Data cleansing, also known as “data cleaning” or “data scrubbing,” is the process of identifying and correcting errors or inconsistencies in datasets. It involves detecting and handling inaccuracies, incompleteness, duplicates, and other issues to improve data quality. The goal? Ensure the data is accurate, reliable, and ready for analysis or other business processes.

Think of data cleansing as giving your data a refreshing shower to ensure it is in tip-top shape.

Read More about Data Cleansing

Data cleansing is a means to ensure data accuracy and reliability. Learn more about it here.

How Is Data Cleansing Done?

Data cleansing typically involves several vital steps to ensure data accuracy and reliability. Take a look at each step below.

  1. Data auditing: Assess the overall quality of the existing data. Identify errors, inconsistencies, and missing information.
  1. Duplicate removal: Identify and eliminate duplicate records to avoid redundancy. Merge or remove entries that refer to the same entity.
  1. Handling missing data: Address missing values by filling them in or making decisions on handling them. Consider imputation techniques or removing rows with significant missing information.
  1. Standardization: Standardize data formats and units to ensure consistency. Convert data into a standard format for better analysis.
  1. Validation: Validate data against predefined rules or criteria to identify anomalies. Verify the accuracy of data entries and correct discrepancies.
  1. Correcting inconsistencies: Correct errors in data values, such as typos or inaccurate information. Ensure that the data adheres to defined standards.
  1. Data transformation: Transform the data into a suitable format for analysis or reporting. Convert data types and structures as needed.
  1. Normalization: Normalize the data to reduce redundancy and improve efficiency. Ensure the data follows normalization principles to minimize anomalies.
  1. Quality assurance: Perform quality checks to validate the effectiveness of the cleansing process. Ensure the data meets predefined quality standards.
  1. Documentation: Document the changes made during the cleansing process. Maintain a record of the data cleaning steps for future reference.
Data Cleansing Steps

By following these steps, you can enhance the overall quality of your data and make it more reliable for analysis and decision-making. It is like giving your data a spa day. Note that different databases and software may require varying best practices. Salesforce data cleansing, for example, follows three major steps.

Who Typically Performs Data Cleansing?

Individuals or teams with expertise in data management, analysis, and quality assurance typically perform data cleansing. Here are some roles and professionals who may be involved in the process.

  • Data analysts: They often play a crucial role in cleaning and preparing data for analysis. They have the skills to identify patterns, outliers, and inconsistencies in datasets.
  • Data scientists: They may be involved in data cleansing as part of their broader responsibilities in data exploration, feature engineering, and model development.
  • Data engineers: They focus on developing and maintaining data architectures. They may be responsible for implementing automated processes for data cleansing and transformation.
  • Database administrators (DBAs): They manage and maintain databases. They may ensure data integrity, identify and resolve issues, and optimize data structures.
  • Data quality analysts: Professionals specializing in data quality analysis focus on evaluating and improving data quality. They may design and implement data quality metrics and procedures.
  • Data stewards: They are individuals within an organization who are responsible for managing and governing data. They may oversee data quality initiatives, including data cleansing.
  • Business analysts: They work closely with data to derive insights for decision-making. They may participate in data cleansing to ensure the data aligns with business requirements.
  • IT professionals: They, including those responsible for system integration and maintenance, may be involved in data cleansing activities, especially when dealing with data across different systems.
  • Domain experts: Subject matter experts with knowledge of the specific domain or industry may contribute to data cleansing by validating data against business rules and domain-specific standards.
  • Data quality managers: Individuals overseeing data quality within an organization may coordinate and manage data cleansing initiatives to maintain high standards.

These professionals’ involvement may vary depending on the organization’s size and structure, the data’s complexity, and the specific goals of data cleansing. Collaboration between these roles is often essential to ensure comprehensive and effective data cleansing.

Data Cleansing Example

Consider a simple example of data cleansing involving an e-commerce company’s customer information dataset. The dataset may have various issues that need to be addressed. Here’s one way to go about that.

  • First, identify and remove duplicate entries for customers who accidentally created multiple accounts with the same information.
  • Fill in missing customer and email addresses or phone numbers using relevant information from other entries or external sources.
  • Standardize the format of phone numbers and addresses to ensure consistency.
  • Validate email addresses to ensure they follow a standard format and are valid.
  • Correct misspellings or typos in customer names, addresses, or product names.
  • Convert date formats to a standardized format for easier analysis.
  • Normalize product categories to ensure consistency in naming conventions (e.g., merging similar categories or standardizing capitalization).
  • Run data quality checks to identify outliers or anomalies that may indicate errors in the dataset.
  • Finally, document all the changes made during the data cleansing process, including the reasons for specific corrections and any data source used for validation.

By addressing these issues, the e-commerce company ensures its customer data is accurate, consistent, and ready for use in analytics, marketing campaigns, or other business processes. This example highlights the importance of data cleansing in maintaining high-quality and reliable data for effective decision-making.

Key Takeaways