By submitting this form, you confirm that you agree to the storing and processing of your personal data by Salesforce as described in the Privacy Statement. By submitting this form, you acknowledge and agree that your personal data may be transferred to, stored, and processed on servers located outside of the People's Republic of China and that your personal data will be processed by Salesforce in accordance with the Privacy Statement. Accurate Turnover Rate – Uses calculations one and two to find our more accurate turnover rate.įinally, if we divide Terminations by the Average Daily Headcount, we get our accurate turnover rate.īy registering, you confirm that you agree to the processing of your personal data by Salesforce as described in the Privacy Statement. The AVG() around the entire calculation gives us the average daily headcount. We are including that lower level of granularity. We use the INCLUDE function here because our view looks at this data at the yearly level, but we want to consider headcount at the daily level. In our new, scaffolded dataset, we have a record for every day each employee worked at the organization. Average Daily Headcount – This calculation finds the daily headcount and then averages it. If so, you can ask Tableau to return the Employee ID and then take a count of those employees. Using logic, check when Calendar is equal to the Terminate Date. Terminations – This calculation gives us a count of terminated employees per year. In Tableau Desktop, you will write three calculations: 1. With the data prepped, you can output it and bring it into Tableau Desktop for analysis. Once we apply this using the ‘Filter data’ option in Prep, we’re left with 12,186 rows instead of the 3,145,728 we had before. You don’t have to manually add a record for every day. The date_dim table extends into the future, which is good. Finally, part three trims out any dates that haven’t happened yet.Part two trims dates post termination date.Part one trims dates prior to an employee’s hire date.To correct this, in the next step, filter out any unnecessary records with a calculation: Apply this to hundreds of employees, and you’re left with a huge data set. You’d have a record for every single day, running back to 1964, when you only need 30 records. Say you worked at a company for 30 days, but your date_dim table extended back to 1964, the year your company was founded. Now we can calculate measures like ‘average daily headcount’ rather than only being able to consider the headcount at the end of the year.īut, we are also left with many extraneous records. This allows us to ask the more complex questions necessary for an accurate turnover rate. This is helpful because we’ve changed the granularity of our date field and now have a record for every day that an employee worked at a company. This duplication occurs because we are joining every row of our first table to every row of the calendar table. Since the field we are using as our join key is just the number 1, the resulting table is going to have a much higher row count: 3,145,728 to be exact. Now you can join the two tables together. Next, connect to the date_dim table and do the same thing-create a dummy key field to join your fields on. This calculation will allow you to join to the date_dim table. In your first Clean step in Tableau Prep, create a dummy calculation: Start by bringing in your headcounts table-the table with one row per employee. Here is what the finished Prep flow looks like. But, you can also just create an Excel file that has the dates you need. If you’re connected to a database like SQL Server that is managed by a database administrator, chances are this table already exists in your database (they’re a standard at most organizations). In order to scaffold, you need a date dimension (date_dim) table-a table that contains one column, with one record per day and no missing dates. Our restructured data will look more like this. In other words, you need a record for every day that an employee worked at the organization. You’ll need a record to represent employee tenure at the daily level. You will need to restructure this data to understand how each date relates to an event. But this structure isn’t ideal for analysis in Tableau. Each employee gets one record and that one record contains all their information. This data structure makes sense from a database perspective because it's efficient and takes up little space. Two additional columns provide the employee’s hire data and termination date, and the termination date is NULL if the employee still works at the organization. A common way to store data about employee tenure looks like this:Įach employee at the organization is assigned an employee ID. Reference Materials Toggle sub-navigation.Teams and Organizations Toggle sub-navigation.Plans and Pricing Toggle sub-navigation.
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