It All Begins With Data

Tue 28 October 2025 — data-driven, production-pipeline, cgi, vfx, animation, game-development, asset-management, pipeline-automation

It all begins with data. In modern CG production, this simple truth can make or break your pipeline efficiency and team sanity.

Why Data-Driven Pipelines Are the Foundation of Good Production

In any CG production pipeline, nothing happens in isolation. Nearly every asset is the result of tight collaboration between many artists across multiple departments. In a team environment, consistency and adherence to required standards and specifications are essential for delivering the highest quality content at the lowest possible cost.

That's why before concepting, modeling, texturing, rigging, or rendering begins, the data for an asset should be clearly defined, structured, stored, and ready to drive other tools and steps for the entire process.

This isn't bureaucracy or needless red tape. It's a core component of pipelines with strong clarity and scalability. Which helps create an environment where artists focus on art, not pipeline adherence.

What "Data-Driven" Really Means

A data-driven pipeline flips the traditional workflow where artists create files somewhere on disk as needed, hoping that they followed the project's standards. A data-driven pipeline instead begins by defining the object's data.

Let's consider a character for a fantasy game. What are the data fields that would help define a character? This table defines values that come to mind:

This data can be stored in a database, CSV, XML, JSON, YAML, or similar format. Then an API can provide easy access to the data, which can be used to drive file paths, naming conventions, URLs, asset retrieval, and validation, etc. The benefits of being data-drive can be felt at every stage of production. Modern pipeline management tools like Shotgrid and ftrack provide elegant solutions (at a price) for this kind of structured data management.

The result? No guesswork, just scalable clarity.

The Harsh Truth About Standards

Standards without enforcement will fail and enforcement without tooling will frustrate artists. Expecting artists or developers to remember endless naming conventions, folder structures, or storage rules is unrealistic, disruptive, and unfair.

Investing on infrastructure to be data-driven, including tools that leverage such data, create the basis of a production pipeline which can scale, FAST. Data can scale fast without introducing chaos.

You see, in production, especially under tight deadlines, it's easy to introduce data chaos. I'll admit—I'm guilty of this myself! Even when I've helped define the standards, I've still made mistakes.

A data-driven system removes this burden. It doesn't depend on human memory; it enforces structure automatically by ensuring that data is correct before being shared with the rest of the team.

The Hidden Cost of Data Chaos

Data chaos is one of the most insidious hidden expenses in most production:

• Artists waste precious time hunting for the right file instead of creating.
• Wrong files slip into the pipeline, triggering costly rework across departments.
• Assets that fail to adhere to standards will break or be ignored by automation tools, triggering more manual work.
• Out-of-date versions get used, introducing errors into shots or builds.
• Duplicate files accumulate, cluttering storage and causing confusion over what's "final."

A few minutes lost here, an hour there—across a large team and a long schedule—can add up to weeks of wasted effort. The real danger isn't just lost money—it's lost time. Time lost to confusion leads to missed deadlines, delayed releases, and unnecessary stress/frustration.

The Pipeline Automation Payoff

When productions invest in a data-driven pipeline, they are taking the first step toward a highly efficient production environment that allows teams to create large volumes of content quickly.

When tools and frameworks leverage this data, they dramatically reduce onboarding and iteration time. Expanding the team becomes easier, and productivity stays high without introducing chaos.

With a data-driven pipeline it becomes possible to implement:

• A context framework which can generate working and publishing directories, provide file I/O, associate data and task validators, and a publisher.

• An asset generation tool which creates temporary files with the correct name and path. Artists just need to update the files with real data as the asset evolves.

• A reporting tool which can traverse the production data and find assets which are not following standards.

Getting Started with Your Asset Management Workflow

Building a data-driven pipeline doesn't have to be overwhelming. Start small:

1. Define your core data schema - What information do you absolutely need for each asset?
2. Choose your storage format - CSV, JSON and YAML are great starting points for smaller teams
3. Build simple validation tools - Even basic Python scripts can catch naming convention errors
4. Automate incrementally - Don't try to automate everything at once

The key is consistency and gradual improvement. Your future self (and your teammates) will thank you.

Summary

Data-driven pipelines aren't just a nice-to-have—they're essential for any production that wants to scale without losing its mind. By defining asset data upfront and building tools that enforce standards automatically, you remove the guesswork and frustration that plague traditional file-based workflows.

The investment in setting up proper data structures and pipeline automation pays dividends in reduced errors, faster iteration times, and happier artists who can focus on what they do best: creating amazing content.

Start with your data schema, build simple tools, and watch your production efficiency transform. Trust me, after 25+ years in this industry, I've seen teams go from chaos to clarity—and it always starts with getting the data right.

Got questions about implementing data-driven workflows in your pipeline? Drop me a line, let's stay in touch. I love talking shop.

Next Steps

I will be diving into more details on how I implemented the data access layer for Banzai, a proprietary production enviornment tool built on top of Rez, BleedingRez and Allzpark

Rudy Cortes

Technical Art Lead / Author

With 25+ years in CG production for animation, VFX, and games, Rudy has worked at industry leaders including Amazon Games, Blizzard Entertainment, Walt Disney Animation Studios, and Pearl Studio. He specializes in building scalable pipelines and tools that help artists focus on creativity rather than technical overhead.

Current: Technical Art Lead at Amazon Games (2020-Present)

Previous: Blizzard Entertainment, Walt Disney Animation, Pearl Studio

Expertise: Pipeline Development, Technical Direction, 3D Rendering, Asset Management