44264 Data Mining Process IT.docx - Running head THE DATA...

Running head: THE DATA MINING PROCESS IT 1
Module 02 Written Assignment - The Data Mining Process IT
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THE DATA MINING PROCESS IT 2
The Cross-Industry Standard Process For Data Mining
The Cross-Industry Standard Process for Data Mining, also known as CRISP-DM offers an
organized technique to planning data mining project. It’s a well-proven and robust approach. It is an
open standard procedure that defines common techniques utilized by data mining professionals. It’s the
most extensively utilized analytics framework (Huber, Wiemer, Schneider & Ihlenfeldt, 2019).
CRIS-DM breaks data mining process into 6 major phases as shown in the process matrix below.
The order of the stages isn’t stringent and moving backward and forth amongst various stages as it’s
always needed. The arrows within the process diagram show the most frequent and vital dependencies
amongst stages. The outside circle in the diagram indicates the recurring nature of the data mining. The
process of data mining carries on following the deployment of a solution. The learned lessons all
through the process could provoke new, generally more focused corporate questions, and future
processes of data mining will profit from the previous process experiences.
Diagram 1: CRISP-DM phases

THE DATA MINING PROCESS IT 3
Phase 1: Business Understanding
The 1
st
phase of CRISP-DM process entails understanding what you require accomplishing from
business viewpoint. Your company could have a competing objective and constraint that requires being
appropriately balanced. The objective of this phase entails uncovering crucial factors that may impact
the result of a project. Overlooking this phase may imply that much effort is placed in generating the
correct answers to the inappropriate questions (Hou, 2018). An example of the processes involved in
this step includes:
 Firstly, we require comprehending the requirements. After that, we’ve to establish the