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Data wrangling is a method of gathering, choosing and transforming data to answer an analytical question. Often referred to as “munging” or data cleaning, data wrangling makes up approximately 80 percent of a data scientist’s time, with the rest devoted to modeling or exploration.
There is going to be a wide range in quality between different data sets. Some will be big data streams that contain unstructured data. Others will be structured (eg data fields are clear and consistent) but will include duplicate or irrelevant data. Other datasets may be in good condition, but so large as to require metrics which have been rolled up in a data warehouse or star or snowflake schema to allow analytic queries.
Data wrangling is something of the unspoken grunt work of data science. It takes time to clean data to the point that it can be used for analytics. These are some of the challenges you will face when data wrangling:
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