Roster File Data Quality Checklist

  • Updated

In order to have your roster upload accepted by our platform, it is important to have data that is formatted correctly. Use this guide to prepare your file for upload and prevent common data file errors. Don't forget that you can always email your Panorama contact or if you run into any issues!

1. Match your column headers with the data file template. This is very important and will ensure that the platform recognizes all data correctly. If your column headers do not match the template, the platform will think required information is missing. For example, label your column "Student First Name" instead of just "First Name" so that the information can be linked to the student. When you upload your file, you will also have the opportunity to map columns that the platform doesn't recognize to make sure the data is read correctly.


(Figure 1.)

2. Ensure that school and/or district names match any previous data uploads. If not, you’ll create a new school or district rather than adding to the existing one! This can negatively impact your reporting experience. When a roster is uploaded in the Roster tool, the platform will notify you if new schools have been created by checking against school names that already exist in the platform from previous uploads.

Warning for New Schools.png                                                                                                                                                            

(Figure 2.)

3. Use only consistent, numeric values for grade levels. Only use K-12 to represent grade level. Avoid “grade 3” and “3rd”, for example.                                                                                                                                                                            

4. Check that you have consistent personal information across all rows in your data file. Consistent name, email and demographic info ensures that surveys and reports represent accurate data about your community. If this data is incorrect, it could result in your community not receiving your messages about the survey, or being incorrectly identified.   


(Figure 3.)


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5. Remove duplicate, empty, or incomplete fields. Check for and remove duplicate ID numbers, duplicate or empty email addresses, or email addresses that are incomplete. Missing or duplicate data will result in an error.

                                                                            (Figure 5.)                                                                      

6. Remove all blank rows and columns from your data file. The data upload will stop at the first blank row in your file, so only the data appearing above this blank row will be imported. Blank columns may also cause an error.


(Figure 6.)

7. Fill in blank cells in demographic columns. Using terms like "N/A" or "None" to note that demographic data does not apply to specific individuals allows you to disaggregate your reports based on this data. If the data point does not apply to any of your survey respondents, you can delete the entire column (see #6 above).

(Figure 7.)                                                                                                                                                                           

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