In my Computer Science courses, my primary goal is to elucidate the critical distinction between data and information.
Information is what the user wanted when he ask for the data.
Rarely does an end user desire an overwhelming deluge of raw data or Excel spreadsheets with countless rows. What they truly seek is a coherent and digestible representation that allows them to mentally grasp the underlying concept.
For instance, if your supervisor requests 3rd quarter sales data for the Midwest region, the last thing you should do is drop a mountain of sales receipts onto their desk. They're actually looking for a presentation that effectively conveys the meaningful insights within the data.
However, the journey doesn't end at information. A noteworthy article by Gene Bellinger, et al., expands the data-to-information concept into a more comprehensive hierarchy: Data -> Information -> Knowledge -> Understanding -> Wisdom
(http://www.systems-thinking.org/dikw/dikw.htm)
Efforts to transform data into information have long been the focus of spreadsheets, reports, and even three-dimensional presentations. Yet, transitioning from information to knowledge has posed a more formidable challenge.
Enter Data Mining, a powerful tool that facilitates this transition to the next level. For a comprehensive overview, Bill Palace provides an excellent resource at
http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm
Moreover, Data Mining has evolved beyond knowledge and into the realm of understanding, primarily driven by advancements in semantics (often referred to as the Semantic Web). A detailed exploration of this topic can be found in the Wikipedia article:
http://en.wikipedia.org/wiki/Semantic_Web.
