Thomas Essl , Senior Consultant, User Experience, QuantumBlack
QuantumBlack recently hosted a 24 hour GIG — a Global Insights Gathering, similar to a hackathon or datathon — in support of an NGO combating human trafficking. The aim was to generate insights from the organisation’s data that could be used to help improve the support of survivors on their restoration journey.
The composition of the attending teams was somewhat mixed — while many arrived with a heavy background in analytics consulting, my own team included one data engineer, and three designers.
At first it may be difficult to imagine how such a design-orientated group could contribute to such an apparently obvious data analytics challenge. However, the task would reinforce valuable lessons in how deploying an interdisciplinary approach — combining a data-driven perspective with Design Thinking — can generate unique insights which had previously been overlooked. The success of this approach relied on exploring the stories which surface at the intersection of analytics and ethnography.
Consider the problem — not the data
Design Thinking has long been a popular problem-solving process, in part because it is regarded as solution agnostic. It enables users to explore a much broader solution space than other approaches such as hypothesis-driven problem-solving.
This makes Design Thinking ideal for deployment in a data-centric environment. When searching for an answer to a data-focused question, we are obligated to first make sure the question is the right one. This additional clarification element is often considered an unnecessary drain on project resources, especially when Design is only brought into a project at delivery stage. Establishing the nature of the problem at an early stage, there can be certainty that solutions in development will address the challenge and enhance performance.
Our team followed this challenge-focused approach for the NGO GIG. We ensured usable data was available for future stages of work, but didn’t dive straight into exploring it. The majority of us didn’t even consult the data dictionary in this initial stage, as this can sometimes cause teams to jump ahead, bias their own thinking and subconsciously eliminate potential rich research areas.
Instead, we moved on with two familiar questions: What is the true underlying problem, and whose problem is it? Attempting to answer both became the preliminary objective, setting us onto the first slope of Design Thinking’s Double Diamond strategy model.
Use data as a constraint
There are many methods to choose from and combine when defining the problem space and target personas, from stakeholder mapping and service blueprints to Five Whys principle and expert. Analytics engagement differs from typical transformation projects in its focus on specific data and how this flows. This focus provides additional constraints — but contrary to popular belief, creativity often flourishes when parameters are set.
Consider how this realisation influences design activities; service blueprints gain a data layer, interview questions on this topic are added to the script, focus areas and ideas are prioritised not only by business, technical, and human constraints but by those related to data as well.
At this stage, data becomes an enabler, driving research and ideas forward while providing an additional lens to consider what can be achieved — and what’s missing.
Uncovering human stories in data
When dealing with any data involving human behaviour, regard that each data point as representative of a real person — and consider how this information translates in the real world.
Examine your project’s key parameters and question what the impact would be on a real person if <metric> was <x>. Running through this thought exercise for a series of metrics helps build a picture of the individuals represented by the data — and builds the foundation of data-infused personas which can be expanded with further research.
During our GIG to combat human trafficking, the distribution of survivors’ ages offered a striking example in how data can represent real — and tragic — human stories. When exploring correlations between age and effectiveness of mitigating interventions, we could see that the youngest victims were only a few months of age. This was a horrible insight, but it also informed our research, directing us to undertake a very different set of conversations with our client to introduce a fresh emphasis on the special needs of the youngest victims.
Context feeds human data stories
While centering research around data and human interaction, pay special attention to what is missing. While this might not be particularly relevant for the analytics engagement at hand, it is invaluable when defining a future data strategy which takes into account the broader picture.
By the GIG’s first evening, the faculty had already conveyed how impressed they were with our range of questions. Through this positive engagement, we learned about their wider ongoing efforts, notifying us of fresh data that could be collected. This broader overview of the organisation’s workflow helped us prepare to develop a strategy that would make full use of these new data streams once ready, while still not being overly reliant on them.
Drive action through adoption
The most advanced analytics outcomes are worthless if they are not adopted, or do not deliver meaningful outcomes. As advanced analytics and AI becomes more widely understood by the business world, human factors such as model adoption will become the key obstacles — while also providing opportunities.
It is therefore vital to build models with the user’s context and needs — their human story — to the front of mind. The work of an outcome-focused analytics team does not finish once a model is delivered — it must be proved to work, adopted by the user and optimised depending on how it is deployed.
For our project, we created a concept that illustrated the future state of a case worker’s journey, providing a range of interventions to help them perform best on behalf of recovering victims. Understanding that the users would likely not be proficient with data tools, we designed many of these interventions to be low-tech and paper based. Crucially, this was a feasible concept, grounded in reality and immediately executable, while also highlighting future potential to optimise and improve results.
At the GIG’s conclusion, our team were immensely proud of our work. We left not only happy with the potential impact of our newly-generated insights, but excited to have completed a fantastic case study in how bringing an interdisciplinary approach to problem-solving can significantly enhance a project’s effectiveness, without draining resource. Most importantly, the nature of the challenge means that every team involved deserves credit for their participation in a difficult and often emotional subject.