1. Budgeting with Power Pivot
Alberto Ferrari – SQLBI

2. Alberto Ferrari alberto.ferrari@sqlbi.com Founder of www.sqlbi.com
Book Writer, Trainer, BI Pro
Microsoft Business Intelligence Partner
SSAS Maestro, MVP, MCP...
In a word: fond of BI

3. Contoso prepares the budget
End of 2009, prepare the budget for 2010
Each country send reports
Some report a large number
Some slice forecasts by product category
Someone else slices by brand
Each country provides data for different scenarios

4. Steps to prepare the budget
Adjust data granularity
Allocate different datasets to obtain same granularity
Country, Brand, Category, Month
Using previous year allocation
Power Query is our best friend here
Define the data model
Some relationships are not-so-easy to build
Build the DAX formulas
Compute forecasts
Handle below-the-granularity values

5. Takeaways Use DAX to query your data model
Use Power Query to massage the data
Using the UI
Writing some M code by hand
Understand logical relationships vs physical ones
Adjust granularity at query time with measures

6. Build Allocation Tables
Forecast by Country
Allocate by Brand and Category
Forecast by Country and Category
Allocate by Brand
Forecast by Country and Brand
Allocate by Category
All of them need to be allocated at month level
Data is in Power Pivot
Power Query cannot query the data model
Use DAX to extract summary tables
Use Power Query to perform the merge 1

7. 2 Allocate Values Power Query UI simple to use Coding by hand
Leads to poor code
Coding by hand
Much better option
Mixed approach
Start with the UI
Clean your code with an editor 2

8. 3 Build relationships Data is at Country, Brand, Category, Month
Category is simple
Month requires a bit more work
Calculated column?
Simple, not the best option
Country and Brand much more challenging
Relationship at a different granularity
Build a lookup table
Use DAX to build a logical relationship
CONTAINS is your best friend 3

9. Fixing Granularity Issues
Need to detect when the budget is no longer valid
Below that, the granularity it is wrong
You can BLANK its value
Or dynamically reallocate the values
Dynamic allocation
Very powerful for budget analysis
Simple algorithm: weighted allocation based on 2009
You can author much more complex code
Physical relationship with the date is an issue
It was easy, but it was wrong
Relationship need to be set at the correct granularity 4

10. Conclusions Budget is always a challenging experience
Different tools complement each other
Power Query and the M language
DAX as a query language
DAX in the data model to build measures
Understanding and mastering granularity is the key
Modify the data model fixing granularity
Use logical relationships (a bit slower, but much more flexible)
DAX is your best friend
DAX is simple, but it is not easy
Yet, it is extremely powerful

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