Audio Streaming + Podcasting
Use Case: 

To deliver complex financial reports, Spotify turns to Flyte™

The company

Spotify is a digital music, podcast and video service that serves consumers millions of songs and other content from creators all over the world. With nearly 200 million subscribers in 180 markets, Spotify’s Streaming, Automation and Tooling, Data and Insights, Business Modeling and Forecasting, and Data Workflow Scheduling teams work continuously to support the business’ growth.

The challenge

To meet regulatory compliance objectives, Spotify’s finance team must generate quarterly reports containing two-year P&L projections that include model-based revenue projections for emerging markets.

Dylan Wilder, an engineering manager on Spotify’s Vivaldi finance team, said that end-to-end forecasting typically took three or four weeks every quarter because of the many departments and models involved. Eight teams from Spotify all contribute their respective workflows to produce those forecasts, which incorporate historical trends and key new business initiatives. The exercise involves as many as 25 people across those eight teams, running more than 15 models on various areas of Spotify’s business — such as premium subscribers, streams, ad inventory, music ad revenue, and royalties.

“The teams usually span different business verticals; P&L line items (such as revenue vs. royalties); and specific markets. Specific models belong to specific teams, and those teams are either responsible for specific verticals (like podcast) or horizontal needs (like royalties).”

The solution

To align processes, eliminate errors and speed results, Spotify’s finance team realized it needed a cohesive platform that could coordinate pipelines built with many technologies. After comparing leading orchestrators on the market, Spotify chose Flyte as the main runtime engine of their model.

Using Flyte as its model’s main runtime engine, Spotify was able to build a single forecast platform, called One Model, to crunch the data in real time. One Model unifies the disparate workflows — from teams including Automation and Tooling, Data and Insights, Business Modeling and Forecasting, and Data Workflow Scheduling — into Spotify’s financial forecast ecosystem.

Wilder praised Flyte’s ability to engage data scientists directly in the refinement and exchange of models. “As engineers, a lot of this might be table stakes for us, but for data scientists, being able to get up and running on Flyte,” he said. “Getting all of this stuff for free is a really big win for them.”

The result

With Flyte, Spotify reduced time-to-forecast, increased the number of runs possible in a quarter and minimized the number of human errors.

What’s more, Spotify can run more business cases and scenario analyses more often to give the leadership team a fast, accurate view of its best course. “The number of forecasts run went from being limited to once per quarter involving all teams to thousands [of forecasts] for analytics purposes. We still have some work to do on reducing errors related to user configuration, but we've eliminated those related to manual handoffs.”

“The overall quarterly process has gone from four weeks to less than two.”