* Tags:: #🗞️Articles , [[Data methodology|Data Methodology]] * Author:: [[liam-kane|Liam Kane]] (Senior Agile Coach in Accenture) * Link:: [Agile Data Science Part 1| The Burndown](https://theburndown.com/2017/10/30/agile-data-science/) and [Agile Data Science Part 2 | The Burndown](https://theburndown.com/2017/10/30/agile-data-science/) * Source date:: [[2017-10-28]] * Finished date:: [[2021-07-30]] - To make Agile work in the context of Data Science, we need to redefine what "value" means: >**Value**: stories that deliver demonstrable progress towards solving a business problem, from any level of the data value pyramid, ideally demonstrated to a stakeholder in a production environment for purposes of getting feedback.  - And it makes sense because we are delivering knowledge, and that is value. So an example of these "new" user stories are: > **Example**: As a project stakeholder, I want to see the results of an EDA, so that I am confident that we understand the primary drivers of customer churn rate. - I will need multiple Definitions of Done (one for EDAs, other for modeling...) - There is a reference here to the [[Agile data science 2.0|Agile Data Science 2]] book.