There are many times when we need to get large amounts of data into a system, but the front end of the application doesn’t provide a way to do it quickly. This creates a dilemma of how to get hundreds or thousands of items loaded, without doing it by hand, which could take days. Alteryx is a solution that makes data movement, blending, and loading quick and easy. It is also a solution that requires ZERO SQL, or other coding skills so more users can be part of the process.
During a new implementation of PowerSchool’s eFinancePLUS, a district wanted to use the system’s ability to calculate the workers’ compensation premiums during the pay run processing. They created the appropriate codes and assigned them to the right job classes for the processing to occur. However, the codes were not able to be added en masse to individual employee records for later use in running informational reports.
Because the district had added the codes to employee records in a spreadsheet, an Alteryx workflow was created to update the appropriate table in the database with the assigned codes. An explanation of the workflow process follows.
The actual SQL server/database connection information is hard-coded in the workflow.
The demographic conversion spreadsheet with employee information has many columns, but it must have the [EMPLOYEE NUMBER] and [WORKCOMP] columns. It can only contain one sheet.





!IsNull([EMPLOYEE NUMBER]) and !IsNull([WORKCOMP])
This tool has two outputs – T(rue) and F(alse). Nothing is done with the records that don’t meet the criteria and that portion of the workflow ends. The rows that meet the condition are passed to the ‘Join’ Tool.

Select distinct wkr_comp From clstable
A list of distinct values is returned as the same code could be assigned to more than one job class. The resulting rows are passed to the ‘Join’ Tool.

The ‘Left’ output contains the database reference table values that don’t match any of the employee records. Nothing is done with these records and this portion of the workflow ends.
The ‘Right’ output contains the employee records with a workers’ comp code that has not been assigned to any job class records. Since pay run processing is based on values in the job class table, if codes assigned to employees are NOT assigned to any job class records, no processing occurs, and having employees assigned to these codes is irrelevant. These records will be displayed for the user to review.
The ‘Join’ output contains the employee records with a workers’ comp code that has been assigned to at least one job class. These records can be used to update the database table.






An ‘Action’ Interface Tool is used to tell Alteryx what to do with the information. In this case, the action type is ‘Update Value (Default)’. The ‘Replace a specific string’ option on the configuration is checked and the file path of the default file name for the report is entered in the text box. This information will be replaced by what the user has selected.

Because Interface Tools are used, Alteryx automatically sets the workflow configuration to ‘Analytic App’. (Clicking anywhere on the canvas – not a tool – displays the workflow configuration on the left.)
When the app is run by the user, a dialog box displays:
The user selects both the spreadsheet of employee information to import as well as the directory to save the PDF report in.
Clicking the ‘Finish’ button runs the workflow. If there are records needing review by the user, this displays when the workflow ends:
Clicking ‘OK’ opens the report. The user can also click the ‘Show Output Log’ link to see the number of records processed at each step.
If there are no records needing review by the user, this displays when the workflow ends:
The user is returned to the main dialog screen where they can run the workflow again with a different input file if desired, or click the ‘Exit’ button to close the app.
Using Alteryx to create a workflow allows the user to update data in a table without knowing any SQL. They can run the process multiple times if they decide to change or add codes after the initial run. Built-in data checking can also prevent errors in data later.
For additional information, please contact us at info@capitalizeconsulting.com!



