Investigate the Implication of “Self-service Business Intelligence (SSBI)”—a Big Data Trend in Today’s Business World

Abstract

Information is a life blood of any business organization. At present, the data will generate from many sources. It is scattered and unorganized in nature. To make a date meaningful, there are various data analytics tools. The data analytics are quantitative techniques and methods that can convert an unorganized data into organized and generate the meaningful information. Through information, business professionals’ can easily scrutinize the business conditions, understand the business problems and explore the best possible solution as well. At beginning, data are in raw form. The data analytics and techniques can organize, classify, summaries the data according to business requirements especially for smooth functioning and decision making. The big data technology supports to manage the large volume and verity (i.e., text, image, audio and video, etc.) form of data generated in a business on a daily basis. For data analysis, an organization needs a data analyst, data scientists or big data software experts and professionals. On the other hand, for hiring these experts, the organizations have to spend lot of money in the form of salaries or provide other benefits too. Therefore, today many enterprises are likely to be looking for the software that helps business professionals to meet their daily data analytics needs. In this context, the current trend in the big data technology is “Self-Service Analysis”. In the upcoming years, “Self-Service Analysis” will become a daily need for every enterprise. Thus, the aim of this study is to find out the significance of “Self Service Analysis”—a big data trend in today’s business world.

Keywords: Big data, self-service analytics, business intelligence, data mining, data scientist

Cite this Article Hamlata J. Bhat. Investigate the implication of “Self-Service Business Intelligence (SSBI)”, a Big Data trend in today’s Business World. Current Trends in Information Technology. 2020; 10(1): 17–22p.