Data Cleaning

Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting or removing errors, inaccuracies, discrepancies, and irregularities in datasets. These could include inconsistencies, duplicates, incorrect entries, missing values, and outliers, which could distort, mislead, or skew the analysis or application of the data.

How Data Cleaning works

Data cleaning works by following a structured set of procedures to ensure the accuracy and reliability of data. This usually happens in the following stages:

1. Data auditing: Involves analyzing the data to identify anomalies and irregularities using descriptive statistics or data visualization tools.
2. Workflow specification: This step defines the guidelines that will be used to correct and handle errors in the dataset.
3. Workflow execution: This stage implements the rules and procedures specified in the previous step. It involves replacing, modifying, or deleting the inconsistent or incorrect part of the data.
4. Post-processing and controlling: After cleaning, the data is re-examined to ensure the cleaning process has not introduced new errors or removed valuable information.

Automated tools, libraries, or programming languages like Python and R, are often used to carry out data cleaning, though it can also be done manually. Effective data cleaning ultimately results in higher quality data, fostering accurate and reliable decisions and interpretations in data analysis, machine learning, and other data-driven fields.