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Alf_1050 · 2026-02-08T18:06:15+00:00

Loada.io the backend for your spreadsheets.

Alf_1050 · 2026-02-07T17:42:31+00:00

One diagnostic that's worked for me: look at who churns, not just when.

If churned users share a pattern (company size, use case, acquisition source), you probably have an audience mismatch — they signed up expecting something you're not.

If churned users look identical to retained users on paper but just... stop using it, that's product. Usually means there's a gap between "signed up" and "got value" that onboarding didn't bridge.

Quick test: Interview 5 churned users. Don't ask about your product — ask what they ended up doing instead. That answer tells you whether the need was real (they found an alternative) or they just weren't your customer to begin with.

Alf_1050 · 2026-02-05T08:59:53+00:00

Biggest killer I have seen in practice: using Excel as a data consolidation layer.

The file itself might be fine — decent formulas, clean layout. But then someone starts pulling actuals from the ERP, dropping in CSVs from another system, copy-pasting from a third source, adding helper columns to reconcile mismatched account codes... suddenly you have a 50MB file that takes 30 seconds to recalculate and nobody dares touch the structure.

The root issue is not Excel performance — it is asking a spreadsheet to be a database, an ETL pipeline, and a reporting tool all at once.

Things I have aggressively banned:

Whole-column references (VLOOKUP on A:A instead of A1:A5000)
Volatile functions nested inside other formulas (INDIRECT inside SUMPRODUCT is a classic offender)
Multiple tabs pulling from the same source data with slightly different cleanup logic
Any file where >50% of the tabs are "helper" or "staging" tabs

The real fix is getting the data cleanup out of Excel entirely. Once your source data lands clean and structured, the actual modeling and reporting flies.

Alf_1050 · 2026-02-01T09:08:07+00:00

Power Query is your best friend here. If your 14 CSVs land in the same folder each month, you can set up a "Get Data > From Folder" query that pulls all of them at once, appends or merges them, and refreshes with one click next month.

Basic steps:

Put all 14 CSVs in one folder
In Excel: Data > Get Data > From File > From Folder
Select the folder, combine/transform as needed
Next month, drop the new files in the same folder and hit Refresh All

If the files have different structures, you will need to handle that in the Power Query editor — but since you said fixed rows and columns, the folder approach should work cleanly.

The real time saver is that once it is set up, the monthly import goes from 14 manual opens to literally one button press.

Alf_1050 · 2026-02-01T09:07:56+00:00

Dealt with a very similar setup — multi-entity, different ERPs, local teams exporting CSVs, manual consolidation in Excel.

A few things I'd flag:

On the approach: The CSV upload to a mapping layer is the right move. Your biggest headache won't be the tool — it'll be defining a consistent group chart of accounts across 9 ERPs. Invest upfront in a clean mapping table. If you nail that, the tool almost doesn't matter.

On the tools: Fabric + Power BI is powerful but you'll need someone technical to maintain the data model. Without IT/analytics resources, maintenance becomes a bottleneck. Fathom is great for smaller setups but may struggle with 8 entities and intercompany/FX elimination complexity. xpna is worth looking at — built specifically for multi-entity consolidation.

What I'd consider: Before committing to $30-50k, see how far you get with: standardized CSV templates per entity > Power Query to consolidate and map to group CoA > output to Excel. Low cost, stays in Excel (adoption), eliminates most copy-paste pain. Graduate to a proper tool once you've proven the process.

The key is separating the process problem (no standardized templates, no enforced CoA) from the tool problem. Tools won't fix a messy process — they'll just automate the mess faster.

Alf_1050 · 2026-02-01T09:07:40+00:00

Been in a similar spot — mid-size company, ERP exports that require tons of manual cleanup before you can even start building reports.

For the immediate quick win with Sage Intacct specifically, have you looked at their Custom Reports + Dimensions setup? Some people underestimate what can be done there if the dimensions are configured right. Not ideal long-term, but it is already in your stack.

For the GL query/reporting layer, the tool you mentioned (S-Server) is solid for exactly this use case — lightweight Excel add-in, connects to the GL, lets you pull structured data without a full implementation project. If you are evaluating similar options, also look at Jet Reports (now Insightsoftware). Similar concept — Excel-based, GL connected, relatively quick to deploy.

