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Predictive vs Prescriptive vs Descriptive Analytics Explained 

Business analytics leveraging data patterns for strategic moves comes in three key approaches – descriptive identifying “what has occurred", predictive forecasting “what could occur” and prescriptive recommending “what should occur” to optimize decisions. We decode the science behind each for aspiring analytics professionals.

Descriptive analytics convert volumes of historical data into insightful summaries around metrics revealing business health, customer trends, operational efficiencies etc. using direct analysis, aggregation and mining techniques producing current reports. 

Predictive analytics forecast unknown future probabilities applying statistical, econometric and machine learning models over existing data to minimize uncertainties and capture emerging behaviors early for mitigation actions. Risk models simulate scenarios balancing upside/downside tradeoffs. 

Prescriptive analytics take guidance one step further by dynamically recommending best decision options factoring in key performance indicators for business objective improvements after predicting multiple futures using bell curve simulations. Optimization algorithms deliver preferred actions.

While foundational data comprehension and wrangling abilities fuel all models – pursuing analytics specializations focused on statistical, computational or operational excellence boosts career-readiness filling different priorities global employers seek!

Posted By:

Aditi Borade, 4th year Barch,

Ls Raheja School of architecture 

Disclaimer: The perspectives shared in this blog are not intended to be prescriptive. They should act merely as viewpoints to aid overseas aspirants with helpful guidance. Readers are encouraged to conduct their own research before availing the services of a consultant.