DATA ANALYTICS AND PREDICTIVE ANALYTICS IN THE ERA OF BIG DATA

• Published in: Internet of Things and Data Analytics Handbook pp. 329 - 345
• Main Authors: Shi‐Nash, Amy, Hardoon, David R
• Format: Book Chapter
• Language: English
• Published: Hoboken, NJ, USA Wiley 2016; John Wiley & Sons, Inc
• Edition: 1
• Subjects: big data; business intelligence; CRISP‐DM; data mining projects; knowledge discovery in databases; machine learning method; predictive analytics; Machine learning algorithms; Data analysis; Correlation; Big Data; Data mining; Expert systems; Business
• ISBN: 1119173647; 9781119173649
• DOI: 10.1002/9781119173601.ch19

This chapter outlines the key principles of machine learning and predictive analytics. It explains the new fundamentals of big data and the evolving technology. The chapter follows by the practical advice on how organizations can establish a new culture in order to truly transform their business in the new era. The wave of data frenzy did not happen overnight. Rather, it is a crescendo of events happening since the early 1980s where the fields of business intelligence and predictive analytics were known as 'data mining', a preexisting discipline with another closely related term known as knowledge discovery in databases (KDD), which is the aim of performing data mining. Analytics has a spectrum of methodologies, techniques, and approaches from descriptive, diagnostic, predictive and prescriptive analytics. Most data mining projects today follow the cross industry standard process for data mining (CRISP‐DM) which was conceived in 1996.