Note: When you clean your data with Data Interpreter, Data Interpreter cleans all the data associated with a connection in the data source.
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Turn on Data Interpreter and review resultsįrom the Connect pane, connect to an Excel spreadsheet or other connector that supports Data Interpreter such as Text (.csv) files, PDF files or Google sheets.ĭrag a table to the canvas (if needed), then on the Data Source page, in the left pane, select the Use Data Interpreter check box to see if Data Interpreter can help clean up your data.
Note: If your data needs more cleaning than what Data Interpreter can help you with, try Tableau Prep (Link opens in a new window). Then, you can make any necessary adjustments.Īfter you select the data that you want to work with, you might also need to do some additional cleaning steps like pivoting your data, splitting fields, or adding filters to get the data in the shape you want before starting your analysis. It can even detect additional tables and sub-tables so that you can work with a subset of your data independently of the other data.Īfter Data Interpreter has done its magic, you can check its work to make sure it captured the data that you wanted and identified it correctly. It can detect things like titles, notes, footers, empty cells, and so on and bypass them to identify the actual fields and values in your data set. What does Data Interpreter do?ĭata Interpreter can give you a head start when cleaning your data. Tip: Though Tableau's Excel add-in is no longer supported, Data Interpreter can help you reshape your data for analysis in Tableau. When you want to analyze this data in Tableau, these aesthetically pleasing attributes make it very difficult for Tableau to interpret your data. To make your spreadsheets easy to read, you might include things like titles, stacked headers, notes, maybe empty rows and columns to add white space, and you probably have multiple tabs of data too. When you track data in Excel spreadsheets, you create them with the human interface in mind.
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