Why take an interdisciplinary approach to data-driven decision-making around complex social problems?

“Data is the new gold”. This is a sentiment that has been repeatedly broadcasted by the big tech players and politicians alike. That may be the case, but what good is data if you do not know how to approach it to look for what you need to know, not to mention learn what you may not know (the unknown unknowns)? There is increasing focus in turning to data analytics to get more value from data to inform decision making, but what is the best way of doing so?

Taking an interdisciplinary approach to producing data-driven insights around social problems can enhance the reliability and validity of the findings, thereby supporting organisations to cut costs and drive efficiencies in evidence-based decision making to tackle complex social problems and increase societal wellbeing.

In this post, we look at the challenge of data-driven human security analysis for military planning and the value of an interdisciplinary approach.

How can we get meaningful insights from data to support complex decision making around human security?

A key-value stemming from an interdisciplinary approach to data analytics is the ability to validate insights through working with the end-users and those with domain expertise (both conceptual and technical).

Our domain expertise regarding the human security problem lies with a rigorous approach to research. Our team have experience of both the theoretical basis of the topic at hand (e.g., women, peace & security, human rights, gender-based violence, modern slavery and human trafficking, children in conflict, among others) and the skills to apply the theory in an applied manner.

Doing so makes it possible to integrate theoretical concepts into practical considerations that govern the world our end users are operating in, and furthermore, the wider theory and policy that governs them (i.e., defence studies and military planning). Likewise, our solution architects and data scientists bring the state of the art in responsible data analytics to the table to consider how to tackle the problem.

To get meaningful insights from data it is necessary to understand the problem at hand and the intricacies at play, context is key. Let’s look at an example.

A challenge such as how to improve analytical measures to understand human security threats and vulnerabilities in conflict requires a multidisciplinary team that can approach the problem that military planners (i.e., the end user) is facing from different angles.

First, there are those in the team that need to bring domain expertise to the table to understand the problem. The social scientists, human rights experts, defence specialists and data scientists need to think about what human security means as it relates to conflict.

  • What are the threats and vulnerabilities that undermine human security?
  • What should/do the military care about?
  • What insights do we need to bring them?

Their individual approaches and understanding of the problem-set aids them in beginning to unpack the problem and is a key starting point for the process of collecting user stories. Avoiding disciplinary silos, their varying viewpoints have to be brought together – here communication is essential. Together in collaboration with the end-user, the team can begin to shape a cohesive understanding of the intricacies at play and what needs to be achieved.

Second, the solution architects and data scientists need to conduct a technical needs assessment that helps them to further understand what solutions can be applied and developed to help the end-users with their problem and what they are looking to achieve (e.g., data management, data storage, data analytics, data visualisation). This can involve the development of further user stories.

Third, communicating the various findings to the group at large, the domain experts need to work with the solution architects and the data scientists to consider how a data-driven approach can support the end-users’ goal and meet the requirements as gathered across the user story elicitation process.

It is necessary for the team to draw on the user stories gathered to start to unpack the problem:

  • Consider what data sources can be leveraged
  • How machine learning and analytical techniques can be used to allow users to interact with the data
  • Filter the noise from large-messy data and focus on what matters to inform military planning

In doing so, we have to consider how the end-users want to engage with the insights and subsequently what technical measures can be used/are preferred to enable interaction and reporting.

Fourth, working in collaboration with the end-users the team presents the challenge and the envisioned solutions and work through the complexities to come to a point of agreement for proceeding with agile development and testing.

Over time, the ‘vision’ may adapt, the user stories may shift – but that’s ok – agile means being flexible, and doing so yields the best possible outcome.

Bringing multiple disciplines together leads to a balanced and complementary interdisciplinary co-design approach that in our view must be taken to intimately engage with the problem at hand.

Responsible innovation

Our interdisciplinary approach goes beyond simply tackling the problem, to ensuring that our research and development activities are conducted in a manner that is bound by principles of responsible research and innovation. Such a measure is critical in a world that, rightly so, is rife with concern over the data protection and the ethics of data analytics and artificial intelligence.

To do so we place great emphasis on incorporating privacy, ethical and security by design practices into our agile development cycle. Engaging and integrating data protection, ethical impact assessments and cyber risk assessment into our work ensure transparency of the way in which our tools operate and to mitigate any potential risks that may lie in both the tools or the way in which the user may operate them.  

Want to leverage your data?

From our standpoint, when leveraging data analytics to inform business intelligence and/or to have a positive social impact, it is necessary to go beyond simply applying analytical techniques, to focusing on context and truly understanding the dynamics of the problem set at play. Doing so can improve the value of the insights and their usefulness to inform decision making.

For Trilateral, our tried and tested methods in leveraging a team of multi-disciplinary experts, in an agile and co-design manner allows us to develop bespoke solutions that are fit for purpose.

For more information contact our team:

Hayley Watson, Senior Practice Manager at Trilateral Research

Hayley Watson


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