Forecast using a Blank Template
Last updated
Last updated
A common requirement for organizations is to forecast using a blank template in Power BI, just as we would do with Microsoft Excel. Data may be entered by a single individual, or by many individuals across the organization.
Let us explore how we can set this up quickly in Power BI using Inforiver.
In our case, ACME Inc. wants to create sales forecasts for all four quarters of the year 2024 for each geography. These are the steps involved.
Launch Inforiver Matrix visual.
Assign Region & Sub Region categories to rows and Quarters to columns. You will have a blank table structure.
We will now create a data input field (a forecast measure) so that users can type in their values. To do this, click on Insert -> Data Input -> Number (since it is a numeric field type) -> Insert a new empty series.
Name the column 2024 Forecast and click on Create.
You will get a blank template with four columns. You can also add additional fields of other types (text, multi-select, date, etc.) using the same Data Input menu shown earlier.
Let us go ahead & add these fields:
a 'Person' column for inputting users' names from the organization. The list of names comes from the Office365 directory.
a 'Status' text drop-down field, and
a 'Remark' column for entering multi-line comments.
Let us start entering our forecasts now. Double-click the value for Q1 for the APAC region. A formula bar appears above the table. Type in ‘10m’ in the formula bar.
Press Enter, and the APAC values automatically roll up to the International region and All (company-level total). This makes hierarchical budgeting easier without having to write formulas at each parent level.
Similarly, you can go ahead by filling out values for the other granular regions. Assign the people & status fields to each row.
Just like we entered the values, other users in the organization also can enter the values for their respective regions.
This is how a sales forecast can be done using Inforiver with a blank template in Power BI. In the next section, we'll learn to forecast using the existing data.