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**Post II of the Causal Discovery Series by https://diksha-shrivastava13.github.io/**
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My biggest learning at DPS has been to keep things simple, although I do tend to slip sometimes. I find that an effective balance between exploration and exploitation is to ship fast and let things break (for some cases); you get strong signals for the next better approach while still fulfilling some user requirements.
Until now, I’ve tried to explain the nuances, implications and urgency of this problem from the user’s perspective. Now it’s time to dive into the technical details of the problem. It’s important to mention here that while this gig was a more research-focused one, unlike my previous experience with SAP through the Digital Product School, the expectation is the same as a Product Team working in an agile way: shipping 2-3 features per week.
So while I couldn’t solely focus on research and designing the best possible system by exploring multiple ways as I had to constantly maintain the backend, I delivered features as soon as I got incremental results from the research.
For the technical specifications to make sense, it’s important to first look at some user stories and an average user’s interactions with the platform.
The User: Country Policy Officer of South Africa (let’s say).
Their Purpose: Find important information related to an ongoing project XYZ in partnership with Germany to answer critical questions and draw proposals.
Information includes: