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DATA CLEANSING

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Data Cleansing Challenges in Data Mining
Name
Institution

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Data Cleaning Challenges in Data Mining
Data quality is a crucial aspect of decision-making from data. Organizations are shifting
to data mining to gather timely and relevant data (Ridzuan& Zainon, 2019). However, most of
the data are unstructured and come from various sources such as customer feedback, social
media, and website reviews (Koszalinski et al., 2018). Therefore, data quality is a problem
because of the scattered data sources. Organizations must strive to get clean data to make
accurate decisions. However, getting clean data in data mining is the main issue (Lu, 2019). The
paper presents an explanation of some of the challenges organizations face while cleaning data in
data mining.
Organizations must collect the right data instead of the large data set. Data cleaning to
mine only the clean data is often faced with various problems during data scrubbing (Lu, 2019).
The organization has to understand the challenges so that they can design solutions to them.
The first data cleaning challenge is that data is never static. Organizations retrieve data
from various sources, which makes it continuously flowing. The ever-moving data must be
structured and arran...