The main difference between data cleansing and data transformation is that the data cleansing is the process of removing the unwanted data from a dataset or database while the data transformation is the process of converting data from one format to another format.
A business organization stores data in different data sources. It is important to make decisions by analyzing the data. Analyzing data from multiple data sources is difficult. Therefore, business organizations use data warehouses. It is a central location that stores consolidated data from multiple databases. Data warehouses help to create reports, analyze data, visualize data and make valuable business decisions. In other words, data warehousing supports the overall business intelligence process. Data cleansing and data transformation are two techniques that are used in data warehousing. Data cleansing refers to eliminating meaningless data from the data set to improve data consistency while data transformation refers to converting data from one structure to another structure to make them easier for processing.
Key Areas Covered
1. What is Data Cleansing
– Definition, Functionality
2. What is Data Transformation
– Definition, Functionality
3. What is the Difference Between Data Cleansing and Data Transformation
– Comparison of Key Differences
Datebase, Data Cleansing, Data Transformation, Data Warehouse
What is Data Cleansing
A business organization uses various sources to store data. They can have different databases such as Oracle, MySQL, etc. It is difficult to analyze data in different data sources. Data warehousing provides a solution to this issue. It helps to collect, store and manage data from a variety of data sources into a central location called a data warehouse. The data warehouse gets data from transactional systems and various relational databases. Finally, this data is processed and analyzed to get meaningful business insights.
The data should be cleaned and transformed before loading into the warehouse. The extracted data from multiple sources can consist of meaningless data. Dummy values, contradictory data, absence of data are considered as meaningless data. These unnecessary data must be removed from the dataset. Overall, data cleaning will not just provide a clean dataset. It also brings data consistency to different sets of data that have merged from various data sources.
What is Data Transformation
After cleansing, the data is transformed into a suitable format. Data transformation helps to process the data easily. Data transforming can be simple or complex depending on the required changes on the data. Standardizing data, character set conversion, encoding handling, splitting or merging fields, conversion units of measurements into a standard format, aggregation, consolidation, delete duplicate data are some of the tasks involved in data transformation.
After completing the data transformation, the data is loaded into the data warehouse for processing. Finally, the senior management and data analysts can take decisions based on the processed data. Apart from data warehousing, data cleansing and data transforming are also used for statistical and mathematical operations.
Difference Between Data Cleansing and Data Transformation
Data cleansing is the process of detecting and removing corrupted or inaccurate records from a record set, table or database while the data transformation is the process of converting data from one format or structure into another format or structure.
Furthermore, data cleansing helps to clean the dataset and improve the data consistency while data transformation helps to make data processing easier.
Data cleansing and data transformation are two techniques used in data warehousing. The difference between data cleansing and data transformation is that the data cleansing is the process of removing unwanted data from a dataset or database while the data transformation is the process of converting data from one format to another format.
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4. ETL Tutorial | Extract Transform and Load, Vikram Takkar, 8 Sept. 2015, Available here.
1. “Dataset-survey R-MASS package” – public information (Public Domain) via Commons Wikimedia