Business

Data modeling programs encourage coordination among various aspects of your knowledge engineering program. Whereas the data modeling initiatives of the late 1980s and 1990s were focused on predictive analysis; modern pattern discovery has less to do with prediction and more to do with actionable analysis. The truth is that complex systems are intrinsically difficult to predict beyond a certain degree.

What started as a paradigm shift in the 1970s has grown to a full-blown science. Chaos Theory describes the high sensitivities of complex systems to initial conditions. Popularized by the movie Jurassic Park, Chaos Theory is really the predecessor to a much more sweeping science, known as Complexity Theory. Many scientists, including Nobel laureate Murray Gell-Mann, have contributed to Complexity Theorys new approach to data modeling.

Data Modeling and Complexity Theory
One of my favorite Complexity theorists is a maverick scientist by the name of Stephen Wolfram. Wolfram wrote the 1300 page tome, A New Kind of Science, which discussed how complex systems can be quantified. Wolfram came to the startling conclusion that, once a system reaches a certain level of complexity, it can never achieve a higher degree of complexity.

This profound but simple theorem has applications for a wide variety of fields. As far as data mining is concerned, Wolframs theory suggests that predictive analysis may never work on complex patterns. After all, if all complex systems are equivalently complex, the only way to figure out a systems behavior is simply to perform an experiment and watch to see how it all unfolds.