| How can a data-flooded manager get out of the “mire”? How can a confused decision maker pass through a “maze”?
How can an over-burdened problem solver clean up a “mess”? How can an exhausted scientist decipher a “myth”?
The answer is an interdisciplinary subject and a powerful tool known as data mining (DM). DM can turn data into
dollars; transform information into intelligence; change pattern into profit; and convert relationship into resources.
As the third branch of operations research and management science (OR/MS) and the third milestone of data
management, DM can help attack the third category of decision making by elevating our raw data into the third stage
of knowledge creation.
The term “third” has been mentioned four times above. Let’s go backward and look at the three stages of knowledge
creation. Managers are often drowning in data (the first stage) but starving for knowledge. A collection of data is not
information (the second stage); and a collection of information is not knowledge. Data begets information which begets
knowledge. The whole subject of DM has a synergy of its own and represents more than the sum of its parts.
There are three categories of decision making: structured, semi-structured and unstructured. Decision making
processes fall along a continuum that ranges from highly structured decisions (sometimes called programmed) to highly
unstructured (non-programmed) decisions (Turban et al., 2005, p. 12).
At one end of the spectrum, structured processes are routine and typically repetitive problems for which standard
solutions exist. Unfortunately, rather than being static, deterministic and simple, the majority of real world problems
are dynamic, probabilistic, and complex. Many professional and personal problems are classified as unstructured, or
marginally as semi-structured, or even in between, since the boundaries between them may not be crystal-clear.
In addition to developing normative models (such as linear programming, economic order quantity) for solving
structured (or programmed) problems, operation researchers and management scientists have created many descriptive
models, such as simulation and goal programming, to deal with semi-structured alternatives. Unstructured problems,
however, fall in a gray areas for which there are no cut-and-dry solution methods. The current two branches of OR/MS
hit a dead end with unstructured problems. |
