Today, the world is undergoing a digital transformation. In the business world, large volumes of data are being constantly generated. Organizations need to convert data into different formats in order to maximize its value and understand, analyze, and utilize the information to make informed decisions. Data warehousing, which involves storing data from disparate sources in a central repository, is the first step in data analytics. Data cleansing and data transformation are two essential processes in data warehousing. Carried out by data conversion companies, these distinct processes make data more accurate and user friendly.
Let’s look at the differences between data cleansing and data transformation.
Also referred to as data scrubbing or data cleaning, data cleansing is an important process that needs to be done when transferring files from a database to a data warehouse. In a database, there could be information that lacks accuracy and is inconsistent, incomplete, duplicated, and redundant. Data cleansing is the process of removing unwanted data from the database to improve the consistency and accuracy of the files before they are transferred to the data warehouse.
The process of data cleaning includes:
- Standardizing data
- Identifying and fixing errors
- Removing incorrect data
- Correcting format
- Checking the accuracy of information
- Compiling all data information in a single area
The steps involved in the process of data cleansing are:
- Removing irrelevant observations
- Fixing errors in structure
- Filtering irrelevant or unwanted outliers
- Handling missing information
- Identifying the purpose of the data
Popular tools used to perform data cleansing include:
- Trifacta Wrangler
- Tibco Clarity
- Data Ladder
- Melissa Clean Suite
- Winpure Clean And Match
These tools simplify the data cleansing process and help you get the most out of your data.
Like data cleansing, data transformation is an important process that needs to be carried out before warehousing data. It is the process of converting data from one format to another. Data transformation preserves your files for future purposes with greater accuracy. This process changes the format, structure and values of data in a file. The process of data transformation involves:
- Data integration
- Data migration
- Data warehousing
- Data wrangling
The data transformation process, which makes it structured and accessible, maybe constructive, destructive, aesthetic or structural. The process of data transformation involves:
- Extraction and analysis
- Translating and mapping
- Filtering, aggregation and summarizing
Differences Between Data Cleaning and Data Transformation
|Data Cleansing||Data Transformation|
|Process of removing inaccurate data from the database||Process of converting data from one format to another|
|Makes data error-free||Makes data processing easier|
Organizations need both data cleansing and data transformation to maintain the accuracy of data in the data warehouse. As these processes require a great deal of attention to detail and are challenging to perform in-house, many businesses rely on a data conversion company for support. With years of experience in the field, a reliable business process outsourcing company can help businesses take full advantage of the data available to them.