π§ Intro: Data, Music, and Meaning
What I listened to while building this system:
Stepping into RVNG Intl. never felt like walking into a tech office.
It felt like stepping backstage at an art performance β cables everywhere, ideas everywhere, humanity everywhere.
RVNG is a label that exists between intuition and experimentation.
And I arrived there as someone who lives between two worlds myself:
- music, which has always been how I understand emotions
- data, which became the way I understand systems
This internship wasnβt just about engineering pipelines.
It was about learning how numbers sing.
π A Different Kind of Data Problem
Most labels rely on dashboards, KPIs, and traditional industry tools.
But RVNG is not βmost labels.β
They had years of history β
Shopify orders, Bandcamp releases, Secretly Distribution feeds, Luminate streams β
but everything existed as fragments.
It wasnβt just a technical challenge.
It was a narrative challenge:
How do you help a creative organization βhear itselfβ through its data?
My mission became clear:
to build a system that respects the art while revealing the story behind it.
π What I Built, and What It Meant
βοΈ I built pipelines. I built dashboards. I built databases.
But underneath those systems, I was actually building something more subtle:
- Clarity out of chaos
Turning scattered CSVs into structured intelligence.
- Confidence for decision-making
So the label could move from intuition β insight β strategy.
- A living map of RVNGβs catalog
One that captures not just revenue, but momentum, discovery, and longevity.
π I designed an ingestion web app
A control center for every revenue source β digital, physical, international β
so RVNG could finally press one button and watch the system run itself.
πΌ I built the Luminate analytics layer
A unified ISRC β track β album β artist graph
that finally let the label see the full shape of its catalog in streaming ecosystems.
π I engineered signal-processing metrics
ΞΌ, Ο, RSI, volatility, momentum, NPV, winsorization β
but not as abstract math.
As ways to ask:
βHow is this song doing? Is it growing? Has it reached new people?β
Β
𧩠What I Learned
The deeper I went, the more I realized that:
Data in the music industry isnβt just a measurement β itβs a memory. A trace of who listened, where, and why.
I learned how to build systems that are:
- technical enough for analysis
- stable enough for the future
- human enough for an art-driven label
RVNG taught me that analytics can be creative
and that engineering can support culture β not replace it.
This wasnβt just an internship.
It was a collaboration between art and data,
and I was lucky to stand at the intersection.