How did Tom Khabaza come to lay down the laws of data mining? There’s something to be said for being first on the scene. Khabaza started data mining in the early 1990s, when few people had even heard of data mining, let alone tried it.
He began his career in psychology and gravitated to the study of cognition, human learning. Data mining has its roots in the attempt to simulate the complex process of human learning.
In truth, data mining is pretty crude compared to real human learning. But it’s fast and consistent.
Tom got involved in the development of some of the earliest software designed for data mining (software that grew into a commercial product still widely used today). He put data mining to work in practical applications, lots of them. And he was one of the first people to make a career as a data miner.
Tom has broken a lot of ground for everyone else. Are you interested in using data mining to predict customer churn (turnover)? Tom was a pioneer in that application. Perhaps you’re intrigued by the potential of data mining for law enforcement. Tom was one of the first to do that, too.
Then Tom delved into his data-mining experience and knowledge of psychology to define the guiding principles of data mining. His 9 Laws of Data Mining were an instant hit in the data-mining community (such a big hit that now you may come across articles about the 9 Laws that don’t even mention the originator).
That’s how Tom Khabaza became the Isaac Newton of data mining, a leader who provides inspiration and structure for others in the profession.