Exploring the World of Big Data: Understanding Impact and Possibilities


  • Shubham Singh
  • Satyam Singh


Structured, big data, sortable, data processing’s, unstructured, semi-structured


Big data refers to the management and analysis of large and complex data sets that cannot be handled by traditional data processing software. It involves techniques for extracting useful information from these data sets, and involves challenges such as data storage, analysis, sharing, and privacy. The three main characteristics of big data are the large volume of data, the variety of data types, and the fast pace at which data is generated and processed. Rather than sampling, big data often involves observing and tracking data as it occurs, and the data sets are often too large for traditional software to process within a reasonable amount of time and value. Advancements in big data, artificial intelligence, and data-driven innovation have the potential to bring significant benefits to society and various industries. However, if these technologies are not used in an ethical and socially responsible manner, they can lead to the misuse of data and violation of privacy and ethical standards. This phenomenon is referred to as the "creep factor" of big data, and it must be addressed urgently, especially as we move towards a "datafied" society where data collection and processing are becoming faster, cheaper, and accessible. While using big data in a transparent and ethical framework can facilitate sustainable development, using it without such a framework, poses numerous threats and ethical challenges. For instance, new surveillance tools and data gathering techniques, including group privacy, high-tech profiling, automated decision-making, and discriminatory practices, can impact privacy and lead to unfair practices. In today's society, life-changing opportunities are often determined by predictive algorithms applied to data, and it is crucial to ensure the fairness and accuracy of these scoring systems and decision-making processes to avoid stigmatization and bias. Moreover, there is a risk of "social cooling," which refers to the long-term negative effects of data-driven innovation, such as selfcensorship, risk-aversion, and lack of free speech, generated by intrusive big data practices lacking an ethical foundation. In addition, the increasing volume, speed, and variety of data sources collected in IoT environments raise questions regarding data ownership and other hurdles that require further investigation.