Database Reference
In-Depth Information
have an ethical obligation to protect these individuals' rights. This requires the utmost care in
terms of information security. Simply because a government representative or contractor asks for
data does not mean it should be given.
Beyond technological security however, we must also consider our moral obligation to those
individuals behind the numbers. Recall the grocery store shopping card example given at the
beginning of this chapter. In order to encourage use of frequent shopper cards, grocery stores
frequently list two prices for items, one with use of the card and one without. For each individual,
the answer to this question may vary, however, answer it for yourself: At what price mark-up has
the grocery store crossed an ethical line between encouraging consumers to participate in frequent
shopper programs, and forcing them to participate in order to afford to buy groceries? Again, your
answer will be unique from others', however it is important to keep such moral obligations in mind
when gathering, storing and mining data.
The objectives hoped for through data mining activities should never justify unethical means of
achievement. Data mining can be a powerful tool for customer relationship management,
marketing, operations management, and production, however in all cases the human element must
be kept sharply in focus. When working long hours at a data mining task, interacting primarily
with hardware, software, and numbers, it can be easy to forget about the people, and therefore it is
so emphasized here.
CHAPTER SUMMARY
This chapter has introduced you to the discipline of data mining. Data mining brings statistical
and logical methods of analysis to large data sets for the purposes of describing them and using
them to create predictive models. Databases, data warehouses and data sets are all unique kinds of
digital record keeping systems, however, they do share many similarities. Data mining is generally
most effectively executed on data data sets, extracted from OLAP, rather than OLTP systems.
Both operational data and organizational data provide good starting points for data mining
activities, however both come with their own issues that may inhibit quality data mining activities.
These should be mitigated before beginning to mine the data. Finally, when mining data, it is
critical to remember the human factor behind manipulation of numbers and figures. Data miners
have an ethical responsibility to the individuals whose lives may be affected by the decisions that
are made as a result of data mining activities.
