Angga Gumilar joined Pulse Lab Jakarta in 2018 as a programme assistant, and has been supporting the Lab’s analytic partnerships and data innovation projects for policy making alongside our government counterparts. He recently participated in a series of training on policy-related project management which was facilitated by Policy Lab Indonesia, and in this piece shares his takeaways and looks at how they intersect with his work at the Lab.
Since the beginning of my assignment with Pulse Lab Jakarta in 2018, project management has been central to my work. I have been involved in several data innovation projects for policy making, many of which are carried out in collaboration with our government partners. Despite the many experiential learnings that I take from each project, as a continuous learner, I always feel the need to further strengthen my knowledge and capacity. Focused on policy development and project management, I was thrilled to have been selected through a competitive process for participation in a series of training facilitated by Policy Lab Indonesia.
Throughout the training, I had an opportunity to have first-hand, in depth discussions with a range of policy practitioners and professionals working across the public and development sectors in Indonesia. The sessions were held twice weekly over a 4-week period, and due to the COVID-19 situation, we all convened virtually. The training modules were delivered in a structured manner, touching on some of the key components of understanding policy problems, designing activities, budgeting, implementation, monitoring and evaluation, and measuring impact. Going beyond theory, the participants were tasked with developing their own policy project proposals based on the nature of their respective work and then share some of the key learnings and challenges they encountered along the way. Following the Chatham House Rule agreed for the training, I will share my own takeaways and discuss some of the intersections with my work at the Lab, particularly through the lens of the current pandemic.
The Dynamics and Complexity of Policy Issues
The COVID-19 crisis has given rise to many uncertainties, challenging the progress of ongoing projects and how we typically go about business. In some cases, resources have been repurposed to help the Government respond to the pandemic, for example in strengthening the country’s data ecosystem to enable better use of data in designing public policies. However, there are several underlying questions that are important to consider when defining what project management should entail in terms of policy development, such as: What is our existing knowledge on project management in the government and development sectors? Does it differ from traditional project management? And, what steps should we take to ensure implementation leads to actual policy practices?
Project management has evolved from a traditional practice dealing with physical development, construction and engineering into a complex socio-technical process that involves behavioural and organisational changes. The practices have advanced into all areas of government and international development in addressing emerging challenges such as poverty, environment degradation, education, corruption, healthcare systems and many other development issues.
The dynamics and complexity of policy issues require contextualised approaches, given their nature of often being difficult to define and involving many interdependencies, and even sometimes with no immediate solutions. Considering the many stakeholders and community groups that must be involved throughout the participatory process, this can make effective policy implementation even more challenging. Thus, project management needs to be conceptualised more broadly in order to incorporate a range of outputs with inherent complexity, and the assumptions that underpin project management practices need to be reimagined to include emergence, self-organisation and adaptation.
Problem Driven Iterative Adaptation
A problem-focused approach in project design and implementation is one of the factors that can contribute to a project’s success. In the beginning of the training, we were introduced to the Problem Driven Iterative Adaptation (PDIA) approach developed by the Center for International Development at Harvard University. It basically outlines a problem-driven, step-by-step approach to solving complex problems, for instance whole-of-society policy issues that governments and international development actors face from time to time.
PDIA rests in these four principles:
(i) Local Solutions for Local Problems
PDIA centers on allowing a problem to be understood within its own context. Assessing a problem and understanding the root causes (and sub causes) are very important in the initial steps towards applying PDIA in any policy project. We might want to avoid pushing for new ways of doing things or “innovating” prematurely, but instead reserve this thought process for the later stage, to allow a step by step iterative process to determine what holistic actions are needed to solve the problems. In other words, it is not advisable to use predetermined solutions without acknowledging the local context and dynamics.
Governments and international development practitioners sometimes err in implementing approaches that have worked well elsewhere, but might not be appropriate for the problems in question they’re attempting to address. Not only would this result in limited impact, but also it would affect the future sustainability of the desired outcomes. There are things to consider such as political and administrative capabilities, where do formal and informal authorisation of the projects come from, and other aspects of the organisations that need to be acknowledged. Thus a continuous engagement with those who are in the problem context is also very important, because they are typically well familiar with the dynamics of the problems and can help nurture an environment for locally sourced solutions to be adopted.
(ii) Pushing Problem-Driven Positive Deviance
Enabling environments to encourage experimentation and identification of potential positive deviants within and across stakeholders and organisations is an important part of problem solving. There are at least four areas of opportunity in finding possible solutions to any given complex problems that include:
- Existing Practice: Existing practice can be a source of opportunities to learn about what works, what does not work and why. There are many ways in which this can be done, for example during project evaluation, gap analysis, immersions, among others. It is the practice that people in the context know best and starting from where they are is a great source of empowerment.
- Latent Practice: Rapid-results type interventions where groups of people are given a challenge to solve a focal problem in a defined period with no new resources is an example. These can be incredibly motivating and empowering for local agents who get to see their own achievements in short periods. There are of course caveats.
- Positive Deviance: Positive deviance refers to the ideas that are already being acted upon under common circumstances and are resulting in far more positive change but are not the norm. Upon successful identification of such practices, it is crucial to diffuse the core principles of the positive deviants’ success.
- External Best Practices: External best practices can be a great source of learning, however these ideas need to be translated to the problem’s context. Again, finding several external best practices is a good way to go rather than settling for just one prematurely.
(iii) Try, Learn, Iterate and Adapt
Promoting active experiential (and experimental) learning with evidence-driven feedback built into regular management that allows for real-time adaptation.
(iv) Scale through Diffusion
Engaging multiple agents across sectors and organizations to ensure reforms are viable, legitimate and relevant.
Implications for Data Innovation Projects and Policy Making in Indonesia
Most of the practices in PDIA are not new for the Lab. We emphasize the experimentative and exploratory aspects in developing projects and benefit from continuous iterative processes. For example in our agriculture portfolio, the team from the Lab was able to identify positive deviants in rice farming in Indonesia through an explorative approach using traditional data and statistics combined with non traditional satellite imagery to fill information gaps attributed to traditional methods. This experiment has shown positive results but of course there is room for improvement, especially when dealing with complex policy problems. To have meaningful impact, the process must not stop there, but instead should continue beyond research into a more deliberate, integrated process with the people working in the context, in this case such as the Ministry of Agriculture.
A flawed understanding of data innovation tends to overlook the root of a problem, and instead jump directly into finding solutions or new ways of doing things. Put another way, the focus becomes more about using advanced analytics and big data without really taking into consideration the non technical aspects of the projects such as how legitimacy will be secured, where will the authorisation come from and issues around the sustainability of the outcomes. In Indonesia’s local context, the shortfall in government capabilities to deal with complex problems often makes it challenging to sustain the change. There is also a tendency to look outside for best practices to be applied in the local context. To achieve sustainability, this process alone is not enough. PDIA is a good approach that can be adopted to build state capability. By normalising the problem driven approach, possible local solutions for local problems may flourish.
Author: Angga Gumilar, Programme Assistant
Pulse Lab Jakarta is grateful for the generous support from the Government of Australia