Purpose & Types Of Descriptive & Predictive Analytics

Descriptive analysics do exactly what the name implies they “Describe”, or summarize raw data and make it something that is interpretable by humans. Descriptive analytics take data, collects it, and tries to map the data to patterns that you can understand in the data. It looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Descriptive analytics help to understand the relationship between customers and products and the objective is to gain an understanding of what approach to take in the future: learn from past behaviour to influence future outcomes.

A simple example of descriptive analytics would be assessing credit risk; using past financial performance to predict a customer’s likely financial performance. Descriptive analytics can be useful in the sales cycle, for example, to categorize customers by their likely product preferences and sales cycle. Most management reporting – such as sales, marketing, operations, and finance – uses this type of post-mortem analysis.

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The only thing that descriptive analytics requires is data (and a tool to analyze it). One primary limitation of descriptive analytics, however, is that it is usually only capable of studying relationships between 2 or 3 variables at a time. If a problem has numerous interactions among the variables (which is common in practice) it becomes difficult to analyze the data in a holistic manner using descriptive analytics alone.

Predictive analytics has its roots in the ability to “Predict” what might happen. These analytics are about understanding the future. Predictive analytics answers the question what is likely to happen.

This is when historical data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. They seek to leverage the historical data used by descriptive analytics to build mathematical models that can be used to to forecast what might happen in the future.

The three keystones of predictive analytics are decision analysis and optimization, transactional profiling, and predictive modeling. A simple example might be: "Given the behavior of my past customers, how likely is a particular new customer to buy my product if I send him/her a promotional coupon?” The question itself acknowledges that there may be patterns within past data that can be used to make better decisions when interacting with future customers. Predictive analytics try to take the behavior of consumers find by descriptive analytics and predict from their past behavior what they’re going to do in the future.

The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. Prescriptive analytics goes beyond simply predicting options in the predictive model and actually suggests a range of prescribed actions and the potential outcomes of each action. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.

These are often formulated as optimization and simulation problems where a business or manager is trying to maximize (or minimize) some objective (e.g. profit, efficiency, cost, employee satisfaction, etc.) subject to a set of limitations on resources, contractual obligations, or other constraints. Tesla’s self-driving car is an example of prescriptive analytics in action. The vehicle makes millions of calculations on every trip that help the car decide when and where to turn, whether to slow down or speed up, and when to change lanes — the same decisions a human driver makes behind the wheel.