Workflows

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.

Transform notebooks into scalable playbooks. Workflows are customizable, flexible, full-lifecycle project rails.

Scale your best practices and help your team do better work faster with clear expectations and support.

Workflows transform ad-hoc work into scalable structure. Do more consistent, zero-defect work faster with less managerial overhead.

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Scale what works.

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

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Execute data science workflows effectively. Recall fewer systems from prod. Deal with less unacceptable and unexpected behavior.

Velocity

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Projects move quickly from concept to production with clear expectations and requirements, coordination, and high visibility.

Consistency

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Follow predictable standards and best-practices that everyone agrees on.

Continuity

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Known data failure modes are analyzed and small steps are constantly taken to prevent them.

Knowability

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An automated papertrail of how your people, processes, and tools worked together.

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