Advanced Analytics: Descriptive, Predictive, Prescriptive Analytics Explained
Advanced analytics is a term used to describe the process of autonomously discovering, interpreting and communicating the meaningful patterns in data using high-level data science methods in combination with other sophisticated techniques and tools. Analyst firm, Gartner, states that advanced analytic techniques include machine learning pattern matching, cluster analysis, multivariate statistics and a variety of other methods.
Unlike what can be achieved with traditional Business Intelligence (BI), advanced analytics is often used by organisations to gain much deeper insight into trends, patterns and themes that may be contained in the data that they have – which will help them better understand their business and operations, reduce risk, make predictions and generate the appropriate recommendations for actionable insights.
There are three distinct types of advanced analytics, which are:
Descriptive Analytics - mines and creates a summary of historical information to provide insights and answers into understanding “what happened” in the past.
Predictive Analytics - examines historical and real-time data to identify patterns, trends and future probabilities in order to provide information about “what could happen” in the future.
Prescriptive Analytics - looks at both historical and real-time data and, based on identifiable patterns, recommends the best possible outcomes and courses of actions to take so organisations know “how to respond” to potential future events and mitigate future risks.
These advanced analytics approaches are becoming an especially useful and powerful tool for organisations to remain competitive in an increasingly data-driven era. By making it part of a broader digital transformation strategy, organisations are able to use the insights provided to create new business models, improve operational efficiencies and drive innovation in products and services.
