Big Data Analytics is changing the way data is getting captured across digital processes and analysed using specific platforms to provide actionable insights to domain specific highly complex problems.
Big data analytics offers various benefits to the organizations such as enhancing production and competitions; enabling to analyze customer-generated data present in various formats such as video, blog, and social media data. The firms using Big Data analytics need to store the transaction time, product prices, purchase quantities and customer credentials on regular basis for estimating the market conditions, consumer behavior, trends and patterns. The data can be used by producers for evidence base decisions for production and inventory, sales forecasting, commodity price optimization, inventory optimization, logistics optimization, supplier coordination, forecasting demand, improving services and increasing the customer satisfaction. The dependence of the firms on the information, knowledge, and evidence based insights derived from the data is increasing day by day with the growth of digital transformation.
The 5 V’s of big data were defined to address the limitation of relational database management system (RDBMS) are the main cause for increasing popularity of Big Data. The first V, which stands for volume can be related to the high quantity of generated data, often generated by sensors and machines. The second V represents the velocity of data creation. One of the popular examples of high velocity generated data is social media data streams, where a lot of users generated unstructured data is created on a regular basis. Managing and processing these data streams is one of the biggest challenges faced by data scientist. The third V represents a variety of the data types which may be analyzed to generate meaningful information. The fourth V represents the value of actionable insights derived from big data. In the larger database, there is a need for identifying the valuable data from which valuable information can be extracted. From 2012, the Fifth V of the big data has also been evolved and that is veracity. Veracity refers to extent of the uncertainty of data. The veracity characteristics of the big data are explained as objectivity, truthfulness, and credibility. The technology to be used by ananalyst for analyzing the big data is also challenging.
While in the last decade there was no need for mechanisms to manage such large volumes of data; but today scientific approaches are required for storing, analyzing and visualizing big data so that organizations can make the best use of the information assets readily available to them. A big data framework, consisting of five phases had been defined in the scientific literature for academic accreditation.However, for analyzing big data, a revolutionary step is needed from traditional data analysis, which has led to the emergence of the domain of data science. The information gained through big data analytics can be used by firms, organizations, and governments for planning ahead and take better data-driven decisions while maintaining flexibility and agility. However, while the domain of big data is attracting a lot of attention, the information is still in siloes. Not much of the existing tries to provide a holistic picture of how the knowledge in the domain is emerging and unfolding itself. Some reviews of big data highlight that it will drive competition, productivity, growth, innovation and consumer surplus across industries through data exploration and utilization which can generate new insights into variables.