The global data cleaning tools market is projected to grow significantly due to increased digitization during the COVID-19 pandemic, highlighting the necessity for data cleansing to remove inaccuracies and duplicates. Data cleaning is essential for accurate analysis, helping businesses derive insights while avoiding erroneous conclusions. Implementing effective data cleaning processes leads to improved data quality, increased productivity, and better decision-making capabilities.

1. The Growing Importance Of
Data Cleaning

2. The global data cleaning tools market is all set to see a meteoric rise in
the coming years following a rise in the digitization of global business in
the ongoing COVID-19 pandemic. Know more about the growing
importance of Data Cleaning in analytics.
Data cleansing tools are needed to remove the duplicate, inaccurate data
from databases.

3. The pandemic has become a catalyst for the rising need for data
cleansing tools. Since businesses globally are now forced to move online,
be it telecom, retail, banking, or even government departments for that
matter, the requirement for such tools is being felt even more.

4. What Is Data Cleaning?
Data cleaning itself is the process of deleting incorrect, wrongly
formatted, and incomplete data within a dataset. Such data leads to false
conclusions, making even the most sophisticated algorithm fail. Data
cleansing tools use sophisticated frameworks to maintain reliable
enterprise data.

5. Solutions for data quality, include master data management, data
deduplication, customer contact data verification and correction,
geocoding, data integration, and data management.
One more outcome of a data cleaning process is the standardization of
enterprise data. When done correctly, it results in information that
can be acted upon without any more course correction to another data
system or person.

6. How Do You Clean Data?
Like any such process, cleaning data requires technique and as well as
accompanying tools. The techniques may vary since it is related to the
types of data your enterprise, and so the tools to deploy them.
Here are the first steps to tackle poor data:
Inspect, clean, and verify. The first step is to inspect the incoming
data to detect inconsistent data.

7. This is followed by data cleaning, which is to remove the anomalies,
followed by inspecting the results to verify correctness.
Steps in Data Cleaning
1. Identify data that needs to be cleaned and remove duplicate
observations
Use your data cleaning strategy to identify the data sets that have to be
cleaned. This is the primary responsibility of data stewards, individuals
tasked with maintaining the flow and the quality of data.

8. Among the first steps here are to start deleting unwanted, irrelevant, and
duplicate observations from your datasets. The reason why deduplication
is first on the list is that duplicate observations occur most during data
collection. It’s like nipping the problem in the bud. Duplicate data also
flows in when you combine datasets from multiple places, received
perhaps from multiple channels.

9. Unwanted observations are those datasets that may be correct but do not
conform with the specific problem you are trying to analyze. So if you
are looking for patterns of young girls spending online, any data that
includes teenage boys is irrelevant.
2. Fix structural mistakes
Errors in the data structure are weird naming conventions, typos, and
some such inconsistencies.

10. 3. Set data cleansing techniques
Which data cleansing techniques does your enterprise want to
deploy? For this, you need to discuss with various teams and come up
with enterprise-wide rules that will help transform incoming data into a
clean state. This planning including steps like what part of the process to
automate, and not.

11. 4. Filter outliers and fix missing data
Outliers are one-off observations that do not seem to fit within the data
that’s being analyzed. Improper entry of data could be one reason for it.
While doing so, however, do remember that just because an outlier
exists, doesn’t mean it is not true. Outliers may or may not be false but
they may prove to be irrelevant you’re your analysis so consider
removing them.

12. Missing data is another aspect you need to factor in. You may either
drop the observations that have missing values, or you may input the
missing value based on other observations. Dropping a value may end
up in losing information while adding a presumptive input means
risking losing data integrity so be careful with both tactics.

13. 5. Implement processes
Once the above is settled, you need to move to the next step, which is
the actual implementation of the new data cleansing process. The
questions here that need to be asked and answered are:
a. Does your data make complete sense now?
b. Does the data follow the relevant rules for its category or class?
c. Does it prove/disprove your working theory?

14. Eventually, you need to be confident about your testing methodology
and processes, which will be evident in the results. If adjustments have
to be made in the procedure, they have to be done and then the entire
process has to be “fixed” in place. Periodic re-evaluation of the data
cleansing processes and techniques must be made by your data
stewards or data governance team, especially when you add new data
systems or even acquire new business.

15. Call it data cleaning, data munging, or data wrangling, the aim is to
transform data from a raw format to a format that is consistent with
your database and use case.
Why Is Data Cleaning Required In The First Place? What Are The
Benefits?
The answer in short would be: to obtain a template for handling your
enterprise’s data. Not many get this: data cleaning is an extremely
important step in the chain of data analytics.

16. Because its importance is not understood, it is often neglected. The
result: erroneous analysis of your data, which translates into a waste of
time and money, and other resources. Having clean data can help in
performing the analysis faster, saving precious time.
Why data cleaning is required is because all incoming data is prone to
duplication, mislabeling, missing value, and so on. The oft-quoted line:
Garbage in means garbage out explains the importance of data
cleansing very succinctly.

17. Benefits of data cleaning include:
• Deletion of errors in the database
• Better reporting to understand where the errors are emanating from
• The eventual increase in productivity because of the supply of high-
quality data in your decision-making

18. What Is The Importance Of Data Cleaning In Analytics?
Data cleansing is the first crucial step for any business that wants to
gain insights using data analytics. Clean data allows data analysts
scientists to get crucial insights before developing a new product or
service.
Cleaning of data helps an enterprise deal with data entry mistakes by
employees and systems that do so occasionally.

19. It helps adapt to market changes by making your information fit
changing customer demands. What’s more, data cleaning helps your
enterprise migrate to newer systems and in merging two or more data
streams.
Original Source: https://expressanalytics.com/blog/growing-
importance-of-data-cleaning/