What is Data Cleaning?
Data Cleaning is the process of preparing the data by removing incorrect, incomplete or duplicated data within a dataset. It is more than removing the data. It standardizes datasets, identifies duplicate points, corrects mistakes such as empty fields, missing codes, fixing spellings, and syntax errors.
Not cleaning the data will hinder the process of analyzing the data and provide inaccurate insights. It is a foundational element of the data science basics, as it plays an important role in the analytical process and uncovering reliable answers. It also maximizes dataset accuracy without necessarily deleting information.
Why is Data Cleaning so important?
As businesses hold the personal information of many different people (customer or client), the organization must keep the data safe and organized. It’s important to have accurate information that can help you to get to know your audience better and help you to get the most out of your marketing efforts.
It also improves the data quality and increases overall productivity. When the data is clean, all outdated and incorrect information is gone, and all you have is the highest quality of information. It also allows the employees to focus on higher priority and productive tasks.
What are the benefits of Data Cleaning?
- Removal of errors when multiple data sources are at play.
- It helps maintain data quality and enables more accurate analysis that improves the decision-making process.
- Maintaining a clean and organized dataset minimizes compliance risks.
- It saves time and increases productivity.
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