AbstractsComputer Science


by Donald Lee

Institution: California University FCE
Department: Management Information Systems
Degree: Doctorate Degree
Year: 2016
Keywords: Big Data, Data Science, Business Intelligence
Posted: 12/22/2016
Record ID: 2150643
Full text PDF: https://1drv.ms/b/s!Asi_zcxDY4KNgah4_hIw21wBZEjepg


Since 2005, Big Data analytics have been gaining traction and recognition as an essential and rewarding business resource to enhance strategic and operational decision making for business leaders. Big Data analytics consist of the 4 V’s of unstructured data—volume, velocity, variety, and veracity—stemming from nonstop 24/7 exchanges of emails, photo-sharing, and other Internet activities around the world. Big Data’s power lies in making targeted predictive analysis from mining messy, unstructured data points with machine learning, an aspect of artificial intelligence. Big Data analytics has upended the traditional use of data analysis for business intelligence to benefit businesses. This research study employed the Interpretative Phenomenological Analysis (IPA) method to discern the lived experiences of data practitioners, executive-level decision makers located in the U.S. Pacific Northwest. Participant responses were unequivocal about Big Data analytics playing important roles for successful businesses to gain even more traction to achieve and maintain the competitive edge. Yet, as one respondent pointed out, the old carpenter saw of “Measure twice, cut one” still prevailed in being smart about analyzing an organizations’ business goals first—before deploying Big Data analytics. The findings suggested strong leadership was essential to articulate and motivate buy-in from all decision makers to achieve a collaborative approach to Big Data analytics for successful implementation. The implications for industry applications range from mining data points for healthcare and education to writing bestselling books.