1/ You can't bolt AI onto chaos.
In biotech, if your data is a mess, your AI won't save you.
Build the data strategy first. Here's how.
2/
Real-world data isn't AI-ready.
Without structure, governance, and clarity, it’s noise.
AI needs fuel. And that fuel is clean data.
3/
At a biotech startup, we learned this the hard way.
Here’s what I took from a panel and years of practice.
The essentials:
Governance
Management
Metadata
Team dynamics
Tool choices
4/
Start with data governance.
Access control.
Versioning.
Basic security.
Do it early. Fixing leaks later costs 10x more.
5/
Cloud is great—but only if you use it right.
Define who sees what.
Set folder rules.
Use Google or AWS security playbooks. They’re free and solid.
6/
Next up: Data management.
Chaos begins with "just toss it in the drive."
Don’t.
Structure folders. Standardize metadata.
Make it findable again.
7/
Spreadsheets are fine—until they aren’t.
Start smart:
“Female” not “F”
No weird characters
Train your wet lab team. Seriously.
8/
You’ll accrue technical debt. That’s fine.
If someone curses your naming scheme 5 years from now, congrats.
You survived.
9/
But please—do the basics right.
Read this paper. Print it. Frame it.
“Data Organization in Spreadsheets”
tandfonline.com/doi/full/10.10…
10/
Public data is cheap.
In-house data is gold.
Use a LIMS to track it.
Know where each sample came from.
Know what each file means.
11/
Aim for FAIR:
Findable
Accessible
Interoperable
Reusable
Even doing 70% right will put you ahead.
12/
Keep it simple:
Internal/
├── RNAseq/
├── WGS/
Public/
├── TCGA/
├── ENCODE/
Each with a README. Just say what the data is and where it came from.
13/
README template:
When was this data generated?
What experiment?
Where’s the preprocessing code?
Who should I ask?
14/
You’ll get pressure to move fast.
Investors want plots, not pipelines.
But for big projects—do it right.
Rushed analysis rots from the inside.
15/
Custom tools give you power.
Commercial tools give you speed.
Pick based on your team’s skill—not vendor marketing decks.
16/
And finally—people.
Your computational and wet lab teams must sit together.
Talk daily. Argue weekly. Trust always.
17/
Example:
Bioinformaticians prep the Seurat object.
Wet lab explores it in Shiny.
This builds insight AND independence.
18/
Good data strategy isn't sexy.
But it's the foundation.
It makes your R&D faster, your AI smarter, and your team happier.
19/
Startups die by disorganized data.
Don’t be one of them.
Fix your foundation now—before the chaos scales.
20/
Have you seen data disasters in biotech?
How did you fix it—or not?
Reply and let’s trade war stories.
I hope you've found this post helpful.
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