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Variations in Handling Information
A key factor that defines the distinction between people and
computers is the ability to handle information. There is an order in
knowledge management known as the DIKW hierarchy: data,
information, knowledge, and wisdom. Partly inspired by the play The
Rock by T. S. Eliot, there have been numerous versions from
different authors. We use a six-tier model by adding noise and
insight into the DIKW framework (see 
Figure 7.2
).
Data, information, and knowledge are the established domains of
machines. Computers have become extremely good at processing
disorganized data into meaningful information with speed and
almost limitless capacity. The new resulting information is then
added to a reservoir of related information and other known contexts
to develop what is called knowledge. Computers organize and

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manage this wealth of knowledge in their storage and can retrieve it
whenever needed. The quantitative nature and large volume of
processing make machines ideal for this sort of work.
On the other hand, the three somewhat fuzzy and intuitive elements
(noise, insight, and wisdom) are within the human realm. Noise is a
distortion or deviation in data and can be a major distraction when
grouping the data in structured clusters. An example of this is an
outlier, which computers can quickly identify as a significant
deviation from other datasets. But an outlier can either be a valid
variability or an error. And the only way to determine it is through a
subjective judgment based on an understanding of the real world.
This is where humans—businesspeople instead of data scientists—
play a role in deciding whether to keep or filter out the outlier.