Big Data and Digital Analytics

Big Data: Introduction

Big data owns 3 V’s (Volume, Velocity and Variety). It is defined as the data that appears to be in huge volume of different data that travels with right speed and reaches the destination within the stipulated time frame so that the data could be used for real-time analysis and further reaction. The structure of big data could be structured or unstructured data that could be in any form such as text files, multimedia files, financial data etc. Data comes from several data sources in order to form data inundation. Data sources could be categorized as electronic gadgets like mobile sensors, medical imaging, smart grids, video surveillance, social media video rendering and genetic engineering etc. so as to form data surge. The fastest- growing sources of big data are social media and genetic engineering. Big data might possess data that contains data structures such as structured, unstructured, semi structured and quasi structured. The configuration of structured data includes reports, contracts, email and other communications. Structured data displays the information in the form of text files. The information is displayed in columns and rows. Furthermore, it could be easily organized and processed with the help of data mining tools. This could be envisaged as a perfectly organized filing cabinet in which identification, labeling is a simple task and it is easy to access.

Most of the organizations might like to be familiar with this type of data and it is used in an effective and efficient manner. Data does not have any intrinsic structure. It is available in the form of complex data sources such as email, multimedia content, web logs, sales automation and social media data as denoted by Marcos et al (2015). As per Holzinger et al (2013), unstructured information may account for more than 70% to 80% of all data in organizations. Semi structured data does not match with the prescribed structure of data models in the perspective of relationships. Examples of semi structured data could be XML, JSON, MongoDB that stores data in JSON. Quasi structured data is defined as the textual data with inconsistent data formats that could be formatted with effort and time. For example, quasi structured data refer to the click stream data that might consist of inconsistent data values and formats. Few organizations, definite methods must be extended so that it has to manage the huge volume of data. The below Table 1 illustrates the generation of data that occurred in several organizations.