What is Data Cleansing?
Data cleansing, also known as data scrubbing, is the process of identifying and correcting (or removing) inaccurate records from a dataset. In the context of
business, it ensures that data is accurate, consistent, and usable for analysis and decision-making.
Duplicate records Missing or incomplete data
Inaccurate or outdated information
Inconsistent data formats
Spelling and typographical errors
Data Auditing: Assessing the dataset to identify errors and inconsistencies.
Data Standardization: Ensuring that data follows a consistent format.
Data Deduplication: Removing or merging duplicate records.
Data Enrichment: Adding missing information to records.
Data Validation: Verifying the accuracy and completeness of data.
Data Transformation: Converting data into a usable format.
High volume of data requiring cleaning
Complexity of data from various sources
Time-consuming and labor-intensive processes
Need for specialized skills and tools
Maintaining data integrity during the cleansing process
Conclusion
Data cleansing is a vital practice in business for ensuring that data is accurate, reliable, and usable. By addressing common issues and leveraging appropriate tools, businesses can reap significant benefits, including improved decision-making, efficiency, and customer satisfaction. However, it is important to be aware of the challenges and adopt strategies to mitigate them for effective data management.