Data accuracy in AI animation
When you’re animating numbers—KPIs, growth rates, conversion metrics—credibility is the product. A beautiful animation that’s wrong is worse than a static chart.
Common failure modes
- Hallucinated values: a model invents data points to complete a story.
- Unit mismatch: percent vs. absolute, monthly vs. quarterly.
- Stale inputs: the dataset changed but the animation didn’t.
What “data-accurate animation” should mean
- Every value is derived from a data source (CSV, sheet, DB export).
- Bindings are validated (missing columns/rows produce clear errors).
- Style changes don’t affect numbers (color/layout changes are separate from data).
- Regeneration is reproducible (same dataset + same bindings yields the same chart).
Why this matters for teams
If your work is going into reports, decks, or client deliverables, accuracy and consistency matter more than novelty.
movium is designed with this mindset: trust first, polish second, speed third.