Data cleansing, also known as data cleaning or data scrubbing, is the process of removing data that is incorrect, duplicate, corrupted, incomplete, or incorrectly formatted from a dataset. This is a key step when preparing a dataset to be processed and analyzed, and there are specific tools used to perform it.
The reason why data cleansing is performed is that otherwise you risk getting inaccurate results from your data analysis and ultimately make decisions and formulate strategies based on inaccurate data. Data cleansing is a way of optimizing the accuracy of your dataset. However, it doesn’t just involve removing or deleting errors; it also includes fixing syntax and spelling errors, standardizing your dataset, and correcting other mistakes (e.g. empty fields).
Maintain a clean database
Why is it important to maintain a clean database? Because data-driven decision making is crucial in any organization and strategic setting, the data is at the core of any strategy or roadmap to success. Therefore, it’s important to make sure you have accurate and standardized data to base your decisions on. In two words: data quality.
Dirty data can lead a business to make wrong decisions and eventually waste money. Data cleansing is the key to ensuring your data is cleaned and ready to be processed, and that all incorrect information is uprooted from your dataset.
Typical cleaning process
What’s a good data cleansing process you can adopt?
- Watch for errors: Keep your eyes open, try to identify patterns, and notice what your errors are usually like. This will help you identify future errors.
- Standardize everything, in particular your data point of entry, so that you reduce the risk of duplication.
- Check for uniformity: Are you spelling the same word differently? One of the most common examples is: United States / US / U.S. / USA, etc.
- Test for accuracy: There are many useful tools out there to validate the accuracy of your data.
- Find duplicates: Also for this process, there are automated tools that can identify duplicates.