Big Data and Its Technical Challenges

H. V. Jagadish , Johannes Gehrke , Alexandros Labrinidis , Yannis Papakonstantinou , Jignesh M. Patel , Raghu Ramakrishnan and Cyrus Shahabi
Communications of the ACM , pp. 86–94 , 2014
PDF DOI

Abstract

In a broad range of application areas, data is being collected at an unprecedented scale. Decisions that previously were based on guesswork or on painstakingly handcrafted models of reality can now be made using data-driven mathematical models. Such Big Data analysis now drives nearly every aspect of society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences.

Creating value from Big Data is a multi-step process: acquisition, information extraction and cleaning, data integration, modeling and analysis, and interpretation and deployment. Research challenges abound, ranging from heterogeneity of data, inconsistency and incompleteness, timeliness, privacy, visualization, and collaboration, to the tools ecosystem around Big Data.

This article has been downloaded over 31,000 times from the ACM Digital Library and has received over 1,700 citations according to Google Scholar [as of May 2026].