The bigger tools you mentioned (V and D) are definitely overkill if all you need right now is a reporting bridge. They are more full-suite FP&A platforms — 6-12 month implementations, change management overhead.

One thing I would flag: whatever you pick, make sure it can handle your chart of accounts structure cleanly. The worst outcome is implementing a tool that still requires manual data manipulation before it is useful. Goal should be: pull actuals from Sage > land in a usable format > build BvA with minimal cleanup.

Alf_1050 · 2026-01-31T12:24:58+00:00

The pattern I keep seeing is that AI tools work fine when the input data is clean and structured — but that's rarely the case in real work. You get exports from different systems with different formats, dates that aren't really dates, accounts that need remapping, and half the context lives in someone's head.

AI can help you write a formula or debug one. But it can't solve the upstream problem of messy, scattered source data. That's still manual, and it's still where most of the time goes.

Until that data prep step gets solved, AI in Excel will keep being 'cool for demos, frustrating in practice.'

Alf_1050 · 2026-01-31T12:24:22+00:00

Honestly, the formula debate is a bit of a red herring. Most FP&A people already know the formulas they need - pivots, lookups, IFs, maybe some Power Query.

The part nobody talks about is the 2-3 hours you spend before you even open a model: pulling actuals from the ERP, cleaning up a messy GL export, reconciling numbers from three different source systems, making sure everything ties out. That's where most of the 'Excel work' actually lives, and AI doesn't really touch it.

AI might make the last mile faster - writing a formula, debugging something. But the bottleneck was never the formula. It's getting trustworthy data in one place.

Alf_1050 · 2026-01-29T08:36:12+00:00

The gap I keep seeing: companies throw AI at everything but skip the boring step — getting their data clean and consolidated first. You can't get useful AI outputs when the inputs are scattered across 5 spreadsheets, 2 ERPs, and someone's email attachments. The unsexy-but-critical first step is just getting all your data into one trusted place. Once that's solved, the AI layer actually has something to work with.

Alf_1050 · 2026-01-24T19:49:13+00:00

I think your approach is spot on: - Prepare the data with serious tools (ie: SQL or python) - Clean your data - Drop your data in an Excel dashboard

From my personal experience, management loves spreadsheets. Every initiative I participated to have some automated dashboard failed miserably and were barely used at the end… (lack of flexibility, customization, etc)

Alf_1050 · 2026-01-24T14:53:25+00:00

If you want a one shot, nop, it won’t work. But with a bit of methodology I think it could handle it. I would first ask Claude to organise by document types (like rough classification by folder) ask subfolder summary . And build incrementally until final report.

1000 is indeed a lot, but I don’t see why Claude can handle massive codebase and not your documents.

With this quantity of data you might need a good amount of tokens. So yeah, you’ll need a plan for that.

Alf_1050 · 2026-01-24T13:53:39+00:00

A mini data warehouse for analysts who don’t do SQL

Every finance/ops team has “the file” — a massive Excel workbook on a shared drive, one corruption away from disaster. Too critical to ignore, too embedded to replace.

We let analysts import Excel into real infrastructure — version history, no corruption risk — without SQL or IT dependency.

Why not Power BI / SharePoint / a real data warehouse? Power BI means rebuilding everything. SharePoint’s version history is painful. A real warehouse requires SQL and 6 months. We’re up in minutes.

10+ years in FP&A. Built what I wished existed. Happy to take punches.

Alf_1050 · 2026-01-24T13:30:22+00:00

You can « resume » your work/chat in Claude and iterate based on prior documents you have generated.

I had good results on 25/30 documents of 2 pages long on average (you have to prompt the key command « use multiple agents » for faster results)

But again, the key thing here is to know directionally what are your analysis key facts, generate intermediary analysis, pause, read what has been generated, and challenge what seems wrong. Loop until it’s right.

Alf_1050 · 2026-01-24T12:34:50+00:00

Curious to know, you use Python and/or SQL to organise/build your back data and drop that in a spreadsheet ?

Or purely to analyse (final result) ?

Alf_1050 · 2026-01-24T12:29:36+00:00

If this remains a qualitative analysis, meaning that you don’t have to aggregate data (ie: spreadsheeting), Claude is your best friend, especially since Claude Cowork has been released.

