[Show & Tell] New Microsoft Power BI Website based off of Tiller Foundation Template - Report Site Works with Mac or PC or Mobile Phone

[freshman.david]

Turn your Tiller Data into an Enterprise Power BI Dashboard (Automated Personal CFO)

Hey Tiller Community!

Like most of you, I absolutely love Tiller. Having my bank feeds automatically pulled into a spreadsheet changed the game for my personal finances. However, I kept running into the same problem: I was spending hours every month fighting with complex formulas, broken charts, and pivot tables trying to actually visualize that data.

I wanted the kind of interactive, high-level analytics that massive corporations use, without the manual spreadsheet maintenance.

So, I built Freshman Finance (https://freshman-finance.com). A website dedicated to the report.

It is a fully automated, interactive “Personal CFO.” It takes the raw data you are already pulling, routes it through an automated pipeline, and renders it in a mathematically isolated, enterprise-grade Power BI dashboard.

Instead of updating spreadsheets, you can instantly see:

• The Core Foundation: Total Income, Expenses, and actual Net Cash Flow.

• Your Burn Rates: Paced daily spending averages to see exactly how fast cash is leaving your accounts.

• Wealth Building Ratios: Automated Expense Ratios and Savings Rate Percentages.

• Time Intelligence: Instant Year-Over-Year (YoY) variance and Month-Over-Month (MoM) growth comparisons.

Here is a YouTube video that gives a 1 minute overview of the report:

Freshman Finance Power BI Engine

A few architectural highlights from the clip:

• Dynamic Scenario Testing (0:07): Adjusting a monthly expense parameter (like a car payment) and watching the limits instantly recalculate against the threshold without script execution delays.

• Relational Speed & Drillthrough (0:28): Slicing across Years/Quarters/Months in milliseconds. More importantly, drilling through to the atomic transaction level ensures the semantic model never orphans a row from its Category or Account context.

• “Z-Axis” Survival Runways (0:49): Instead of using rigid category folders, this utilizes multidimensional Behavior/Necessity filtering. Using the dropdowns to show “Discretionary” spending across all categories simultaneously allows the dashboard to instantly show you where to cut spending if necessary.

Test Drive the Live Sandbox I spun up a fully functional sample environment so you can click around and see the Power BI engine in action before you connect your own data.

• Desktop Mac or Windows Portal: https://reports.freshman-finance.com

• Username: GuestAccount

• Password: ReportsABC!$!

  

Start analyzing your future! With CFO Metrics! all metrics explained with an info glossary. Also we have a glossary with analysis at the website. Let me know what you guys think of the sandbox!

Best, David Creator, Freshman Finance

[freshman.david]

Just a quick update for the community: After running into the mathematical limits of flat-file forecasting, I’ve completely pivoted the architecture of this project. I rebuilt it as an ‘Executive Tier’ SQL/Power BI engine that derives its information from a Tiller Excel formatted file. It works with either Google Sheets (saved as Excel file) or Excel template. I added a 1-minute video demo to the original post above showing how the VertiPaq engine handles instant scenario testing and Z-axis filtering without script timeouts. Would love to hear from anyone else porting their feeds into a relational database or if you are interested in seeing your data in the dashboard!

[freshman.david]

Freshman Finance Mobile Phone 3 Tiered Drillthrough

I was reading through the thread on handling inactive categories and zero-budget months. This is exactly why I ultimately had to move my own architecture off of a flat-file spreadsheet and onto a relational database (Azure SQL + Power BI).

When you use a flat file, you have to write heavy multi-condition formulas just to hide a category, which eventually creates massive lag. In a relational model, you just flip a simple IsActive = 0 flag in the dimension table. The historical math stays perfect, zero computational load is added, and the category simply drops off the current active slicers.

I actually just recorded a quick screen capture on my iPhone to show what the UX looks like when the data is structured this way natively. It allows for a full three-tier ‘waterfall’ drilldown right from the mobile screen without any caching delays. Hope this helps give some perspective on different ways to tackle the data model!

