As we work our way backwards through the different capabilities that biotech startups need their data tooling to support, we’ve so far gone through decision making and analysis. This week, I want to cover one of the last things you do right before you start the analysis: finding the data. This is closely related to an even earlier step that we’ll cover a bit later - evaluating what data you have (or can generate) to define your approach and strategy. In particular, the both rely on something that’s often surprisingly difficult: Knowing what data you have and where it is.
Don't let good be the enemy of good enough
Don't let good be the enemy of good enough
Don't let good be the enemy of good enough
As we work our way backwards through the different capabilities that biotech startups need their data tooling to support, we’ve so far gone through decision making and analysis. This week, I want to cover one of the last things you do right before you start the analysis: finding the data. This is closely related to an even earlier step that we’ll cover a bit later - evaluating what data you have (or can generate) to define your approach and strategy. In particular, the both rely on something that’s often surprisingly difficult: Knowing what data you have and where it is.