Why was Oikocredit looking beyond descriptive analytics?
Oikocredit is a social impact investor and worldwide cooperative active in areas including financial inclusion, agriculture and renewable energy. By the time the collaboration began, the organisation already had a data strategy, governance structures and teams using data across different parts of its work.
Most analytics, however, still focused on describing what had already happened. That can explain past performance, but it does not necessarily answer questions about what may happen next or which course of action is most appropriate.
For Oikocredit, the challenge was therefore not simply to collect more data. It was to understand how existing data could inform decisions more directly and support questions connected to the organisation’s mission.
Why does moving from descriptive to predictive and prescriptive analytics matter?
Descriptive analytics looks backwards: it shows what has happened. Predictive analytics uses data to estimate what may happen next. Prescriptive analytics goes a step further by comparing possible actions and their consequences.
For Oikocredit, this creates the possibility of moving from reporting towards questions such as where risks may emerge, how different options might perform and where resources could be allocated most effectively.
The collaboration focused on identifying which real decisions within Oikocredit could benefit from these methods. That matters because advanced analytics is more useful when it starts with a decision that people already need to make, rather than with a technology looking for an application.
How did Oikocredit and Analytics for a Better World identify suitable applications?
Oikocredit and Analytics for a Better World brought colleagues from different teams and regions together through workshops and discussions.
The process combined Oikocredit’s knowledge of its investments, operations and organisational priorities with ABW’s experience in analytics and decision science. Participants examined existing challenges and explored where predictive or prescriptive approaches could add value.
The workshops also connected colleagues who had not previously worked together but were dealing with related questions. In some cases, relevant data or skills already existed elsewhere in the organisation.
Rather than introducing one predefined solution, the collaboration focused on identifying a set of mission-related use cases that Oikocredit could explore further.
"For me, the biggest learning was in the interactions we sparked. People from different departments and even from different continents collaborated. We discovered that we have amongst our own ranks a lot of skills that we can use. And I think that we are all less scared of advanced analytics. We now know that we can do it."
Matthias Lehnert, Market Research and Data Analyst, Oikocredit
What changed through the collaboration?
One immediate result was a clearer understanding of what advanced analytics could mean in Oikocredit’s own work.
Predictive and prescriptive analytics became connected to specific organisational questions rather than remaining abstract technical concepts. Teams left the process with a clearer view of which use cases could be developed further and what data, expertise and collaboration those projects might require.
The workshops also created stronger links between teams working on related challenges across the organisation. This made it easier to see where existing knowledge, data and analytical skills could be combined. Oikocredit moved from a broad ambition to use more advanced analytics towards a more concrete set of questions and possible applications.
Why is this more than an efficiency exercise?
For Oikocredit, the potential value of advanced analytics is not limited to automating tasks or saving time. As Matthias Lehnert describes it, the more important question is whether analytics can address challenges that matter directly to the organisation’s mission. That means starting with the problem rather than the technology.
Instead of asking where AI or analytics could be introduced, teams can ask which decisions are particularly important to financial inclusion, agriculture, renewable energy or other areas of Oikocredit’s work — and whether better use of data could improve those decisions.
This shifts the focus from becoming more data-driven as an organisational goal in itself to using data where it can contribute to more effective decision-making.
What can this mean for Oikocredit next?
The next step is to move from identified use cases to testing selected applications in practice.
That means defining the decision each project is intended to improve, assessing whether the necessary data is available and testing whether predictive or prescriptive methods lead to useful changes in practice.
As those projects develop, Oikocredit can also compare what works across different teams and contexts rather than assuming that one analytical approach will apply everywhere.
The collaboration therefore provides a starting point rather than an endpoint: a clearer way to identify where advanced analytics could contribute to Oikocredit’s mission and a basis for deciding which opportunities should be developed first.
