CSV
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      What are some common CSV mistakes?

      As powerful as it can be, custom data from CSV files can also be very tricky. This page aims to be the troubleshooting center for any problem encountered during CSV operations.

      1 – Template Definition Errors

      These errors will prevent the template from being used as a data source. The most common errors seen in regards to the CSV template can be found here: What can I do with the CSV Files Manager

      2 – No Data/Bad Data in the Widgets

      Having empty widgets means no data. There are a number of possible explanations for this:

      1. No Uploaded Data: this is the most frequent cause of an empty widget. After defining the CSV template, linking the CSV data source to a dashboard, and creating the widget(s) in that dashboard, you still need to go into the CSV Files Manager. You then need to upload data for that CSV template. Alternatively, you can also click on “Resend”, if you already uploaded the file at least once.
      2. Bad Customer Field ID: if the Customer Field ID set in the CSV file is not the same as the “Key” entered when linking the CSV data source to a dashboard, no data will be uploaded. DashThis will not recognize the file as being the same template, and will simply not retrieve the data.
      3. Bad Number Format: if the number format for a given metric is different in the uploaded data compared to the CSV template no data will be uploaded. Hence, the widget will look either empty or have outdated data.
        • The currency symbol in the cell will prevent DashThis from parsing this cell’s data
        • Mismatched number of decimals
        • Mismatched number type (integer instead of decimals, etc)
      4. Incorrect period selected: if you do not see data in your widgets, try switching to a period where data was uploaded for this CSV template.
      5. Multiple files with overlapping dates: DashThis will treat files with overlapping dates as different files. Thus, the data will be added for overlapping days. For example, if you have a file ranging from May 1st to May 16th, then another from May 12th to May 31st, you will get double data for days between May 12th and 16th.
      6. Removed/added/modified columns/rows positions: changing the column number or the number of header lines can lead to bad results. It might be possible to correct this in the back end, but this is not always the case.