Four Ways To Clean Data By Using Data Cleansing Techniques

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4 WAYS TO CLEAN DATA BY USING DATA CLEANSING
TECHNIQUES
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INTRODUCTION
Are you looking for ways to clean data by using
data cleansing services? If yes, then you have
arrived at the perfect place. In this blog, we
are going to discuss that all, but first let's
understand what exactly is data cleaning.   What
do you mean by data cleansing? Data cleaning
refers to the process of fixing and detecting the
errors or issues in connection with a dataset.
But what exactly are the factors which are
covered under the data issues? There are
generally four factors that come under data
cleansing such as data correctness, data
completeness, data relevance, and data
accuracy.   That's why data cleansing refers to
the sequence of steps that makes sure that the
underlying data used for the modeling or high-end
analysis is correct, relevant, complete, and
accurate.
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Ways To Clean Data Using Cleansing Techniques
Here are some of the ways by which you can clean
the data. They are as follows 1.Unexpected
Whitespaces within the Content This is the most
common issue with most of the data structures
where the unexpected and random spaces come in
the middle and twist the actual meaning of the
data. For instance, 'this is a dog' and 'this is
a dog' would be contemplated as separate data.
This issue can be solved by using a TRIM function
which clears all such unexpected data. 2.
Eliminate The Blank Data Blank data is another
serious concern for most analysts because it
reduces the quality of data. For instance, A
record where the 6 fields out of 10 fields are
empty cannot be used for the targeted analysis.
This blank data should be treated in the data
collection stage where they must design the
brilliant forms with the scheduled fields so that
it doesn't acknowledge the null values.
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3.Numbers Which Gets Converted Into Text At The
Time Of Exporting Most data scientists generally
face issues where the numerical functions stop
working. This happens because the number gets
converted into texts automatically. To resolve
this issue, data cleansing companies can make use
of the VALUE method. 4.Highlighting Incorrect
Data In the big datasets, there are a lot of
fields where the error handling is not done the
right way. Because of this, there can be data
errors such as VALUE or N/A, etc, which can
spoil the whole content. Moreover, if these
fields are used anywhere else, it can lead to
errors in other calculations as well. That's why
it's necessary to fix the issue as soon as you
recognize it. One of the best ways to do this is
by using the IFERROR operator and assigning a
default value to that particular field in case of
any errors in the calculation.   So, here we go!
We have listed down some of the ways by which you
can clean data by making use of the data
cleansing services. So, what are you waiting for?
Clean the data with the help of data cleansing
companies as soon as possible to avoid further
errors.
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