Types of Analytics: Descriptive, Predictive, Prescriptive Analytics

The big data revolution has given birth to different kinds, types and stages of data analysis. Boardrooms across companies are buzzing around with data analytics - offering enterprise wide solutions for business success.

However, what do these really mean to businesses?

The key to companies successfully using Big Data, is by gaining the right information which delivers knowledge, that gives businesses the power to gain a competitive edge. The main goal of big data analytics is to help organizations make smarter decisions for better business outcomes.

Big data analytics cannot be considered as a one-size-fits-all blanket strategy.

In fact, what distinguishes a best data scientist or data analyst from others, is their ability to identify the kind of analytics that can be leveraged to benefit the business - at an optimum.

The three dominant types of analytics –Descriptive, Predictive and Prescriptive analytics, are interrelated solutions helping companies make the most out of the big data that they have. Each of these analytic types offers a different insight. In this article we explore the three different types of analytics -Descriptive Analytics, Predictive Analytics and Prescriptive Analytics - to understand what each type of analytics delivers to improve on, an organization’s operational capabilities.

Big data analytics cannot be considered as a one-size-fits-all blanket strategy. In fact, what distinguishes a best data scientist or data analyst from others, is their ability to identify the kind of analytics that can be leveraged to benefit the business - at an optimum.

The three dominant types of analytics –Descriptive, Predictive and Prescriptive analytics, are interrelated solutions helping companies make the most out of the big data that they have. Each of these analytic types offers a different insight.

In this article we explore the three different types of analytics -Descriptive Analytics, Predictive Analytics and Prescriptive Analytics - to understand what each type of analytics delivers to improve on, an organization’s operational capabilities.

Understanding Predictive and Descriptive Analytics

A lioness hired a data scientist (fox) to help find her prey. The fox had access to a rich Data Warehouse, which consisted of data about the jungle, its creatures and events happening in the jungle.

On its first day, the fox presented lioness with a report summarizing where she found her prey in the last six months, which helped the lioness decide where to go hunting next. This is an example of Descriptive Analytics.

Next, the fox estimated the probability of finding a given prey at a certain place and time, using advanced ML techniques. This is Predictive Analytics. Also, it identified routes in the jungle for the lioness to take to minimize her efforts in finding her prey. This is an example of Optimization.

Finally, based on above models, the fox got trenches dug at various points in the jungle so that the prey got caught automatically. This is Automation.

What is Descriptive Analytics?

90% of organizations today use descriptive analytics which is the most basic form of analytics. The simplest way to define descriptive analytics is that, it answers the question “What has happened?”.

This type of analytics, analyses the data coming in real-time and historical data for insights on how to approach the future.

The main objective of descriptive analytics is to find out the reasons behind precious success or failure in the past. The ‘Past’ here, refers to any particular time in which an event had occurred and this could be a month ago or even just a minute ago. The vast majority of big data analytics used by organizations falls into the category of descriptive analytics.

A business learns from past behaviours to understand how they will impact future outcomes. Descriptive analytics is leveraged when a business needs to understand the overall performance of the company at an aggregate level and describe the various aspects.

What is Predictive Analytics?

The subsequent step in data reduction is predictive analytics. Analysing past data patterns and trends can accurately inform a business about what could happen in the future.

This helps in setting realistic goals for the business, effective planning and restraining expectations. Predictive analytics is used by businesses to study the data and to find answers to the question “What could happen in the future based on previous trends and patterns?”

Organizations collect contextual data and relate it with other customer user behaviour datasets and web server data to get real insights through predictive analytics.

Companies can predict business growth in future if they keep things as they are. Predictive analytics provides better recommendations and more future looking answers to questions that cannot be answered by BI.

What is Prescriptive Analytics?

Big data might not be a reliable crystal ball for predicting the exact winning lottery numbers but it definitely can highlight the problems and help a business understand why those problems occurred. Businesses can use the data-backed and data-found factors to create prescriptions for the business problems, that lead to realizations and observations.

Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future.

Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximise key business metrics. It basically uses simulation and optimization to ask “What should a business do?” 

Prescriptive analytics are comparatively complex in nature and many companies are not yet using them in day-to-day business activities, as it becomes difficult to manage.

Prescriptive analytics if implemented properly can have a major impact on business growth. Large scale organizations use prescriptive analytics for scheduling the inventory in the supply chain, optimizing production, etc. to optimize customer experience.

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