The Blind Philosopher’s Database

Photo by Nam Anh on Unsplash Scaling a biotech research platform requires an organization to define and optimize the flow of data, which can be broken down into three questions: What tasks do we need data for (and what data do we need for these tasks)? 2. How will we collect/acquire/generate this data? 3. HowContinue reading “The Blind Philosopher’s Database”

Scaling Biotech: A Framework

Photo by Ashkan Forouzani on Unsplash Scaling a biotech research platform requires a data platform that enables a wide range of project and functional teams to efficiently and effectively coordinate and share data. Over the last few months, I’ve been writing about different aspects of this, circling around a mental model for making decisions aboutContinue reading “Scaling Biotech: A Framework”

The Experiment Cost Inflection Point

Photo by Oliver Roos on Unsplash Scaling a Biotech research program means managing and optimizing sequences of closely interrelated experiments, i.e. the Experiment Factory. A lot has been written about the value of creating short, inexpensive experiments to explore an idea. But is there ever value in making an experiment slower and more expensive? (MyContinue reading “The Experiment Cost Inflection Point”

The Experiment Factory

Image by Lou Blazquez from Pixabay For a biotech organization to scale its research program, it must balance the flexibility needed to explore a variety of biological hypotheses against the consistency needed to make a collection of observations into more than the sum of its parts. The balance between these opposing forces will shift overContinue reading “The Experiment Factory”

The Operational-Analytical Data Cycle

Photo by Miguel Orós on Unsplash For a biotech organization to effectively scale its research program, its data platform must enable all users to leverage as much data as possible within their decision making context – the levers that allow them to make decisions and take action. This is easier said than done, and oneContinue reading “The Operational-Analytical Data Cycle”

Designing a Chimera Data Platform

Image by Gordon Johnson from Pixabay One of the key roles of a software team at a Biotech organization is to enable the research program to scale without sacrificing flexibility and innovation. I’ll write more about this in upcoming posts, but today I want to explore how my understanding of the scope of this workContinue reading “Designing a Chimera Data Platform”

Building Your Data Governance Toolbox

Photo by Cesar Carlevarino Aragon on Unsplash When you first start learning about data governance, it often seems like a hairball of tightly knit ideas where you can’t understand any one piece until you’ve studied and learned the whole thing. I’m not an expert by any stretch, but I’ve wrestled with learning about data governanceContinue reading “Building Your Data Governance Toolbox”

Layers of Data Infrastructure 3: Storage

Photo by Cobro on Unsplash In my last two posts I’ve explored the high-level design decisions related to two of the three layers that define each pipeline stage of each category of data use cases: Control and Compute. The Control layer defines how the user interacts with the system, while the Compute layer defines howContinue reading “Layers of Data Infrastructure 3: Storage”

Data Infrastructure Layers 2: Compute

Photo by Noah Negishi on Unsplash In my last post I described how you can think of your organization’s data infrastructure as a grid of blocks defined by category of use case and stage of the pipeline. Each block can be further broken down into three layers: Control, Compute and Storage. Last time I brieflyContinue reading “Data Infrastructure Layers 2: Compute”