13 Distributors, 5 File Formats, Zero Standards -The Reality of Music Royalty Data

Every month, an independent label receives royalty reports from over a dozen distributors. Not a single one looks the same.

This isn't a hypothetical. This is what a real download folder looks like when you work with music royalty data at scale.

The wall of files

Here's a small sample of actual filenames from a single label's monthly data intake -anonymised, but otherwise untouched:

That's 18 adapters across 500+ files per year -each with its own naming convention, file format, and internal structure. From a 6 KB Bandcamp CSV to a single Orchard report that can reach 700 MB.

Spot the pattern

Go ahead, try. You won't find one.

And that's just the filenames. Open these files and you'll find different column names for the same data, different date formats inside the cells, different encodings, and multi-sheet workbooks where each sheet follows its own rules.

Why this matters

Someone has to make sense of all this. Every month.

For most independent labels, that means hours of manual work -copying data between spreadsheets, reformatting dates, matching column names, fixing encoding issues that turn artist names into garbled text.

The cost isn't just time. It's delayed royalty payments to artists. It's reporting errors that erode trust. It's the finance team spending their week on data cleanup instead of analysis.

How teams try to solve this

There's more than one way to tackle this problem. Here's how the most common approaches compare:

Generic ETL tools work well for standardised APIs and databases. But music royalty data doesn't come from APIs - it comes from email attachments, FTP servers, and distributor portals. Each source is its own special case. That's why an adapter-based approach wins here: each distributor gets its own parser, isolated from the rest, easy to update when formats change.

One clean dataset

Here's what the pipeline looks like in practice:

Every file goes through its format-specific adapter - handling encoding, column mapping, date parsing, and multi-sheet logic. What comes out the other side is one consistent dataset: same columns, same date format, same encoding. Ready for analysis, reporting, and artist payouts.

That's what we build at MusicTech Lab. Not another dashboard on top of messy data - but the data layer underneath that turns chaos into clarity.

Looks familiar?

If your monthly royalty workflow involves more spreadsheet wrangling than actual analysis, we should talk. We've built data pipelines for independent labels handling exactly this kind of complexity - and we can do the same for you.