Linking variables to data items
You can think of Data Items as the items within a Data Source.
In the example below, the 'Revenue/Cost' tab of the 'Causal Template' Google Sheet is the Data Source, and New Prospects is one example of a Data Item.
To connect an existing variable to a Data Item:
- 1.Hover over the variable name -> click on the data icon
- 2.Search for your Data Item/s, and tick all of the data items you wish to connect to this variable.
The Linked variables column in the Data Tables section shows which variables are linked to each data item (in the current model), and lets you link to existing (or new) variables.
On a variable level, you can click the data icon to see which Data Items are being used at a glance.
You can also go into the Data Tables section, and view this on a Data Item basis, with the "Linked variables" column.
- Each variable is automatically named according to the line items, and is automatically connected to the line item in the data source.
- Use this to quickly generate a skeleton for a model, e.g. a P&L model based on a P&L in Xero/QuickBooks.
If your data item is broken down by a category you can transform the Data Item by aggregating the category items (if you don't care to see the breakdown), filtering category items (if you just want to pull in particular items), or grouping by another category.
For example, if your Operating Expenses line item from your accounting system is broken down by Region, then you can
- Filter it to only pull in Operating Expenses for a single region
- Group it by a different category, e.g. Country (note: this category link must already exist in your Causal model)
- Aggregate it to pull in the total Operating Expenses as a single line
The Data Aggregation function on a variable connected to data determines how Causal aggregates transactions within each data item linked to a variable. This is usually always going to be Sum (which is our default), but there are examples where you might want it to be Average, Median, Initial, Final, or Count instead.
For example: If you have daily cumulative data in your data source, and are pulling that into a weekly or monthly model in Causal, Sum wouldn't make sense (as that would be adding multiple cumulative numbers together), so you might choose Final instead.
- e.g. 2022-12-20 Cumulative User Count 33, 2022-12-21 Cumulative User Count 35. Sum would return 68 whereas Final would return 35 (the true ending users for the week/month).
When there's multiple data items linked to a variable, Causal always aggregates over them by Sum. If you'd like the averaged values across multiple data items, you can obtain these by using the Count data aggregation.
- e.g. Employee Voluntary Churn Growth Rates are being pulled in from two different data items, and you'd like the average rate. You'd create 3 variables in total:
Total Churn Rates(data aggregation by sum)
Churn Rate Transactions Count(data aggregation by count)
Average Churn Rate = Total Churn Rates/Churn Rate Transactions Count
Data aggregation settings in a variable's right-click menu