Analytics in Big Data- Basic Concepts

Analytics is a very crucial part of any big data analytics project. Analytics in Big Data refers to the collection, analysis, and organizing large datasets to gather important information. As in a complete dataset, the data may be available in structured, semi-structured, and unstructured formats, it becomes difficult for organizations to perform analysis. So, for effective implementation of analytics of data, various advanced tools are required.

Reporting – Analytics in Big Data

 A report is created from reporting which is the process of organizing and summarizing in a ready format. It can help organizations with performance measurement and improve profitability or customer satisfaction in organization. Data analysis is the process of getting insights from the reports. Sometimes various reports may not provide the full insights for a particular role in a company. In this case, the relevance of the report will matter and not the quantity.

 Steps of Analytics in Big Data

Analytics in Big Data- Steps

 Business understanding and deduction

This step involves the identification and understanding of the business objectives. It deals with problems and identifies their solutions. It caters to increasing the profitability of a business. According to the deduced goals, a plan is devised to execute the analytical operations.

Data Collection

Data Collection refers to the process of collecting data to execute a plan from the 1st step. Data from various sources is collected and described as per the needs of the big data project.

Data Preparation

In this step, unwanted data is removed from the complete data to prepare it for the next step.

Data Modeling

Data modeling deals with the creation of a data model to analyze the data relationships among different objects present in the dataset.

Data Evaluation

Data Evaluation is the process of evaluating the results obtained from the test cases. Errors are also reviewed in this stage.


The data analytics plan is deployed for development. Maintenance and frequent error checks are also a part of this stage.

 For analytics to be performed on big data, some questions need to be answered so that positive effects can be implemented onto a business. The problem for any data analytics problem must be carefully framed and understood to solve it successfully. It starts with understanding the data correctly and then create a great plan for analysis.

Types of Analytics in Big Data


It is used to find the best course of action for a particular situation.


In this type of analytics, future-based events are predicted using patterns derived from past results. For example Stock prediction.


Used to determine what has happened in the past. For example Social media insights


It deals with determining the decision-making process in real-time. For example customer satisfaction reports and classification.

 How to be an analyst for Big Data?

 Many companies are looking for data analysts for the growth of their businesses around the globe. Being a data analyst can be challenging, but with the correct skills and courses, one can easily become a data analyst for any company in the globe. The incorporation of risk management in big data analytics is very useful for any company.