What is Data Prototyping?
Data Prototyping is a term used to describe a technique whereby single or multiple data sources are transformed into a resultant dataset without any operational systems being impacted.
Why is Data Prototyping important?
A common reason for carrying out data prototyping is to deliver a more agile approach to data management. By regularly refining rules and observing the final dataset created, you can arrive at a higher quality output dataset.
Data prototyping can be used in many scenarios but is particularly useful during:
- Data migrations
- Data integration initiatives
- Application implementation projects
What is an example of Data Prototyping?
Data migration analysts may wish to verify what will happen when one or more data sources are consolidated together to create a new target data source using various transformation rules. Data prototyping allows the consolidated data source to be created and then interrogated, typically using data profiling and data quality assessment tools.
Where problems are found, they can revise their source transformation rules or adapt their target structures to ensure an optimal data migration process.