Manufacturing has evolved through several stages: From handmade parts, to interchangeable components, from scientific management and statistical process control to numerical control, and finally to intelligent machine tools and flexible production facilities. While it took over 200 years for traditional manufacturing to mature, semiconductor manufacturing advanced through all these stages in four short decades. More so than in other types of manufacturing, today's semiconductor manufacturing is "data driven" in many levels of abstraction. Much like traditional manufacturing, there is tremendous data infrastructure at the factory level, monitoring parts movement, recipes, consumables, etc. Unlike traditional manufacturing, however, there is also data driving the production at the process or tool level. Examples here include the sophisticated control and diagnosis databases that often describe individual process steps of critical nature, such as plasma etching, photolithography and Chemical-Mechanical Planarization. An additional example of unusual datasets in semiconductor manufacturing has to do with the large amount of wafer or work-piece data, collected to monitor tooling, to predict yield, to drive control applications, and to also drive the evolution of semiconductor manufacturing technology. This talk will examine this hierarchy of data in semiconductor manufacturing, and it will specifically focus on the interactions between the various levels. Examples will be given regarding critical deep sub-micron patterning steps of the semiconductor manufacturing sequence.