Problem:
Our data science work is inconsistent. This is starting to hurt as we grow.
If this sounds familiar, you're not alone.
A team of 2 can do ad-hoc data science. Not a team of 10. Especially when you're remote. Worse; as your team grows, your median years of experience will drop. More people, less intuition.
Workflows helps you standardize and scale best practices as flexible project work templates. Do more consistent, quality work faster.
If you don't standardize how you work, you can't scale effectively.
Workflows turn ad-hoc work into templates your team can execute. This helps you spread better ways of working. Improve team autonomy, velocity, quality, and consistency.
Efficacy
Execute data science workflows effectively. Recall fewer systems from prod. Deal with less unacceptable and unexpected behavior.
Velocity
Projects move quickly from concept to production with clear expectations and requirements, coordination, and high visibility.
Consistency
Follow predictable standards and best-practices that everyone agrees on.
Continuity
Known data failure modes are analyzed and small steps are constantly taken to prevent them.
Knowability
An automated papertrail of how your people, processes, and tools worked together.
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