Data cleansing is a fundamental yet essential in the data analysis process. The quality of the analysis results depends on the accuracy and completeness of the data from the beginning. If the data used is inaccurate, incomplete, or duplicated, the analysis will yield inaccurate results and lead to incorrect decision-making.
The importance of data cleansing
- Increase the accuracy of the analysis
Data with incorrect values, inconsistent formats, or missing information can lead to flawed analytical models. For example, predicting sales based on data where "0" represents "no data" may result in inaccurate averages
- Reduce the risk of making wrong decisions
Using inaccurate data as the basis for strategy development, such as outdated customer information or incorrect addresses, can lead to the complete failure of a marketing campaign
- Build credibility
Clean data ensures that analysis results are acceptable at the team, management, and stakeholder levels, enabling the organization to confidently utilize the findings
Examples of common problems that occur if you don't cleanse your data:
- Duplicate Records
- Missing Values
- Incorrect value type, such as entering "สามสิบ" (thirty) instead of "30" (thirty for age)
- Inconsistencies, such as dates in different formats
summarize
Data cleansing is like "filtering water before drinking." No one wants to analyze data from dirty information, because even with the best analytical tools, if the starting data is wrong, you can't get accurate results. Good analysis must always start with "clean and reliable" data
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