To understand what drives database design, you must understand the difference between data and information.
Data are raw facts. The word raw indicates that the facts have not yet been processed to reveal their meaning.
For example, suppose that a university tracks data on faculty members for reporting to accrediting bodies. To get
the data for each faculty member into the database, you would provide a screen to allow for convenient data entry,
complete with drop-down lists, combo boxes, option buttons, and other data-entry validation controls. Figure 1.1,
Panel A, shows a simple data-entry form from a software package named Sedona. When the data are entered
into the form and saved, they are placed in the underlying database as raw data, as shown in Figure 1.1, Panel B.
Although you now have the facts in hand, they are not particularly useful in this format. Reading through hundreds
of rows of data for faculty members does not provide much insight into the overall makeup of the faculty. Therefore,
you transform the raw data into a data summary like the one shown in Figure 1.1, Panel C. Now you can get
quick answers to questions such as “What percentage of the faculty in the Information Systems (INFS) department
are adjuncts?” In this case, you can quickly determine that 20% of the INFS faculty members are adjunct faculty.
Because graphics can enhance your ability to quickly extract meaning from data, you show the data summary pie
chart in Figure 1.1, Panel D.