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Data wrangling is a specific type of data management that as arisen out of new software capabilities introducing large, messy and diverse data sets that need to go into a service-oriented architecture (SOA) for the purposes of analytics and use. Data wrangling generally involves many different sophisticated techniques for handling irregular or diverse data and manipulating it for business use cases.
It may sound like an informal term, but data wrangling actually occupies a particular space in data management. One helpful way to understand data wrangling is to contrast it with the often more formal extract, transform and load (ETL) methodology. Data wrangling has different aspects and use cases than ETL. It is often done by skilled data scientists or others close to the pipeline. In some ways, data wrangling could be called a type of "open source" ETL in that those engineers dealing with the data may be more "hands-on" or use more manual methods of extraction.
For those who really understand the refined processes by which diverse data gets culled, sorted and fed into enterprise architectures, data wrangling is actually a very important topic. IT professionals look at a vast array of tools, resources and techniques to bring value from messy, raw or unstructured data.