Value Proposition
Project phase: Business and Data Understanding
You make explicit what value your solution creates and for whom: the target user and their job to be done, the pains they experience today, and how your proposed machine learning solution relieves those pains or creates gains, expressed in terms the client recognises. You include how the value would be measured in practice — time saved, errors avoided, decisions improved — contrast your approach with the current way of working, and base every claim on what you learned from stakeholders and data rather than assumptions.
Starting Points
Key Points
- I have clearly identified the target user and their "job to be done."
- I have distinguished between the main problem and secondary issues within the societal context.
- I have described the current way of working and why it is insufficient.
- I have explained how the ML solution relieves specific pains or creates specific gains.
- I have included measurable success metrics (e.g., time saved, error reduction, better decision-making).
- I have used arguments backed by stakeholder input and data, not just assumptions.
- My language and tone are specifically adapted to the target audience (e.g., business owners vs. technical leads).
- The report is a standalone markdown document, made available in GitLab.