Has Our Industrial Scientific Community Been Acting a Little Goofy When It Comes to Scientific Data, Information, and Knowledge?

By John F. Conway

Chief Visioneer Officer

What Disney Imagineers Can Teach Drug and Therapy Discovery Operations About Data and Reproducibility

In the early days of Walt Disney’s Imagineering team, the magic was real – but the record-keeping was not. According to Imagineering lore, almost nothing was written down in those pioneering years.[1] Ride concepts and design tweaks were tacit knowledge that lived in engineers’ heads or in ad-hoc conversations. Reproducibility was essentially unheard of; each project was a one-off creative adventure.

Amazingly, Walt Disney gave his Imagineers just one year to build Disneyland, and they pulled it off in 1955 with an all-hands-on-deck, “get it done” mentality. But that breakneck, undocumented creativity came at a cost: without notebooks, standards, or formal processes, it was hard to repeat their successes or efficiently train newcomers.

Fast forward a few decades: Disney was preparing to build Tokyo Disneyland (opened 1983), its first international park. At this point the Imagineers had a revelation, they could no longer rely on tribal knowledge alone. In fact, one astonishing fact from a recent Disney documentary was that Imagineers didn’t start writing things down until the Tokyo project![1]

Building a park an ocean away, with a Japanese partner company, forced Disney’s team to formalize their processes. They began keeping design notebooks, engineering specs, and adopting standards for construction and operations. This shift ensured that beloved attractions could be reproduced with consistency. After Disney adopted more rigorous record-keeping and standards, the company was able to scale its success, designing and building new parks around the world and improving efficiency without losing the “magic.”Disney Imagineering’s evolution from free-form creativity to documented, reproducible processes was a turning point that helped the company take off to new heights.

Parallels in Pharma: From Artisanal to Reproducible Science

The story of Disney’s early “data and process hygiene” problem, and later transformation, has an uncanny parallel in today’s pharmaceutical R&D. Too many (but not all) drug discovery labs have traditionally operated a bit like Walt’s early Imagineers: brilliant scientists doing amazing experiments, but often with siloed data, inconsistent record-keeping, and protocols that live in individual notebooks, scattered files, or in people’s heads.

In pharma (and science in general), this has contributed to a well-documented reproducibility crisis.[2] Too often, promising findings can’t be replicated, and data gets lost in dusty lab books or disjointed systems, or even worse, never captured.

Just as Disney eventually saw the need for standards, modern drug hunters are realizing that better data and process hygiene is not a bureaucratic burden, it’s the key to accelerating innovation. At 20/15 Visioneers, we emphasize creating a FAIR data environment (Findable, Accessible, Interoperable, Reusable) and tightening up process discipline to enable reproducible experimentation and testing.[2]

The goal is to create a lab ecosystem where every result is logged and contextualized, every protocol is standardized, and experiments can be readily repeated or built upon, in essence, making science as reproducible as it is creative, with FAIR, Model-Quality Data (MQD).

In the incredibly complex pursuit of a drug or therapy that can help someone prevent, cure, or treat a disease or disorder that causes low quality of life, it is essential to document not only the successes, but also the failures, it has to be the complete picture.

How Big Is the Problem Today?

Consider these findings from industry and academia:

• High cost of irreproducibility: An estimated $28 billion per year is spent on U.S. biomedical research that cannot be reproduced by others.[3]

• Alarming validation rates: Bayer (2011) could validate only ~25% of the preclinical studies it examined internally.[4]

• Landmark studies in question: At Amgen, scientists replicated just 6 of 53 landmark cancer biology papers.[4]

These sobering numbers underscore how much potential knowledge, and potentially life-saving therapies, may be slipping through the cracks due to poor reproducibility.

Why It Matters Now: AI and the Next Leap in R&D

We’re now in the age of AI and machine learning, where computers can analyze vast datasets to find patterns no human could. But the old computing adage still holds true: “Garbage in, garbage out.” AI is only as good as the data we feed it. If experimental data is incomplete, inconsistent, or inaccessible, even the most sophisticated AI models will struggle to deliver meaningful insights.

On the flip side, if pharma companies embrace the Imagineering mindset, meticulous documentation and standardized processes, they create a foundation of reliable, high-quality data that AI can truly leverage.

Recent expert roundtables in drug discovery emphasize that real AI progress depends on three pillars:high-quality data, strong governance, and reproducibility.[5]

As one Nature article noted, even when perfect reproducibility isn’t possible, following FAIR data practices ensures at least “scientific correctness” and integrity in findings.[6]

Imagine the Future…

Picture a world where every medicinal chemistry reaction, every biology assay, every clinical trial result is captured in FAIR, searchable form — much like how Disney now keeps blueprints and design standards.

In that world:

• Scientists (or AI) could instantly find prior work instead of repeating it.

• Experiments could be replicated across sites and teams.

• AI could rapidly propose new candidates or conditions — cutting years off the R&D timeline.

• Learning accelerates, instead of being trapped in siloed trial-and-error cycles.

Embracing the Lesson: Quality Data = Quality Results

The lesson from the Imagineers is powerful: document the magic.In their world, documentation turned fleeting ideas into scalable experiences — from Anaheim to Tokyo to Paris. In the pharmaceutical world, combining creativity with reproducible, well-documented science can turn breakthroughs into real-world therapies.

There are encouraging signs:

✔ ELNs and LIMS adoption is rising

✔ FAIR data principles are gaining traction

✔ Data stewardship roles are being created

✔ Cultural change is underway: an experiment isn’t complete until the data is accessible and sharable

We continue to evangelize for data hygiene, painting a vision of labs where FAIR data and reproducible science are the norm.[2]

Conclusion

Creativity and serendipity will always be at the heart of discovery, whether designing a theme park or uncovering a new therapy. But when paired with disciplined record-keeping and reproducible methods, creativity becomes scalable. It becomes a force multiplier.

In the era of AI, where data is fuel for innovation, that mindset could transform drug development and accelerate the journey to new cures. Just like Disney learned: when you capture the spark and bottle it in a process, you can light up the whole world with it.

References

• Leslie Iwerks – The Imagineering Story (SYFY Wire) [1]

• John F. Conway (20/15 Visioneers) – Next-Gen Lab Roadmap [2]

• Monya Baker, Nature News: “Irreproducible biology research costs put at $28B per year” (2015) [3]

• IDBS Blog: “Replicating Science: $28 Billion is Wasted Every Year in the US Alone” (2020) [4]

• Digital Science Roundtable: “AI in Drug Discovery – Key Insights” (2025) [5]

• Scientific Data (Nature) – “FAIR for AI” Perspective (2023) [6]