An Agile Framework Designed for Data Science

Key Tenets of Data Driven Scrum

AGILE IS ITERATIVE EXPERIMENTATION

Agile is intended to be a sequence of iterative experimentation and adaptation cycles.

Iterations are Capability Based

Teams work iteratively on a given set of items until they are done (no inflexible deadlines).

Focus on Create, Observe, Analyze

Each iteration always follows three core steps: Create something, observe its performance, and analyze the results.

Easily Integrate with Scrum

DDS's interfaces can be seamlessly integrated within a traditional Scrum-based organization.
 
Develop & evaluate ideas

Develop & evaluate ideas

No arbitrary sprint timelines

Similarities with Traditional Scrum

Roles, Events and the use of an Item Backlog

Similar Roles

Just like traditional Scrum, each DDS team is a group of three to nine people, one of whom is the product owner, and one of whom is the process master.

Similar Events

Just as in traditional Scrum, there is a daily stand-up, as well as Iteration and Retrospective Reviews

Similar process to create and prioritize items

Just like traditional Scrum, items are created, prioritized and viewed on a task board.

Differences between Data Driven Scrum and Scrum

DDS has variable length iterations with flexible task estimation
Functional Iterations
Flexible Estimation
Collective Analysis
Iteration independent meetings

The framework is based on Research & Experience, such as this recent paper

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