Data cleansing (aka data cleaning or data scrubbing) is the act of making system data ready for analysis by removing inaccuracies or errors. This process prevents questionable and costly business decisions based on messy data.
Data volumes and sources have grown much bigger and are expected to scale up even quicker. Companies wish to access valuable data to make competitive and good business decisions. Data inputted into a system comes with the risk of errors, duplications, omissions, or simply being irrelevant. Furthermore, integrating information from multiple database systems across the entire enterprise means synchronizing different data requirements and standards, which can be chaotic. Data cleansing, either manually or automated, unifies data to be found and acted upon for business cases.
Data cleansing is a necessary preparation step to drive Industry 4.0 technologies such as the Internet of Things (IoT), machine learning, and artificial intelligence, which rely on real-time accurate data.
Other Definitions of Data Cleansing Include:
- Ordering messy datasets “riddled with noise, inaccuracies, and duplications.” (Paul Barba)
- Taking “collected data and making it usable in your preferred statistical software.” (Northeastern University
- “Improving Data Quality and utility by catching and correcting errors before [data] is transferred to a target database or data warehouse.” (DZone)
Data Cleansing Use Cases Include:
Businesses Do Data Cleansing to:
- Make data ready to “fuel the most valuable use cases”
- Prepare for an AI project
- Have reliable and accurate data for analysis
- Improve decision making
- Streamline business practices
- Increase revenue
- Prevent bias
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