Business Information Systems

Running head: BUSINESS INFORMATION SYSTEMS
BUSINESS INFORMATION SYSTEMS
Name of the Student
Name of the University
Author Note

1BUSINESS INFORMATION SYSTEMS
Part A:
Question 1:
The data mining is a technique for examining huge databases for extracting patterns or new
information from those. The CRISP-DM is the abbreviation of Cross-Industry-Standard
Process for Data Mining is the efficient data mining guiding process which was proved
beneficial in various industries (The-modeling-agency.com, 2019). The life cycle of data
mining project contains six phases, respective tasks of each phase and the relationships
between the tasks. The six phases of CRISP-DM process is shown by the following figure.
The phase sequence are not very much rigid and the back and forth movement between the
different phases is always the requirement. The result of one phase indicates the phase that is
going to be performed (Ibm.com, 2019). The dependency of most significant and frequent
phases are indicated by the arrows in above figure. The phases are
Business understanding: Understanding the requirement and objectives of a business and
conversion of those knowledge into the data mining problem and planning for solving or
finding the solutions is performed in the initial phase of data mining.
Data understanding: This phase involves data collection and the collected data is then
explored and familiarized by different visualizations, the quality problems are then identified

2BUSINESS INFORMATION SYSTEMS
and insights of the data is taken into account. The interesting patterns are also detected in
order to gain hypothesis of unknown information.
Data preparation: The activities that are needed to be performed to form the final datasheet
which is fed into the modelling tools are done in this phase (The-modeling-agency.com,
2019). The tasks in this phase are marinating record, table and selection of attributes,
transformation and filtration of data.
Modelling: In this particular phase different modelling techniques are chosen and then
applied to find the best model for the data.
Evaluation: The results from the developed models in the previous phase are evaluated and it
is checked that the results answering the objectives or requirements of business. Also, the
level confidence of the obtained results are also considered to estimate how much the model/s
explain the business objectives.
Deployment: Now, in the final phase the knowledge obtained from the results of developed
model/s is organized and then presented in some way so that it is usable by the customers.
This mainly involves application real models inside the decision making process of an
organization.
Question 2:
The data mining in business or other industries is very much important because it helps for
informative decision making process, provides detail insights of data and translates
information into business intelligence. The three elements of data mining is
1. Accuracy: The solution of the data mining process will be appropriate if the values are
of good quality. It is required that the sources from which the data is collected is
accurate or the conclusions made from the data mining will be erroneous (Cerami,