[freshman.david]

Just as an update. I changed the password to my GuestAccount. I put the updated password in my original post. Again feel free to message me here if you have any questions or comments or want to see how your Tiller Foundation template fits into the database. Thanks, David.

[freshman.david]

Beyond the Budget: Why I Track “Daily Burn Rate” for Discretionary Spending

Most of us start using Tiller to set a monthly budget. But I quickly realized that waiting until the end of the month to see if I hit my ‘Restaurant’ or ‘Entertainment’ goals was like driving by looking in the rearview mirror. I needed an operational metric, not a historical one. That’s why I shifted my focus to calculating a Daily Burn Rate.

Your fixed costs (mortgage, insurance) don’t need daily babysitting. But your discretionary categories—like dining out—are highly volatile. By breaking down those specific categories into a rolling Daily Burn Rate, you get instant feedback. If my target burn rate for restaurants is $15/day, and I drop $60 on a Tuesday dinner, I don’t have to wait until day 30 to realize my budget is busted. The burn rate spikes, and I immediately know I need to throttle back for the next three days to normalize the curve.

The challenge is that standard spreadsheet templates aren’t really built for dynamic, rolling daily averages that isolate specific categories on the fly. You end up having to build incredibly complex SUMIFS and date-math formulas that get heavy and prone to breaking.

Getting this one metric dialed in completely changed how I manage my cash flow. How is everyone else tracking their mid-month pacing? Are you just eyeballing your budget bars, or do you have a way to calculate daily operational burn?

[freshman.david]

Just wanted to show the updated table in Page 2 of the drillable dashboard.

[freshman.david]

Taking “Live Pacing” Mobile

I’ve been watching some of the discussions in the community lately about the struggles of building “Budget-to-Actual” pacing formulas that track against =TODAY() without breaking nested arrays.

I wanted to share a quick screenshot of how I handle this using the relational database architecture I mentioned in my original post. Because the backend SQL engine handles all the Time Intelligence natively, the frontend can be accessed anywhere without UI friction.

This is a screenshot from my phone today (May 17th) looking at my live production data.

The engine plots my cumulative actual spend (the blue step-line) against an auto-calculated daily target pace (the grey area) based on my 30-day rolling YTD expenses.

There are no CHOOSECOLS or manual month updates involved. The exact business value here is that I can pull my phone out while sitting at a restaurant, tap today’s date, and instantly see if my blue line is safely inside the grey target area before I pay the bill.

As a reminder, the public sandbox linked above uses historical static data, so it doesn’t include this specific “live calendar” visual, but this is the exact native mobile experience when the model is connected to a live Tiller feed.

[freshman.david]

Just pushed a UI/UX update to the live sample file to tackle information density.

When you are monitoring your “Daily Burn Rate,” the immediate next question is almost always: “How does this specific pacing affect my overall year-to-date trajectory?” Instead of forcing users to click through to a secondary page or cluttering the main screen with more visuals, I wired up a dynamic report-page tooltip.

If you tap (on mobile) or hover (on desktop) over the Daily Burn Rate, the engine instantly renders your YTD Expenses Burndown chart.

Because the data is sitting in a columnar in-memory model, the tooltip inherits your exact filter context on the fly. It crunches over 4,000 rows of historical ledger data and renders the visual in about half a second.

I’ve attached two screenshots from my phone to show how it handles the dynamic slicing natively on mobile. Both are looking at 2022:

1 First image: The overall YTD running spend across all selected categories.

2 Second image: Sliced specifically to “Groceries” to instantly isolate that specific burndown trajectory.

The data labels render quarterly totals in both images.

The live sample file on the storefront is updated if anyone wants to test the render speed or the dynamic filtering on their own device.

[freshman.david]

There is a sample dashboard too on mobile that I have been mentioning. You can get instructions on how to access the sample mobile report Mobile report setup instructions. It does require downloading from your mobile App Store - Microsoft Power BI. Full instructions at that link. Thanks, David

Edited: to fix link. It’s correct now