Data cleansing and normalization
Having high-quality data is essential in the data processing and transformation industry. Messy, inconsistent, or incorrect data can produce questionable conclusions and should be avoided at all costs. This is where the need for data normalization and cleansing becomes apparent. In this section, we’ll delve into these two methods and examine their significance in preserving data quality.
Data cleansing, also known as data scrubbing, is the process of inspecting data for mistakes and then fixing (or removing) them. Errors in data entry, technical difficulties, and even representational differences can all contribute to these problems. Your data will be more useful for analysis, reporting, and decision-making if you take the time to clean it first.
Here are a few examples of typical data-cleansing activities:
- Fixing misspelled words and typos
- Creating a universal time and date format
- Adding or editing data to complete a record...
