Pixar 30 Year Anniversary‍

Reverse Engineering Pixar in Power BI: From 100 Rows to a Studio-Grade Analytics Model

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The Live Embedded Report
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Reverse Engineering Pixar in Power BI: From 100 Rows to a Studio-Grade Analytics Model

Most demo datasets in Power BI are flat files. A few columns, a handful of rows, and maybe a chart or two. They prove the tool works, but they don’t prove the point: with the right modeling discipline you can transform a small dataset into an immersive, enterprise-level analytics experience.

This Pixar report takes a thin 100-row film list and engineers it into a complete semantic model. The front end feels like a streaming app, but it’s built on the same principles we use for financial reporting, supply chain optimization, and executive dashboards.

Starting Point: A Single Table

Seed dataset: ~100 Pixar films with title, release date, rating, runtime, and poster URL. Good for a gallery. Not good enough to answer:

• How do budgets compare to worldwide box office?
• Which cast members define the brand across multiple films?
• How do critic scores trend over time?
• What patterns exist between genre, ROI, and award nominations?

Answering these requires enrichment, not just visualization.

Building the Model

Target architecture: hub-and-spoke with PixarFilmsDim at the center.

Spokes

• BoxOfficeDim — budget, domestic, international, worldwide, ROI
• PublicResponseDim — Rotten Tomatoes, IMDb, Metacritic, CinemaScore-style fields
• AcademyDim — nominations and wins
• FilmCast / PeopleDim — names, bios, roles, gender, departments, “known for”
• GenresDim — standard genre mapping
• FilmPictures / CastPictures — posters, logos, backdrops, headshots
• FilmVideos — trailers and clips
• Date — canonical calendar for release and year analysis

Each spoke keys back cleanly to the film. One fact was expanded into a studio-grade model.

Reverse Engineering the Graph

Key move: M functions that reach outward from each film id to pull related data—cast, crew, box office, public response, awards, images, videos—then normalize and clean.

Cast enrichment (excerpt):‍

let
    Source = #"PixarFilmsDim (3)",
    #"Invoked Custom Function" = Table.AddColumn(Source, "castCrewpull", each #"Credits function"([Id])),
    #"Expanded castCrewpull" = Table.ExpandTableColumn(
        #"Invoked Custom Function",
        "castCrewpull",
        {"PersonID","Name","CreditType","Job","Character","Department","KnownFor","Gender","ProfileImage","Birthday","PlaceOfBirth","Biography","Popularity"}
    ),
    #"Replaced Value2" =
        Table.ReplaceValue(#"Expanded castCrewpull",
                           null,
                           "https://cdn0.iconfinder.com/data/icons/web-ui-20/160/no_image-256.png",
                           Replacer.ReplaceValue,
                           {"ProfileImage"})
in
    #"Replaced Value2"

Measures That Matter

Semantic clarity comes from a small set of focused DAX patterns.

Formatted cast display

MAX(FilmCast[Name]) & " (" & MAX(FilmCast[Character - Copy]) & ")"

Gender-aware “Known for …”

VAR _gender   = MAX(FilmCast[Gender])
VAR _knownfor = MAX(FilmCast[KnownFor])
VAR _pronoun =
    SWITCH(TRUE(),
        _gender = "Male",   "His",
        _gender = "Female", "Her",
        "Their"
    )
RETURN "Known for " & _pronoun & " " & _knownfor

‍Box Office ROI

DIVIDE(
    SUM(BoxOfficeDim[box_office_worldwide]) - SUM(BoxOfficeDim[budget]),
    SUM(BoxOfficeDim[budget])
)

‍All business logic is centralized in a Model Measures table to keep maintenance tight and avoid duplication.

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Why This Works

• Context. A budget number alone isn’t meaningful. Budget beside critic scores, awards, and worldwide gross tells a complete story.
• Navigation as narrative. Pages like Cast, Crew, Production, and Visual Effects mirror a studio workflow. No training required.
• Consistency. Every page uses the same grammar: synopsis, poster, ratings badges, box office block. Learn one film page, you can read them all.

Applying This Beyond Pixar

Mechanics map cleanly to business:

• Finance — Divisions or products as “films” with P&L, market feedback, and ROI.
• Operations — Shipments or projects as “productions” with crews, timelines, costs, and outcomes.
• Sales — Accounts as “stories” with contacts, interactions, and revenue arcs.

The Pixar skin is cosmetic. The method—hub-and-spoke modeling, function-driven enrichment, and semantic discipline—is general.

The Lesson

A dataset doesn’t have to be big to be valuable. With reverse engineering, enrichment, and disciplined modeling, a flat 100-row table became an interactive studio system.

This is what Power BI can do when you stop thinking in tables and start thinking in experiences. When data is structured like a story, people don’t just read it—they explore it.

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-Markus