A company is a large-scale enterprise involved in many industries and has rich business data. Due to the wide range of data sources, there are some problems with data quality, such as incomplete data, data errors, and duplicate data. In order to improve the data quality, the company decides to perform data cleansing. Our data cleansing services utilize efficient and accurate algorithms and techniques combined with manual review and processing to ensure data quality and accuracy.
1、remove duplicate data and ensure data uniqueness.
2、fill in missing data to improve data completeness.
3、correct erroneous data to ensure data accuracy
First of all, data preprocessing, including data format conversion, data screening and data de-duplication. For example, convert text-based data to numerical data, screen out valid data, remove duplicate data, etc.
Next, data cleaning is performed, using the following methods:
- Remove duplicate data: Use unique indexes or temporary tables to remove duplicate data and ensure the uniqueness of the data.
- Completing missing data: Use historical
values, average values, maximum values, minimum values, etc., to complete the missing data and improve data integrity.
- Correct erroneous data: Use business rules and data dictionaries, etc. to correct erroneous data and ensure
data accuracy.
After cleaning, validate the data to ensure that the quality of the data has been improved. Validation includes the following:
- Checking the completeness and accuracy of the data to ensure that the data has been correctly cleaned;
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Checking the consistency of the data to ensure that there are no conflicts in the data.
- Checking the readability of the data to ensure that it is easy to understand.
Finally, the cleaned data is backed up to prevent data loss or corruption.
By performing the above cleaning process on the dataset, we can get a clean and accurate dataset, which provides strong support for subsequent data analysis, modeling and decision-making. Data cleaning is an important part of ensuring data quality and plays an important role in improving the effectiveness of data analysis and the accuracy of the results.