Why Knowledge when Data Suffices

Abstract

According to the classic knowledge pyramid, we turn the data we collect into information by applying its context. We then interpret the information to derive knowledge from it. Our focus on knowledge management stem from our belief that knowledge is what provides value to our endeavors. Is this paradigm still true with the explosive growth in Big Data? One of the most obvious examples is language translation. By employing machine learning on the massive multilingual text data, Google Translate, without natural language understanding, outperforms traditional natural language processing (NLP) methods when it comes to translation. Medical science is another good example. Since the sequencing of the human genome in 2003, we have dreamed about treating patients more effectively based on their genomic profile. Such a dream remains elusive due to the complexity of system biology. On the other hand, major progress can be and has been made in "targeted medicine" with machine learning on the accumulated patient medical data. In essence, we can uncover ways to help patient treatment directly from the data without knowing how and why it works precisely. Using Big Data to derive value brings another set of management problems, namely the heterogeneous nature of data sources, different taxonomies, the enormous size of data volume, and large-scale data analytic processing requirements. We will discuss all these issues and show some examples at this talk.