Complexity Navigator
Problem:
We don't know how our moving parts connect.
We can't predict success or failure.
If this sounds familiar, you're not alone.
When teams grow, they lose track of how the pieces fit. This leads to unwanted surprises; usually in the form of defective projects, scope creep, low velocity, and problems caught too late.
Complexity Navigator helps your team drill down into the network of connections that power your success.
The complexity of a data science project isn’t the number of moving parts; it’s the number of relationships between the parts.
Complexity Navigator helps you visualize relationships; between people, datasets, models, workflows, and policies - and make better decisions faster.
Moving parts are inevitable.
Unknown dependencies aren't.
If you don't know how things connect, you can't predict how they interact.
Model the relationships underneath your data science processes as the weighted edges in a graph.
Uncover otherwise opaque dependencies.
Track how project success and failure spread through the network.
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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