When I first came across the term data mining and knowledge discovery in databases, I was excited and curious to find out what it was all about. I was excited because the term tends to convey a new field that is in the making. I was curious because I wondered what it was doing that the other fields of research, such as statistics and the broad field of artificial intelligence, were not doing. After reading up on the literature, I have come to realize that it is not much different from conventional data analysis. The commonly used definition of knowledge discovery in databases: “the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” is actually in line with the core mission of conventional data analysis. The process employed by conventional data analysis is by no means trivial, and the patterns in data to be unraveled have, of course, to be valid, novel, useful and understandable. Therefore, what is the commotion all about? Careful scrutiny of the main lines of research in data mining and knowledge discovery again told me that they are not much different from that of conventional data analysis. Putting aside data warehousing and database m- agement aspects, again a main area of research in conventional database research, the rest of the tasks in data mining are largely the main concerns of conventional data analysis.