As I discussed in my last post, I’m working on a list of principles analogous to the Agile Manifesto, but tailored to data teams embedded in biotech organizations. In the next few months, I’m going to be writing about some proposed principles, the anti-patterns that they’re addressing, and why they’re important. The initial few will be about setting objectives, and here’s the first:
Our highest priority is to drive progress towards organizational objectives.
The anti-pattern that this addresses is data teams setting and prioritizing technical objectives that may contribute to organizational goals but aren’t completely aligned with them. Maybe your predictive model answers an interesting (and publishable) question, but it ignores the less interesting yet more existential questions that would actually drive the pipeline. Maybe your dashboard gives bench scientists the information they asked for, but doesn’t fit into their workflows in a way that they can use.
In a tech biotech, the data team needs to play an equal role with the wet lab in driving the whole organization’s objectives - validating the approach, advancing pipelines, building the platform. If the model is 99% accurate and the dashboard is deployed ahead of schedule, but the drug never makes it to the clinic, then the data and the wet lab teams have both failed.
In other words, the data team should consider itself accountable for more than just the technical deliverables and requirements. They must also be accountable for ensuring the tools they’re building are right for the organization, and that those tools are integrated into a coherent pipeline and platform. Every decision should be traceable through a deliberate and defensible sequence of “why”s to an organizational objective. If the sequence stops at a technical objective, the data team becomes just another service organization.
Don't stop at technical objectives
" traceable through a deliberate and defensible sequence ". That combination is worth a double like. In Calculus, it is often useful to have both a continuous function and a continuous derivative. In biotech, economics change, new findings are made, and occasionally previous work is retracted. All make for a challenging landscape. We rely on stability - deliberate and defensible are good policies to have in place. Scalability is never guaranteed, as new types of interactions are possible.