Year-end Summary 2021

Image by hudsoncrafted from Pixabay

I started writing this blog in early 2020, but it was only this year that I started writing regularly, here and on my newsletter.

When I started, I didn’t know exactly what I wanted to write about, or why I wanted to write it, beyond generally wanting to get back into blogging.

But I figured if I just started writing, I would eventually figure it out.

(This is a well established strategy, not a new idea.)

Well, it worked more or less. It ended up being a great way to collect my thoughts, figure out some themes that I care about, and even learn a few things.

In this post, I want to review how this happened, and tease some ideas I might write about next year.

This post is mostly for my own sake – a way to help me collect my thoughts.

But feel free to follow along.

How it Started

When I started writing this blog, I thought of myself as a software engineering working in Biotech.

I wanted to design and develop software that would accelerate biotech startups like the one I was working for.

So my writing focused on software/data engineering and design in the context of biotech.

However, as I started trying to tease out topics I could write about from the work I was doing and the problems I was trying to solve, I realized that wasn’t actually the role I had ended up in.

Instead, my role had really become one of pushing the organization to change in ways that would allow it to use data more effectively.

As I figured this out, I shifted the focus of my writing.

I started by trying to define the notion a Chimera Data Platform.

Then I worked through different ideas until I came up with a framework for designing such a platform by considering different trade-offs.

But as I started writing about the details of databases and distributed systems according to this framework, I realized I was still too focused on the software, and not enough on the organization.

Getting Closer

I wanted to start writing more about how to help an organization shift towards using data and data science more effectively.

But there was a problem: I didn’t have a clear, straightforward strategy for this side of the equation.

I did, however, have a place to start: Storytelling.

Between teaching (in my academic days), blogging (throughout) and the management style I’ve adopted, I’ve always done a lot of explaining.

And from explaining different things to a range of different audiences, I’ve come to appreciate how structuring information in stories can help you introduce new ideas and change people’s perspectives.

So this gave me a place to start writing and continue to figure out where I’m going.

Up Next

Writing about storytelling has helped me think about what makes stories so important.

And the answer helped me crystallize a theme that had been circling throughout everything I wrote this year: How to coordinate across the very different backgrounds and mindsets that you find within a biotech organization.

Biologists, chemists, doctors, computational biologists, bioinformaticians, data scientists, software engineers.

They all approach problems differently, think about their work in different ways, and use different language and frameworks to communicate.

This variety of ideas is one of the great things about working in biotech.

But it also makes everything more difficult.

In psychology, there’s a notion of a shared mental model – a common way that a team thinks about their goals, their tools, and their strategy for reaching those goals.

The research shows that teams with a good shared mental model perform much more efficiently and effectively.

But creating a shared mental model from such a wide range of backgrounds and mindsets is no easy task.

In my writing about stories, I hinted that telling stories might be one approach to creating a shared mental model.

Indeed, there’s evidence that the way leaders communicate can have an impact on how these shared models form.

But there are other things as well – which I plan to write more about in 2022.


If you’ve followed along as I’ve explored these different topics, thank you.

I don’t know exactly what topics I’ll explore next year beyond where I’m going immediately next, and the general motivation to help biotech organizations use data more effectively as they scale their research programs.

But I’m looking forward to finding out.

See you next year!

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