Batch ISRC Enrichment That Turns Messy Catalogs Into Clean Data

If you have ever opened a 40,000-row CSV and found half the ISRC codes missing, inconsistent, or flat-out wrong, you know the pain. Manually looking up each track via Spotify, MusicBrainz, or ISRC lookup tools takes days. We built Scout to do it in minutes.

The Problem Every Catalog Manager Knows

Track data arrives as CSV or Excel files. They contain track names, artist names, and sometimes ISRCs. The problem is that "sometimes" is doing a lot of heavy lifting in that sentence.

This is not a niche issue. Anyone doing soundtrack acquisition, catalog evaluation, royalty reconciliation, or building investor decks hits this wall. The data exists across multiple sources, but nobody connects it at scale.

A Real-World Example: Santana's "Smooth"

To show why this matters, take a concrete case. Santana's "Smooth" appears in a royalty report with ISRC USAT29900471. When Scout looks up the same track on Spotify, it comes back as USAR19900033.

Both are valid ISRCs for the same recording. The first is from the original 1999 Arista release, the second from a later digital distribution. This happens constantly in the music industry because every release edition (original, remaster, compilation, deluxe, single) can get its own ISRC.

Without automated enrichment, you would either miss this mismatch entirely or spend hours tracking it down manually. Multiply that by 40,000 rows and you understand why catalog managers describe this work as "soul-crushing."

What Scout Does

Scout is a feature inside MusicData Lab that batch-enriches track metadata from two authoritative sources: the Spotify API and the MusicBrainz API.

Upload and Map

Drop a CSV or Excel file. Scout auto-detects common column names (track, artist, ISRC, URL) and lets you adjust the mapping before processing.

Enrich Every Track

For each row, Scout runs a four-stage lookup pipeline:

Confidence Scoring

Each track gets a confidence score from 0.00 to 1.00:

A track matched by both Spotify and MusicBrainz with a resolved ISRC scores a perfect 1.00. Flags are informational only. They tell you something needs attention without penalizing the match quality.

Export

When processing completes, download a clean CSV with all original data plus every enriched field: Spotify metadata, MusicBrainz metadata, resolved ISRCs, status, flags, and confidence scores. Ready for analysis, investor decks, or import into your rights management system.

Processing at Scale

Scout processes files asynchronously using Celery workers. A 40,000-row file runs in the background while you continue working. The job detail page shows real-time progress without page refreshes:

The Flags That Matter

Scout raises four types of flags:

• isrc_mismatch - the input ISRC, Spotify ISRC, and MusicBrainz ISRC do not all agree. Most common flag. Usually means different release editions.
• artist_mismatch - the artist name from Spotify and MusicBrainz has a fuzzy match score below 75%. Can indicate featuring artists, name variations, or genuine data issues.
• duplicate_isrc - multiple rows in your file resolve to the same ISRC. Important for catching double-counted tracks.
• enrichment_error - the lookup failed for technical reasons (API timeout, rate limit, etc.).

From Script to Product

The inspiration for Scout came from a real conversation with a music professional doing soundtrack acquisition work. They had built a Python script that batch-processed metadata from Spotify and MusicBrainz APIs using pandas. No AI needed, just API calls and data wrangling.

We took that exact workflow and productized it inside MusicData Lab:

Who This Is For

Scout is built for anyone who deals with music metadata at scale:

• Rights managers reconciling royalty statements across distributors
• A&R teams evaluating catalogs for acquisition
• Independent labels cleaning up their metadata before pitching to sync agents
• Royalty analysts spotting unclaimed revenue from ISRC gaps
• Catalog managers preparing clean data for investor decks or audits

If you are spending days in spreadsheets manually looking up ISRCs, Scout does that work for you and flags the problems worth your attention.