Data cleansing, also known as data cleaning or data scrubbing, is the process of identifying and correcting errors or inconsistencies in datasets to improve their accuracy and reliability. It is a crucial step in data management and analysis to ensure that the data you work with is of high quality. Here's a general overview of the data cleaning process.
Data cleaning is an iterative process, and it may require multiple rounds of cleaning and validation to achieve high-quality data. It is a fundamental step in data preparation for various applications, including data analysis, machine learning, reporting, and decision-making.
Gather data from various sources, such as databases, spreadsheets, or external systems.
Review the data to identify potential issues, such as missing values, duplicates, inaccuracies, and inconsistencies.
Create summary statistics and data profiles to understand the characteristics of the datasets, such as the data types, ranges, and distributions.
Address missing data by deciding on a strategy, which may involve imputing missing values, removing rows or columns with too much missing data, or flagging missing values.
Identify and eliminate duplicate records or entries to prevent redundancy and errors in analysis.
Identify and address outliers that may skew the data and impact the results. You can choose to remove, transform, or impute these values.
Standardize data formats, units, and representations to ensure consistency. For example, converting dates to a uniform format or handling units like currency or measurements.
Transform data as needed, such as encoding categorical variables, scaling numerical data, or aggregating data into meaningful groups.
Check the data for integrity and correctness by applying validation rules, business rules, and cross-referencing with external sources.
Enhance the datasets by incorporating external data sources or additional information to improve its quality and value.
Ensure that data from different sources or systems are consistent and reconcile any discrepancies.
Maintain detailed documentation of the data cleansing process, including the steps taken, decisions made, and the rationale behind them.
Evaluate the quality of the cleansed data using metrics and validation techniques to ensure that it meets the desired standards.
Establish a process for ongoing data quality monitoring to prevent the re-occurrence of issues.
Store the cleaned data in a secure and well-organized manner for easy access and analysis.
