WeSeeHope is a small charity with big ambitions. Over the past 15 years, it has raised over £15 million to support and give hope to children and young people orphaned or made vulnerable by HIV/AIDS and poverty-related issues in Southern and Eastern Africa. Today, it supports around 50,000 young people each year – but by 2020, it has set itself the goal of tripling that number.
Its most successful initiative to date has been the Village Investors Programme (VIP), which gives parents and guardians in local communities the opportunity to pool their resources and borrow small loans in order to build businesses. In this way, these communities become better able to look after the children in their care.
By mid-2015, WeSeeHope had started 244 VIP groups in Uganda. Each group numbers around 30 people, who may be collectively responsible for as many as 150 children. Over three years, the average income of people involved in VIP groups grows from $1 per day to $3 per day.
In recent years, as the charity has grown, it’s become increasingly clear that so too have its data analysis needs, says Kasia Morgan, head of fundraising at the charity:
On the programme side, we’ve realised that we need to put more systems and processes around collecting and analysing data that will show us where things are working, where they’re not and what’s the impact of our work.
And in fundraising, too, there’s a need to understand data better and share it with supporters. For some supporters, it’s the anecdotal stuff that really impacts them, but for others, we know that they’re really keen to see the stats – and to use the stats to tell others about what we do as an organisation. If we can accompany our stories with solid data and evidence of our impact through data, that’ll help build our case for support.
So in recent years, WeSeeHope has been working to understand its data analysis needs better – an effort in which it has received significant support from business intelligence software company Qlik. Not only has the charity been gifted analytics software and consulting services from the vendor, but also Qlik employees have stepped up to the plate with their own fundraising activities, matched by generous donations from the company itself. According to Morgan, of the £1 million that WeSeeHope raised in its last financial year, almost a third (£300,000) was Qlik-related.
The VIP, meanwhile, has been an excellent place to begin navigating the field of data analysis and developing WeSeeHope’s skills in this area, as programme manager Lizzie
At the moment, we’re developing with Qlik an app for collecting all the VIP data: how many VIP groups we have, across how many countries and then basic information on how much they save each week, details of individual members and their situations, and how many orphans and vulnerable children they’re looking after. We also collect data on how loans are used, the kinds of businesses they’re starting and what profits those businesses generate.
But while it’s all very well to see that the economic status of people involved in VIPs is growing, what we always want to track is that the quality of life for the children in their care is growing, too. We have to be sure that the benefits that VIPs create reaches the children who are our main focus. So that means we also need to collect child-related data, on school attendance, payment of school fees, how many meals they have per day, and so on.
These kinds of insights will be essential if WeSeeHope is to press ahead with bold plans to roll out VIPs in other African countries in which it works. By 2020, it has set itself the target of setting up a minimum of 500 VIP groups, resulting in some 70,000 children being better cared for. Says Morgan:
When it comes to data analysis, VIP is a bit of a gift, because so much of it is based on numbers and on a pot of money that grows over time. So this has been a fantastic area in which to build our data analysis skills and we’ve had fantastic advice along the way from Qlik.
Having now done some good thinking upfront about how we collect data and what should be the standard procedures for analysing it, I feel like we’re in good shape to use these skills elsewhere in our operations, so we become more efficient as an organisation in the long run.
A lot of our measurement in the past has been based on qualitative measures, anecdotal evidence and observations – and I think we’d say we’ve done a pretty good job of all that. But now is a crucial time for us. It’s a crucial time for us to understand how a good grasp of data can make us even better.