Is a “JIRA” mindset for Data Science the way to execute Data Science projects? Can some structure be put in place for Data Science projects? Is there an an Agile Manifesto for Data Science projects?
The Agile Manifesto for Data Science
Well…there isn’t an official manifesto for Data Science, but there was a good one published by Russell Jurney in his book — Agile Data Science 2.0. The seven principles captured in the book are:
- Iterate, iterate, iterate: tables, charts, reports, predictions.
- Ship intermediate output. Even failed experiments have output.
- Prototype experiments over implementing tasks.
- Integrate the tyrannical opinion of data in product management.
- Climb up and down the data-value pyramid as we work.
- Discover and pursue the critical path to a killer product.
- Get meta. Describe the process, not just the end-state.
Project Management for Data Science
Does this mean Agile is suited for Data Science? There are both sides to it. An interesting quote on this states:
The best you can do is experiment, as rapidly as possible. And then use the results of your experiment to decide what to experiment on next, and then gather the next set of results and so on. In other words you need to iterate.
We will get into more details about how Data Science projects can be managed in subsequent articles.
Are you using Agile frameworks for Data Science projects? Do share your thoughts on how it has been implemented in your organisation.