A review and future direction of agile, business intelligence, analytics and data science
We provide review of agile methodologies, business intelligence and data science.We explain the current trends and interplay between agile methodologies, business intelligence and data science.We develop lifecycle approaches for BI, analytics and data science.We discuss challenges and future directions for agile methodologies, business intelligence and data science. Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.
Int. J. Inf. Manag.