Tuesday, September 25, 2012

Difference between Descriptive, Predictive and Prescriptive Analytics

Analytics as a discipline has matured beyond its age propelled by the new era in which the human-machine-network is more Instrumented, Interconnected and Intelligent. The new era has made data more accessible than ever before which can be leveraged by the science of analytics to not only increase the accuracy of future predictions but also to up the ante by one level and start optimizing the best outcome from a set of predicted possibilities.

Analytics has a maturity curve, or more of a roadmap which starts from Descriptive Analytics and works its way up to Predictive Analytics and ultimately to Prescriptive Analytics.

Descriptive Analytics, often called after-the-fact analytics, reports on what happened, the frequency of occurrences of a certain event or action and provide drill down capabilities to get to the root cause of the problem. It provides various reporting views based on user roles; summary views for the executive dashboards, metric views for the mid-level managers and drill down root cause analysis details for the engineers and domain experts. Descriptive Analytics is rooted in what is known as traditional BI reporting.

Predictive Analytics focuses on simulating what could happen in the future, given the conditions of the recent past and forecasting the next possible events if the current trend continued for a given period of time. Predictive Analytics is rooted in building supervised and unsupervised machine learning algorithms and models.

Prescriptive Analytics builds on top of Predictive Analytics and focuses on evaluating the various possible outcomes from predictive models and coming up with the best possible outcome by employing optimization algorithms. Such algorithms are also capable of factoring in the effects of variability. Prescriptive Analytics leverages stochastic optimization algorithms and models.

It is imperative to realize that there is no short cut for any enterprise to achieve the highest maturity levels in Analytics (i.e. Prescriptive Analytics) without developing a solid and sound foundation of descriptive analytics followed by predictive analytics.

Enterprises need also to realize that, just by virtue of being in the new era of instrumented, interconnected and intelligent human-machine-network does not give them a free ticket to accessing the data; the data that is required for analytics to be useful. A solid foundation of data access with key focus on ensuring the veracity and viscosity of the data is of superior quality is the very first step to reap the benefits of modern day analytic processing.

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