This user ran into a problem that every services company eventually faces: the gap between what you've billed and what you expect to bill, and the need to see both numbers in the same view, organized by a fiscal year that changes from client to client. That is a real, practical headache, and the solution is not to build a better pivot table. The solution is to stop treating your spreadsheet like a database and start treating it like a data model that can think.
Let's be clear: the pivot table approach failed here because the user split their data into two separate tables, one for projected billings with expected invoice dates, and another for invoiced billings with paid dates. That is a sensible way to organize raw data, but it creates a problem when you want to summarize both streams together. The data model in Excel can link those tables, but a pivot table still struggles to blend values from two different date columns into a single fiscal-year grouping. The user tried to solve this by creating a joined table in Power Query, and that is exactly the right instinct, but it is also the point where most people give up and ask for help.
Our take is that this user is one step away from a much cleaner workflow, and that step is not a complex database join. It is a single formula that calculates the fiscal year for every row in both tables, then uses a GROUPBY or a simple SUMIFS across the unified dataset. The user already built that formula for their original milestones table. They just need to apply it to both the projected and invoiced tables, then stack the results into one array. The data model is a useful tool for relationships, but for this kind of blended summary, a well-designed flat table with a fiscal-year column is faster, more transparent, and easier to audit.
What this means for you, if you are managing budgets across multiple fiscal years, is that you do not need to become a Power Query expert to get the answer you need. You need to think about your data in terms of what you want to see, not how you want to store it. The projected and invoiced amounts are both just numbers associated with a date. If you assign a fiscal year to every date at the row level, you can group and sum them with a single formula, regardless of which table they came from. The user's original GROUPBY array worked because the data was in one place. The move to separate tables added complexity without adding insight. The fix is to bring the fiscal-year logic to the data, not the other way around.