Why Is Data Cleaning so Important?

The important thing about the data cleaning process is that data accuracy and reliability will be at the center of the process of the information used for analysis. Let me explain that with a cooking example, you cannot feed the wrong ingredients to the recipe – the dish will be a mess. In data, we have to credit the “garbage in, garbage out” rule. Here’s why cleaning data is so important:

• Better Decisions: Dirty data generates lying output, or disinformation. With accurate data and clean data, your analysis connected to reality, and guides you for good options.
• Saved Time and Money: Incorrect data can make the right decision making very difficult, cause wasted efforts that might be directed towards unsuitable and wrong leads and solutions. Clean data saves the time and expense of redesigning processes that crashed due to a dirty data issue.
• Improved Efficiency: When data stays clean, the functioning of the whole system becomes easier. Dirty data leads to friction and inefficiency. The duplication of efforts to obtain reliable information will only add to the losses.

Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to improve their quality, accuracy, and reliability for analysis or other applications. It involves several steps aimed at detecting and rectifying various types of issues present in the data.