What is data transformation?
Data transformation is the process of converting raw data from one format (often a source system) to another format (often a destination system). Data transformation can take various forms including constructive (adding or copying), destructive (removing duplicates or null values), aesthetic (standardizing) or structural (re-organizing or renaming).
The data transformation process can be automated or completed manually and typically occurs in the following manner:
- Discovery: To make decisions on what needs to happen to the data to transform it to an acceptable format, an understanding of the source data must first be achieved.
- Planning: A map of the transformation process is developed.
- Coding: Code is generated to automatically complete the transformation process.
- Execution: Using the code generated in step three, the data is converted to the desired format.
- Review: A detailed review of the output data is completed to ensure it was transformed correctly.
Data transformation is often used in conjunction with data mining, where the resulting information is analyzed to assist in making more accurate and efficient business decisions.
Benefits of data transformation
With vast amounts of data from a variety of sources now available to organizations, the ability for brands to efficiently and effectively mine data for actionable business intelligence is becoming increasingly attractive.
Data compatibility is a major concern when it comes to data mining, which is where data transformation shines. A successful data transformation strategy will provide the following benefits:
- Organization: It can be challenging to organize and store data that comes from various sources. Data transformation makes data more accessible for both humans and computers.
- Quality: Using “bad data” — duplicate data, null values or incompatible formats — to make business decisions can be extremely costly for organizations. Data transformation ensures the data being used for business intelligence is of the highest quality.
- Compatibility: It aids in the collation of data by making one source compatible with another. Transformed data can be utilized by various tools for different applications.
- Efficiency: When data is stored in a standardized format, it can be quickly and easily accessed and utilized.
- Fully leverage data: When data is easily accessible, it removes barriers that often stop businesses from realizing the full potential of their data