How do you decide what data is meaningful to collect in data science?

[Steven.J]

Data science is powerful for finding patterns, but the real challenge is often framing the right question. What's a common problem where the hardest part is deciding what data would even be meaningful to collect?

[Joshua.M]

A classic is churn analysis The real difficulty is choosing signals that truly predict a user leaving instead of chasing every new metric

[William.J]

In product design the toughest question is what to measure Not every click matters so you weigh what shows real value like feature adoption and long term use rather than vanity counts

[Camila_A]

Education apps reveal the trap of data for data sake The hard part is defining outcomes like skill gains or confidence shifts and then designing lightweight checks that keep the user experience smooth This aligns with data science 2025 trends

[Mark_J]

Healthcare projects often stumble on what outcomes matter The right data questions start with the end result for example symptom relief adherence to plan and hospital time The data you choose should guide what actions you take

[Eric63]

If you get stuck ask what decision will hinge on the data and what decision would change if you had it That framing narrows data collection to what actually moves the needle