Data Conversion

Data Conversion is mostly confused with Data Transformation.

In Data Conversion, data is not changed whereas in Data Transformation, data can be changed based on business requirements for certain decision making.

• Data Conversion can be used to standardize different formats into one unified format.
• Data Conversion can be, to convert one format into different formats.
• Data Conversion can be, to convert one format to another format as per business requirement.
• Data Conversion can be, to convert one data model to another using Data Transformation process for business ease of use.
• Data Conversion can be, to convert data from data to Reporting and Dashboards formats.

Example of basic Data Conversion

• Convert weather data like from Celsius to Fahrenheit and vice versa.
• Conversion of all date formats in one format.
• Conversion of unstructured like image, pdf, video, audio etc. into semi or structured file formats
• If a field has numbers only and the data type is string, then better to convert it to number as processing is much faster on number data type as compared to string.
• If a field has predefined data like Male/ Female, Yes/ No etc. and its field data type is varchar or varchar 2 then better to convert it to char data type as processing is much faster on char data type as compared to varchar or varchar 2.

Above are few examples as in new era of Big Data where 90%+ datasets are based on Semi and Unstructured, its utmost important to convert it or standardize it before it can be cleaned or cleansed or scrubbed. Data Profiling also produces much better and faster results if data has been standardized. Same goes with Data Transformations or ETL or ELT process, if data is standardized or cleaned or cleansed or scrubbed etc., the results are better. Data Quality also worked best once data is standardized.

‘Data Governance add policies to make sure data is standardized.’