Abstract
The investigators have brought out the history of big data, its meaning, its different types such as web data, text data, location and time data, social network data, etc. The characteristics of big data such as volume, velocity, variety, and complexity are discussed. Application of big data in various fields in everyday life is discussed in different fields such detection of fraud, application in agriculture field, banking implication, healthcare implication. Entertainment and media industry is also using it effectively. Big data is also used in weather forecasting, transportation industry, education industry, and sports sector. Future of big data is bright. More data on everything is available today. One needs to analyse everything today in order to implement policies. Software is available to process such voluminous data. The chapter also discusses the influence of big data on Indian governance, digitalization in India, finance and banking sector. In conclusion, one can say there is bright future of big data in various private and public sectors. Today's problem is information overload. One has to be very dexterous in disseminating using information with the help of web tools and software one can use. The investigators also discuss the perspectives and applications in academic. Voluminous data available in the academic environment requires proper accumulation, assembly, access, curation, retrieval, and dissemination. Dexterous skills are required for handling this situation. Information literacy deals with accessing, evaluating, and ethically using information.
TopWhat Is Big Data?
Generally big data is the term used to refer to technologies which contains data which is too vast, fast evolving and too diverse for ordinary technologies and skills to address exhaustively. Big data involves various processes like creation of huge amount of data, its storage, it’s retentively and finally its analysis. Various examples of big data are:
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Web data: Searches, reading reviews, page views, website views can be collected to enhance the performance in areas such as next modelling, best offers and focused advertisement.
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Text data: It contains Facebook feeds, emails, documents etc. The main focus is typically to extract key facts from such form of text data as an inputs to analytic processes.
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Location and time data: Wi-Fi connections and GPS gives time and location information. Lots of interesting applications are emerging because of location and time data. But as this data is very sensitive it should be treated with great precaution.
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Social network data: Analysis of social networking sites can give insights into users’ interest. Also interest of their friend circle and colleagues can be figured out.
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Sensor data: Sensor data can be collected from vehicles, various instruments, etc. This data provide important information about performance of respected machinery. Through this data diagnosis of any problem can be easier.