![]() ![]() Upon opening Power BI we first select Get data, select the Folder and input the File Path of the folder.Īfter clicking OK Power BI will show the files within the folder that it has found. Alteryx will automatically populate the Connect a File or Database string in the Input Data tool, we can use an * (asterisk) to bring in multiple files as a wildcard union based on the filename. Then we select one of the files to input. Upon opening Alteryx, we first drag in the Input Data tool. After selecting the Wildcard union we use an * (asterisk) to bring in multiple files based on the filename. ![]() To combine each of the monthly snapshots into a single table, we have the option of using a Wildcard union. ![]() We simply click Connect to Data, and select the file we wish to input. Upon opening Tableau Prep, connecting to data is simple. We are going to look at how to manage this in an automated way. Though we have only a sample of the files, there are many years of monthly data snapshots that require consolidation. Step 1 – Inputting multiple files (with the same format) Output the data ready for use with a visualization software.Flag the date in which a promotion occurred for an employee.Extract the date information from the filename.Consolidate the files into a single historical table.They informed us that the data is automatically generated as an excel file from the source system, and each file contains a snapshot of the data at the start of each month.Īfter spending some time understanding the data and considering the business requirements we’ve established that the technical requirements are as follows: The HR team has provided us with a sample of the data they use. This means we will not be using any scripts (though each of the softwares do offer the ability to add scripts). The business is keen to compare different Data Preparation software as part of a broader data transformation strategy, and have requested that we only utilise the native functionality of the tools to ensure a fair comparison. Ensure the longevity of the process independent of staff changes. ![]() Save 1 hour per month for the employee (plus variable additional time for fixing issues).Additionally, if the team member were to leave the business, the process knowledge may be lost.īy speaking with HR we have identified that automation of this process can: This leads to additional time being used to fix issues and complaints from senior management about the errors.įinally, the team member is the only person who fully understands the correct steps to execute this process, which has caused delays when the member has taken annual leave or sick leave. The team lead also mentioned that occasionally there is a risk of human error in the manual data preparation. This data is then sent higher up in the business to project workforce spend, ensure equality policies are observed, and understand the spread of roles across business units. One of the team leads has identified that a team member spends 1 hour per month manually preparing HR data in order to identify promotions. We have a centralised HR department that manages data related to our employees and they’ve reached out to us for support. Let’s imagine we are a large business with over 1,000 employees. How they manage more advanced data transformation logic.How they import multiple files at the same time.Each of these tools can be used for data preparation, however they do so in different ways. In this guide we’re going to look at how Tableau Prep, Alteryx and Power Query approach the same data challenge. ![]()
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