As part of the Hunger Safety Net programme, 100,000 households in four counties hit by dry spells in northern Kenya, receive money disbursement to ensure access to minimum livelihood opportunities in these areas. HelpAge have amassed a lot of data about the programme and its beneficiaries, but more data doesn’t always make missions easier to accomplish, at least not at first.
HelpAge is an international organisation serving people to ensure that they have dignified, secure and healthy lives. As part of an effort carried out by different institutions, the Nairobi office has been involved in the Hunger Safety Net programme to ensure its accountability, by conducting longitudinal studies on the effectiveness of money disbursement to the extremely poor.
HelpAge aren’t new to data collection: just on the Hunger Safety Net programme, they have two longitudinal studies completed (baseline and first post-disbursement) which they’ll continue periodically for each disbursement round, but they needed to untap its potential.
Preparatory work, like data clean-up, was required before they could process their data. For example: their first study relied on paper-based data collection and the second utilised digital phones. They needed a way to compare both studies.
Then they needed to use it to create awareness of the people’s plight, shape advocacy, and influence policy at the grassroots, national, and regional levels.
DataShift proposed and funded HelpAge to spend five days with a data analysis expert to explore the existing dataset and gather main findings. This research was condensed into a data analysis showcasing main insights and a list of appropriate analysis techniques for this type of data and recommendations for future data collection.
We trained HelpAge in data analysis with different tools (from Excel to R and SPSS) to use in future initiatives.
HelpAge gained a broader understanding of how their choices, like their study variables, attention to detail, and questionnaire components (and resulting responses) relate to each other and how they can be analysed to inform decisions. They were surprised to learn how recognising and exploring anomalies can help improve future data collection.
HelpAge is now better able to connect their data to advocacy – from collection all the way to presentation. They can now read for trends in regards to beneficiaries and accountability, and other organisations have taken notice.
“After all, numbers are everything. To answer anything, you need to start somewhere, and you can’t make sense of anything without data. Data informs the decisions that need to be made; you can’t just ignore it and start doing anything. It’s not meaningful. Data is very crucial for any decision making.”