KBI theory’s core concepts

KBI theory’s core concepts
Because of the controversies in the definition and usage
of each concept in the previous models, it is necessary to
provide a clear definition in order to better understand
these concepts and their relationship. First, we define
data as:
Data are the measure or description of objects or events,
usually referred to as a set of interrelated data items that
measure the attributes of the objects or events.
This definition is consistent with definitions commonly
used in many other IS studies. Statement S1, for instance,
is an example of data since it is the measure of the
inventory level of a particular item. Statement S2 is also
data because it is the description of the current weather
condition. Both statements are of the ‘there-is’ type, that
is, about the facts of some existing objects or events.
Many other examples can be found in various IS
literature, such as a person’s weight or the cost of a
business plan. It should be noted that the term ‘attributes’
is emphasized in the definition (e.g., as in EntityRelationship
modeling) because only attributes of the
objects or events are directly measured or described, and
not all aspects of an object or event may fall within the
interest of the information user.
In addition to the unstructured data statements such as
S1, data can also be represented in more structured ways
such as databases or data tables. For instance, an inventory
table with columns and rows may be used to show
the inventory levels of several items. Such a structured
approach increases the data management efficiency
and is a major endeavor in data modeling research
(Hirschheim et al., 1995). No matter which approach is
applied, the same data are measured or described,
although differences exist in the particular signs used to
represent the data (e.g., sentences vs tables). This further
supports our earlier assertion that data are different
from signs.