also suggests the decision options and consequences of the decision. The paper addresses the survey of prescriptive analytics and the importance of prescriptive analytics. The prescriptive analytics techniques and methods include machine learning, operation research/management science, optimization techniques, mathematical formulation, and simulation techniques and methods. The paper discusses the techniques and methods, frameworks, and domain applications of prescriptive analytics.