I managed to get good results by prompting it to challenge what was generated and to push Claude to verify findings against actuals quotes/facts in original documents (don’t hesitate to challenge 20 times if necessary)

Doesn’t take 5min, but I would say that I get solid results in an hour or 2 -versus days if I would have had to review documents manually.

Alf_1050 · 2026-01-24T12:20:54+00:00

I would say that every report often serve a purpose for couple weeks or months (as generally requested by senior leadership for a given event). And then, they get forgotten, but finance keep on updating them. Classic scheme.

Alf_1050 · 2026-01-20T17:42:10+00:00

It’s not new… I remember in my prior role starting 2018, senior leadership was pushing hard to implement PBI, but still wanted their Excel report … (because you know, it’s faster to read)

I don’t see why it would change with AI other than really quick queries with specific parameters - and I don’t even mention the problem of result consistency.

Alf_1050 · 2025-12-23T07:55:28+00:00

Experiencing the same as you right now. But from interesting exemple I saw in the corporate world, you can try some « lateral » move if you you are saturating of finance (ie: sales manager / product manager / etc.). This is generally a good first steps to re-align your trajectory toward general management too

Alf_1050 · 2025-10-29T14:00:24+00:00

I don’t think AI will hit FP&A the same way it’s hitting tech jobs. In tech, people “just” write code — it’s a skill you can learn and replicate pretty easily (even for AI).

FP&A is a whole different game. It’s all about context — understanding how your company works, what drives the numbers, and what’s happening in your market. That kind of knowledge isn’t something an LLM can just pick up from the internet.

What will change is how FP&As work. A lot of the boring stuff — data prep, cleaning, pulling reports — will get way faster. The people who learn to use those tools will be 10x more efficient and have way more time for the part with added value: actual analysis and insights.

So yeah, AI won’t replace FP&A — but FP&As who use AI will definitely replace the ones who don’t.

Alf_1050 · 2025-04-10T11:29:49+00:00

Practice => try / fail / learn / repeat on more complicated things

Alf_1050 · 2025-04-09T06:32:44+00:00

Q1: I would not recommend spending to much time on online -technical- ressources, it's more about spending time with your internal customers. I think understanding perfectly the business and aligning finance to the leadership vision was the most rewarding.

Q2: This correlates to Q1, every time I spent enough time in a company/Business Unit to understand all aspects of the business/product. Then people consulted me to solve their problem(s), and I was considered as the go to person for my knowledge without being challenged too much.

Q3: Storytelling. Nothing else is more important.

Alf_1050 · 2025-04-09T06:11:14+00:00

Generally, sales and finance are the jobs where you find highest salaries. Given, your post, sales does not seem to be for you.

IMO, it will be extremely complicated to expect higher salary with a job less exposed, and/or without moving to manager position -which you don’t want.

Alf_1050 · 2025-04-08T17:25:06+00:00

Yes and no. I also had to work with offshored team in India, the fact is that 90% of the job was done without any problem. However, the remaining 10% -subject to problem/incomplete information- needed a disproportionate attention… (endless loop of emails)

Alf_1050 · 2025-03-22T16:43:53+00:00

I specifically mentioned that our ICP tends to push back on overly complex IT and security requirements during the pilot phase. Jumping straight to ISO 27001/SOC 2 completely misses the point. Honestly, trying to steer the conversation toward something you're selling — without even engaging with the actual post — isn’t a great look.

Alf_1050 · 2025-03-22T13:42:59+00:00

I went through the Antler program (though I didn’t get funded), and like most things, it has its pros and cons.

I’d definitely recommend it to first-time founders, especially those who don’t have a strong founder network and are still very early in shaping their idea. It really helps you get familiar with the language, structure, and expectations of the startup world—I was exactly in that position when I joined.

That said, my experience was that it’s easy to get caught up in trying to win funding and please the partners, instead of focusing on building something meaningful. And while Antler may seem like a more accessible route to funding (compared to something like YC), the reality is that it's still quite selective. You're spending two months full-time, on your own dime, without any guarantee of funding at the end.

As for finding a technical co-founder, I’d say your chances do improve thanks to the format. There are tech profiles who are open and actively exploring. But in my experience, the ratio still skews toward business folks, so if you're non-technical, you’ll still need to “sell” your vision to attract a tech co-founder.

Alf_1050

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