HomeData EngineeringData EducationDescriptive vs. Predictive vs. Prescriptive in Big Data

Descriptive vs. Predictive vs. Prescriptive in Big Data

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The big data revolution has given birth to different types of big data analysis. The big data industry is buzzing around with data analytics that offers enterprise-wide solutions for business success.

The key for any company that wants to successfully use big data is gaining the right information that delivers knowledge and gives businesses the power to gain a competitive edge. And this can be only be done by identifying and selecting from different types of big data analytics.

Big data analytics should not be considered as a one-size-fits-all blanket strategy. What distinguishes the best data scientist or data analyst from others is that they have the ability to identify the kind of analytics that can be leveraged for gaining benefits for their particular business line. There are three dominant types of analytics available today –descriptive, predictive, and prescriptive. These are interrelated solutions that are helping companies to make the most out of the big data that they have. Each of these analytic types offers organizations a different kind of insight.

In this blog post, we will explore all of these three different types of for understanding what each type of analytics has got to offer to an organization’s operational capabilities.

TYPES OF BIG DATA ANALYTICS: DESCRIPTIVE

The descriptive analysis does exactly what the name implies; they summarize or describe raw data and make it something that is interpretable by humans. It analyzes past events, here past events refer to any point of time that an event has occurred, whether it is one minute ago, or one month ago. Descriptive analytics are useful as they allow organizations to learn from past behaviors, and help them in understanding how they might influence future outcomes.

Usually, the underlying data that gets analyzed is a count or aggregate of a filtered column of data. Descriptive statistics are useful in showing things like total stock in inventory or average dollars spent per customer. Organizations must use descriptive analysis when they want to understand, at an aggregate level,what is going on in their company.

TYPES OF BIG DATA ANALYTICS : PREDICTIVE

Predictive analytics has the ability of “Predicting” what might happen next. Predictive analytics is about understanding the future. Predictive analytics provides organizations with actionable insights based on data. Moreover, it also provides estimates of the likelihood of a future outcome. But, it is equally important to remember that no statistical algorithm can “predict” the future with 100% accuracy. Organizations can use these statistics for forecasting what might happen in the future. This is because the foundation of predictive analytics is based on probabilities obtained from data.

One common application of predictive analytics is to produce a credit score. These scores are used by financial institutions to determine the probability of customers making future credit payments on time.

TYPES OF BIG DATA ANALYTICS : PRESCRIPTIVE

This relatively new field of prescriptive analytics facilitates users to “prescribe” different possible actions to implement and guide them towards a solution. Prescriptive analysis is all about providing advice. It attempts to quantify the effect of future decisions in order to advise on possible outcomes before those decisions are actually made. Prescriptive analytics not only predicts what will happen, but also tells why it will happen, and thereby provides recommendations regarding actions that take advantage of these predictions.

Prescriptive analytics is complex to administer, and most companies are still not using it. However, when implemented correctly, prescriptive analytics can have a large impact on how businesses make decisions and thereby, help them in delivering the right products at the right time, consequently optimizing the customer experience.
Looking at all the different types of analytic options can be a daunting task. No one type of analytic is better than the other; rather they co-exist with, and complement each other.
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