Why this matters
A model nobody understands changes nothing. Data science creates value only when someone makes a better decision because of it: a credit manager adjusts a policy, an NGO director moves field staff, a cooperative board changes when it buys inputs. Adverts repeatedly ask for communication with non technical people and stakeholder management, and skills frameworks list presenting results and recommending solutions as core tasks. Many strong analyses fail at this last step.
Your audience is busy and not technical
The people who act on your work are usually managers, programme officers, farmers' group leaders or donors. They want to know three things:
- What should we do?
- How sure are you?
- What happens if you are wrong?
They rarely want to know which algorithm you used. Keep the method available for anyone who asks, but lead with the decision.
Communication is part of the method
Good communication starts before analysis. Agree the question and the decision with the people who will use the answer. Check in when early results appear, so there are no surprises. A finding that contradicts what a manager believes needs careful, respectful framing and solid evidence, not a dramatic reveal in a meeting.
Ethics belongs here too
Data science in Ghana often uses personal data: transaction histories, survey answers about health and income, phone numbers and locations. How you collect, store, model and share that data affects real people and is governed by law. Communicating results responsibly includes protecting the people in the data and being honest about who a model may treat unfairly.