An Agile Framework Designed for Data Science

Key Tenets of Data Driven Scrum


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

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