Blog by Andreas Pawelke: “Development organizations increasingly embrace systems thinking (and portfolio approaches) in tackling complex challenges.
At the same time, there is a growing supply of (novel) data sources and analytical methods available to the development sector.
Little evidence exists, however, of these two seemingly contrasting disciplines to be combined by development practitioners for systems transformation with little progress made since 2019 when Thea Snow called for system thinkers and data scientists to work together.
This is not to say that system thinkers disregard data in their work. A range of data types is used, in particular the thick, rich, qualitative data from observations, deep listening and micro-narratives. And already back in 2013, MIT researchers organized an entire conference around big data and systems thinking.
When it comes to the use of non-traditional data in the work of system innovators in international development, however, there seems to be little in terms of examples and experiences.
Enhancing system innovation?
Is there a (bigger) role to play for non-traditional data in the systems work of development organizations?
Let’s start with definitions:
A system is an interconnected set of elements that form a unified whole or serve a function.
Systems thinking is about recognizing and taking into account the complexity of the world while trying to understand how the elements of a system are interconnected and how they influence each other.
System innovation emphasizes the act of changing (shifting) systems through innovations to a system (transformation), not within a system (improvement).
Non-traditional data refers to data that is digitally captured, mediated or observed. Such data is often (but not always) unstructured, big and used as proxies for purposes unrelated to its initial collection. We’re talking about the large quantities of digital data generated from our digital interactions and transactions but also (more or less) novel sources like satellites and drones that generate data that is readily available at large spatial and temporal scales.
There are at least three ways how non-traditional data could be used to enhance the practice of system innovation in the development sector:
- Observe: gain a better understanding of a system
- Shift: identify entry points of interventions and model potential outcomes
- Learn: measure and observe changes in a system over time..(More)”