Ask the question and you will receive! - How to find data that leads to insightful questions for navigating complexity?
“Creativity is the power to connect the seemingly unconnected” William Polmer
Data is everywhere in today’s world and plays a critical role in understanding complexity. However, when dealing with complexities of areas affected by violence it is important to look beyond collating large amounts of data and the quality of it, but instead focus on the role data can play in understanding the dynamic nature of complex settings.
In this blog, I draw from some of the lessons we have been learning supporting peace and conflict researchers in Yemen, Myanmar and Syria. In particular how to search for data that enables us to ask more precise questions and gives us insights into how complex conflict settings function.
The search for data in a world of complexity
Complex systems consist of many interacting actors, incentives, interests and beliefs that exhibit emergent behaviours. These interactions can lead to recurrent cycles of violence and responses, that result in unpredictable or chaotic behaviour. Therefore, to understand complexity, data should not be viewed in isolation but rather as a reflection of the larger conflict system and power dynamics. However, having so many variables (interests, incentives, values, actors, and institutions) it can be difficult to know which factors are most important or how they interact with one another. Therefore, the structuring of data is required to understand these interactions and their significance.
Searching for data to explain the dynamic nature of conflict settings?
To visualise the conflict systems, the interlinking factors and networks, we deploy graph database technology. Using graph database technology requires structuring the data into data models. A data model is a conceptual representation of the data and its relationships used to organize and understand it. Using a data model provides a framework for understanding the relationships and patterns of behaviours driving conflict dynamics allowing us to test hypotheses on the nature of the conflict. These data models helped us to focus on what type of data we needed to look for.
Below is a simplified example of a data model used, for exploring how networks establish armed control in specific locations
- “Person” (Node) as “PART OF” (relationship) a “Political Party” (Node) and “COMMANDS” (relationship) an “Armed Group” (Node)
- “Person” is “AFFILIATED TO” (relationship) a “Tribe” (Node) that is “LOCATED_IN” a “Location”
- Country (Node) “FUNDS” (relationship) “Political Party” (Node) and “Armed Group” (Node)
- “Armed Group” that is “LOCATED_IN” a “Location”
How can we use data to help ask better questions?
Using data models helps us gain insights because it enables us to navigate and interact with complexity. In complex conflict settings, patterns emerge from people reacting and responding to one another as they self-organise to pursue or protect their interests. However, uncovering these factors is not often immediately apparent from looking at the raw data. Instead, an iterative process of discovery and querying is required.
Structuring data helps us organise data into a logical configuration, which makes it easier to explore the data in an iterative manner. Building our data through querying improves our ability to better understand the nature of relationships and how actors are connected both to other actors or to structural factors. For example, how does an institution exert control in one area but cannot do so in others, or what areas exhibit greater competition? or what different alliances are being formed to enable powerful actors to maintain control?
Building our data sets through this iterative process of querying makes it easier to identify the type of data we need that explains a specific dynamic or relationship when conducting desk-based research. So, our search for data becomes more precise and the insights that we uncover become more defined. This iterative process of building data helps identify gaps in our data and prioritise which data sources to focus on. This is invaluable when considering how costly and time-consuming data collation can be in conflict settings.
Building our data in this iterative way is also helpful for providing a common language for a range of experts to validate insights and make collective decisions. Conflict settings are multi-dimensional, and providing a structure for the data helps experts and stakeholders from different thematic areas to better understand the interactions. This enables them to develop a common terminology and test hypotheses about underlying interests and the nature of alliances. This is invaluable in conflict settings for making decisions where the outcomes can often be uncertain and involve a degree of risk.
Working through complex settings is challenging, however, our work with peace researchers is helping us better understand the value of structured data and how it improves our ability to ask better questions. While the process of structuring data is difficult, we are finding that learning these skills will be invaluable to navigating complex challenges and harnessing the power of data to drive positive change.