Size Matters: Why You Need Big Data Analytics

Big data is not a new concept. It has been used in business for decades. The reason it’s becoming a buzz word now is because companies are beginning to understand that if they capture all the data that is coming into their business, they can apply analytics and get significant value from it.

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Big data is being used to uncover hidden patterns, correlations and other insights that regular data keeps hidden. In today’s world, it is becoming possible to get answers immediately after analyzing the data.

The technologies powering big data analytics are not sufficient to handle the task by themselves though. People with talent and well-planned analytical processes are needed to carry out effective big data analytics.

Why is big data analytics important?

In short, it helps the company in identifying new opportunities. Below I’ll be referring to Apache Hadoop, which is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware.

Potential benefits of big data include:

Cost reduction: Cost is reduced when big data technologies such as Hadoop and cloud based analytics store large amounts of data and in addition they identify the more effective ways of doing business.

Faster and better decision making: Information is analyzed by the business immediately and decisions are made based on what they have learned when the speed of Hadoop and in-memory analytics are combined with the ability to analyze new source of data.

New products and services: New products are being created by the companies to meet the customer need.

Key Challenges in Big Data

There are three strategic and operational challenges of Big Data:

  1. Information strategy: The power of information assets should be harnessed. Big data is causing enterprises to find new techniques to influence information sources to drive growth.
  2. Data Analytics: More insights are needed to be drawn from big data analytics or large datasets. The future customer behavior, outcomes and trends need to be predicted.
  3. Enterprise Information Management: As we know that information is everywhere and it is growing at a very fast rate, so it is the need to manage such information and drive innovation in fast information processing.

Uses of Big Data Analytics:


  • One to one marketing
  • Campaign management and optimization
  • Location based marketing
  • 360-degree customer view


  • Risk management
  • Wealth management
  • Fraud detection and prevention
  • Trade surveillance


  • Public health reporting
  • Patient care quality and outcomes analysis
  • Clinical data transparency
  • Reimbursement modeling


  • Risk assessment and avoidance
  • Customer value management
  • Catastrophic planning
  • Claims fraud detection


  • Loyalty program management
  • Event /Behavior based targets
  • Supply chain management and analysis
  • Cross channel customer service optimization


  • Call detail record analysis
  • Network planning and optimization
  • Mobile user location analysis
  • New product research and development

Top Big Data Tools:

Apache Hadoop: To handle very large data sets, this open source software was developed by Doug Cutting and Mike Cafarella in 2006. It consists of two parts: Hadoop Distributed File System (HDFS) and MapReduce. Data is stored in Hadoop by splitting files into large blocks and then distributing it along the nodes. The processing engine of Hadoop is MapReduce.

Apache Spark: It is a data analytics tool. It is an open source framework for cluster computing. Streaming data, machine learning and interactive analysis are the common use cases of Apache Spark.

Apache Hive: For data summarization, query and analysis Apache Hive is used. It is built on top of Hadoop. Queries in HiveQL language are supported by Hive. It translates SQL like queries in MapReduce jobs. Currently, there are four file formats supported in the Hive, which are TEXTFILE, SEQUENCEFILE, ORC and RCFILE.

NoSQL Database: It is an approach to data management and database design that is useful for the distribution of very large sets of data. It is a popular option for big data analytics because of its database, like MongoDB, Cassandra, and HBase.

Vaishnavi Agrawal loves pursuing excellence through writing and has a passion for technology. She is based out of Bangalore and has experience of 5 years in the field of content writing and blogging. Her work has been published on various sites related to Hadoop, Big Data, Business Intelligence, Cloud Computing, IT, SAP, Project Management and more.

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