Photo by Patrick Tomasso on Unsplash Scaling a biotech platform requires getting a group of people from different backgrounds and perspectives to understand a common vision and plan. But it can sometime be difficult to recognize where the differences and gaps are. So, how can you make sure you’re communicating all the things that youContinue reading “Structuring communication with stories”
Image by Gerhard G. from Pixabay Scaling a Biotech research platform requires getting many people from a wide range of backgrounds and mindsets to adopt a common vision and plan. The more variation and inconsistency about what your colleagues think they’re working towards, the harder it’s going to be to coordinate the work that willContinue reading “Communicating with Stories”
Photo by Omar Flores on Unsplash Scaling a biotech research platform requires combining a collection of different software systems and components into a single coherent whole, aka a Chimera Data Platform. To keep this system maintainable as it grows, it needs enough consistency to ensure that a small group of developers can quickly understand eachContinue reading “Distributed Metaphors”
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”
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”
Image by Gerhard G. from Pixabay Scaling a biotech research program requires coordinating the flow of data between teams, functions and scientific fields, from the people that generate it to the ones that use it. You may think of data as objective and universal. But in fact, its interpretation depends on the context in whichContinue reading “Making Data Travel”
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