Received data and derived data are categories in which various kinds of data can be sorted into. These two categories have different uses due to the differences in their origins. Received data usually come from primary sources that were created to be used as data. Examples include financial ledgers and membership rosters, even census data. Derived data, meanwhile, are calculated from base data, thus considered “derived.” Both of these types of data can be tidied into a format that is machine readable, which would facilitate the data analysis process.
I think that tidying data is useful for historians because of the previously mentioned effect of allowing data to be machine readable and facilitating data analysis, which can reveal patterns and trends that wouldn’t otherwise be found through manual work. It can cut down on time that could be spent on actually making interpretations and discovering insights from the wealth of historical data that is available and evolving day by day. The form in which the data was received or derived is significant because it shapes the way people can interact with and analyze the data. For example, if there is a data set that is arranged into long, skinny columns, it would be best to analyze the data using a tool like Python rather than Microsoft Excel.