Scaling Biotech

Scaling Biotech

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Year-end Summary 2021

Forming consensus

Over-communicating is difficult

People Trust Pessimists

A platform story reference guide

Chicken and Egg Decisions

Along for the ride

Structuring communication with stories

The magic (trick) of framing

Getting everyone on the same page

The first "why" of your story

Communicating with Stories

Seeing the hard part

The Operational Stack

Constructive Conflict

Distributed Metaphors

The crystallized amoeba

On flexibility, consistency and schema enforcement

Are you buying the software or the schema?

The Blind Philosopher's Database

Process Automation vs Process Change

The Change Mindset

Organizational debt

Scaling Biotech: A Framework

Reader feedback request

Too much information

The end of Big Data?

Making data travel

Stats vs ML

Theory vs Data

Metrics and Proxies

The Experiment Cost Inflection Point

Optimizing the Experiment Factory

ELN vs LIMS

The learning curve and the experiment factory

The Experiment Factory

Does your data science team need an anthropologist?

What does data democratization really mean?

Are you relying on trickle-down infonomics?

The Operational-Analytical Data Cycle

Meet users where their levers are

Understanding your users' levers

Name change: Scaling Biotech

Operational vs Analytical Data

Designing a Chimera Data Platform

Don't let Machine Learning be the enemy of Data Science

You can't share accountability

Building you Data Governance Toolbox

© 2025 Jesse Johnson
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