Haresh Vidja
Written By Haresh Vidja Tech Lead

4 Types of Big Data Analytics; The Ultimate Guide for Business to Succeed

post date
June 28, 2022
Reading Time: 3 minutes

Before explaining types of Big Data Analytics there is a need to understand what Big data analytics is.

Definition of Big Data Analytics

Big Data Analytics can refer to the process of analyzing a large volume of data having diverse characteristics by using advanced analytic techniques.

Different surveys and research had conducted to estimate the outcomes of big data analytics. In 2021; a survey was conducted by New Vantage Partners among IT and business executives of 94 companies. It reported that 91.7% of executives said they are investing in big data projects, and some other data and AI initiatives, while 92.1% documented that their companies have achieved measurable business results and outcomes from these initiatives.

There are some key elements of big data analytics. They are:

Types of Big Data Analytics

There are four types of Big Data Analytics based on different types of approaches. Here is the detail.

Descriptive Analytics

It is a useful technique to see the patterns within a specific group of customers. Its purpose is to simplify the data and summarizes the past data in a form that is readable.

With this technique, an organization can have insight into what has occurred in the past and see the trends to dig in for more details. This technique assists in the creation of reports like the company’s sales, profits, revenue, and others.

Examples may include summary statistics, clustering, and association rules. A practical example of descriptive analytics can be a Chemical company. The company uses its past data so it can increase its facility utilization in its offices and labs.

Diagnostic Analytics

The name of this technique suggests that it will provide the diagnosis of a problem. Diagnostic analytics provide a detailed and in-depth insight into the main cause of the problem. Examples of techniques that use in this type of Big Data Analytics are data mining, drill-down, data recovery, churn reason analysis, and customer health score.

A practical example can be an e-commerce organization. In which a situation is that the sales are going down even though customers are adding items to their carts.

Some main causes of the problem may include that the form does not open correctly, delivery charges are high, or not enough payment methods available.

So with the help of diagnostic analytics company could find the specific reason behind the problem and can resolve the issue now.

Predictive Analytics

The predictive Analytics technique helps organizations in predicting future incidents. These future indications include market-related events such as market trends and consumer trends.

With this technique, historical and present data can utilize to predict future events. This type of analytics usage is common among businesses. It works for two sides; service providers as well as consumers. It tracks the company’s past activities and on the basis of them predicts the next step.

Many models are used with it such as data mining, machine learning, and AI to analyze available data and predict what can happen next in specific scenarios.

A practical example can be PayPal. The company has set some steps that it will take to protect its client’s fraudulent transactions. It will require past payment data and user behavior data to protect such activities.

Prescriptive Analytics

It is the most valuable but underused type of analytics. It comes after predictive analytics as to the next step. Its purpose is to explore several applicable actions and suggest those actions based on the result of descriptive and predictive analytics of a given set of data.

This technique is a combination of data and various business rules. It can involve two types of data that are internal (organizational inputs) and external (social media insights). It helps businesses to determine the best possible solution to the problem.

A practical example can be the health care system that saved $6 million by decreasing the readmission rates by 10%.

It has good usage in the healthcare industry that can increase the process of drug development, and finding the right patient for clinical trials.

Conclusion

With the help of big data analytics, a business can process and make use of the stack of raw data that they collect on a daily basis. AWS Big Data Analytics is providing its Big Data Analytics Consulting Services. With the help of their custom. big data services a business can start processing and make use of raw data for meaningful purposes.

Haresh Vidja
Written By Haresh Vidja Tech Lead
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