As we continue working through my list of principles for embedded biotech data teams, we move on to how we design and frame projects: Design projects around scientific objectives coordinated with the overall organization. This continues the theme from the last two weeks of viewing the work of a data team as broader than just the technical outputs. Today’s principle addresses two common failure modes: Defining objectives that stop at the technical outputs or, even worse, not explicitly defining objectives at all.
"technical debt" is real. However, it also can be used a way just to spin cycles. Rewriting code and capriciously updating tools, can a) be disastrous, b) achieve no net gain, c) distract from more operational needs. Coders who are too keen to address technical debt might just be seeing the twigs and not the trees.
Writing down "goals and objectives" is hard. Oftentimes they are replaced by slogans and platitudes. Developing metrics is key. If the goals for each department are the same, there is a flaw. If a goal is too easily agreed to, that's another weak link. Revisiting goals along the way, might just prevent a runaway traiin.
"technical debt" is real. However, it also can be used a way just to spin cycles. Rewriting code and capriciously updating tools, can a) be disastrous, b) achieve no net gain, c) distract from more operational needs. Coders who are too keen to address technical debt might just be seeing the twigs and not the trees.
What does technical debt look like in biotech?
Writing down "goals and objectives" is hard. Oftentimes they are replaced by slogans and platitudes. Developing metrics is key. If the goals for each department are the same, there is a flaw. If a goal is too easily agreed to, that's another weak link. Revisiting goals along the way, might just prevent a runaway traiin.