The power of Data Cleaning

Massive amounts of data are generated these days which help companies or organizations make business decisions. But this data generated/collected is noisy (improper) which affects the accuracy of the predictions and analysis done. Also, the growing enthusiasm for making data-driven decisions is increasing the importance of getting precise and accurate results or predictions over the years. So, this is when data cleaning comes into the picture which aims to improve the quality of the data.

What is Data Cleaning?

Data Cleaning (also known as Data cleansing) is one of the most crucial initial steps of data analysis. It is the process of making the data ready for analysis by first detecting the unwanted, noisy, incorrect, incomplete, corrupted, or duplicate data within the dataset and then removing or correcting it.

Why is Data Cleaning important?

Data scientists or analysts spend 60-80% of their time cleaning and correcting the data. In simple words, it's like you're creating the foundation of a building, if you give your best at that step, woohoo! you're surely getting the best results. But, if your foundation isn't strong enough, sorry to say.. but the building will collapse.

Data cleaning also improves the quality of your data which improves the overall performance and productivity. Having no outdated or irrelevant data is all that is needed and once that's done, you have the best quality data available.

Data Cleaning doesn't just mean removing the irrelevant data. But it also involves correcting the data, as data analysis is commonly used to generate insights for a business for which the result has to be accurate. Just removing the irrelevant data might seem to be easy but it poses a problem too. As incomplete data will not generate accurate predictions or results of the analysis. And that's why one of the aims of data cleaning is to keep the dataset intact to generate accurate results and make the dataset reliable.

Data Cleaning Cycle: