The arrival of the “big data” era is opening up new avenues in business, healthcare, etc. Much attention has been paid to scaling challenges arising from the huge increase in volume, velocity, and variety of data. Not as much attention has been paid to non-scaling-related issues that affect a number of fundamental assumptions in current statistical analysis approaches. Having more data is tremendously helpful in some analysis procedures. At the same time, having more data can also make the same analysis procedures fail in fundamental ways. We discuss some examples of these issues and how they might be fixed, as well as some examples where big data enhances analysis outcome.

Limsoon Wong is KITHCT Chair Professor of Computer Science and Professor of Pathology at the National University of Singapore. He currently works mostly on knowledge discovery technologies and their application to biomedicine. He is a Fellow of the ACM, inducted for his contributions to database theory and computational biology. Some of his other awards include the 2003 FEER Asian Innovation Gold Award for his work on treatment optimization of childhood leukemias, and the ICDT 2014 Test of Time Award for his work on naturally embedded query languages. He co-founded Molecular Connections, an information extraction and curation services company in India, and oversaw its steady growth over the past decade to nearly 2000 research engineers, scientists, and curators. He serves/served on the editorial boards of journals in computer science (Information Systems and TBD), biology (Biology Direct), and computational biology (Bioinformatics, JBCB, and TCBB). He is also an ACM Books Area Ed itor. He received his BSc(Eng) in 1988 from Imperial College London and his PhD in 1994 from University of Pennsylvania.