People generally think of projects sequentially (i.e. like gantt charts).

However, data science is inherently circular and iterative.

This creates tension.

Here’s one way to ease the tension:

1. Make your DS iterations structured using a framework like CRISP-DM

This allows work to happen naturally, yet adds structure, discipline and decision points to a naturally messy process.

2. Track progress sequentially using milestones

As iterations happen, measure progress against sequential, business value milestones.

For example: project outcomes defined, data available, baseline model complete, POC model ready, POC meets user needs, beta model deployed to users, users adopting the model’s prediction, etc.

Work iteratively. Measure progress sequentially.

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