Pulse Lab Jakarta
Published in

Pulse Lab Jakarta

Towards Collaborative and Interconnected Innovations: Development as an Ecosystem

Since 2020, Pulse Lab Jakarta (PLJ) has looked to evolve its role as an analytic partnerships accelerator. Transitioning our identity from a “big data innovation lab”, the challenge has been to be more impactful, by progressing beyond methodological contributions to building partnerships that impact on the data ecosystem in Indonesia. This repositioning has led us to further explore Indonesia’s data ecosystem, particularly as it relates to different development sectors.

The COVID-19 crisis has emphasized the need for collaborative and interconnected innovations. It also provided an opportunity for us to observe data flows between local and national governments, and identify underlying issues in the data ecosystem. These issues include lack of consistency in applying data standards, lack of data partnership agreements, and poorly integrated data flows across ministries engaged in the response. Separately, a number of international partners and government entities have developed systems to address particular aspects, but the lack of interlinkages between emerging and existing systems often leads to inefficiencies.

The existing dynamics call to mind an article published by Adam Brandenburg in Harvard Business Review, titled: “To change the way we think, change the way we look”. It highlights the manner in which many important innovations came about by changing the way innovators looked at “the familiar in an unfamiliar way”. At the Lab, we’ve been reflecting on the article in light of our emerging identity as an analytic partnerships accelerator. Here, I would like to share some of our key impressions that we hope will support a broader conversation across development actors in looking anew at development as an ecosystem.

The Definition of Ecosystem

The word ecosystem is far from new. It is commonly defined in terms of science to describe the interaction between living and non-living components within an environment or system. Borrowing attributes such as adaptive, collaborative and interconnected, we often talk about “data ecosystems” and “innovation ecosystems’’ within the development space, but we have rarely looked at development itself as an ecosystem — or as an organic generative system with core, extended and external components.

Since our establishment in 2012, our work has been on harnessing data innovations to support development practices and humanitarian action in Indonesia and the Asia Pacific region. Working with different stakeholders and development partners across various sectors, the strength or weakness of data flows becomes a proxy indicator of the health of these relationships.

Nonetheless, development as a process of change is about innovating, which means practices undertaken in any given context should also be seen anew. With the advent of digital technologies and surge in data innovations, we are seeing a push to apply data innovations, as well as concepts that have long been applied in the data science field towards the development sector. By fundamentally looking and perceiving “development” as an ecosystem, it is fair to say that the strength or weakness of an ecosystem is based on the level of functionality and interlinkages between components. In other words, the better the level of functionality and interlinkages, the higher the likelihood of survival, consolidation and growth.

Application in the World of “Development”

As we mentioned, the phrase “innovation ecosystem” is often used, but in its breadth and entirety this is perhaps much too broad and complex to analyse. Furthermore, many components of an innovation ecosystem are likely beyond the mandate of international development programmes that are seeking to affect positive change and improve the welfare of citizens. As such, we suggest looking more specifically (in the case of PLJ) at what we could term “Indonesia’s development ecosystem”. This comprises meshed systems with multiple actors, including international agencies and programmes, as well as national institutions.

In Indonesia, there are many international agencies, multilateral and bilateral, with each intent on playing a role impacting on socio-political and bureaucratic processes. At the same time, there are a range of efforts also being undertaken by civil society organisations and the private sector. In essence, they are all components within Indonesia’s development system, and should be incorporated in the overall configuration.

We can then map the entities intensively engaged in any given development sector as components in subsets of the overall development ecosystem, who are engaged in efforts of producing or supporting development policies. Referring to the literature on digital or data ecosystems, a useful reference is IBM’s depiction of an analytics ecosystem (seen below). This is perhaps most relevant to a development ecosystem in terms of linking and aligning components to support improved analytics on policies leading to better development outcomes.

IBM’s depiction of an analytics ecosystem

Core, Extended and External Players

We can categorize components of any development ecosystem into three main layers or groupings. Essentially, we have core components of the ecosystem which are key institutions, systems and decision makers establishing the policies and controlling resources. These components can be likened to keystone species, where the whole system collapses or becomes dysfunctional if these components are no longer effective. For instance, many leading development organisations in the disaster risk reduction space face great difficulties in collecting and collating data and information from various agencies, hampering their own effectiveness for example in interacting with Indonesia’s National Disaster Management Agency (BNPB) as a keystone species in this space. Extended players/institutions are part of the second layer, which include private sector and civil society organisations who are closely linked with core components. Then we have a third layer of external players and systems, including regulators, data providers and consumers.

Ecosystems function and are strong or weak based on relationships between components/institutions. As mentioned before, this can be observed from the flow and utilisation of data and information, whereby a stronger flow points to a stronger relationship. So in the case of International agencies and programmes, one could say a number of them are “extended players” in the ecosystem, but are functioning and have high information flows with core institutions. At the same time, others are in the periphery of the ecosystem as external components, and struggle to impact on core decision making processes, again based on the flow and regularity of information and data supplied to be absorbed by core institutions.

From these groupings, we can also see how relational trends are progressing in terms of where components are getting their information and data from, particularly in this era of digitalisation transformation. If institutions are not able to change the way they collect, analyse and project their inputs, they may soon be pushed to the periphery or become irrelevant to the ecosystem. What we’ve observed is that innovations from international development agencies are often not fully adopted or incorporated by core state actors, as these recipients often do not commit the resources to facilitate and sustain data flows and exchanges. Development as we see it being undertaken by global, regional and national bodies when competing for influence and resources can often exacerbate the poor health of national development ecosystems.

The Importance of Development Ecosystems Moving Forward

With the development sector, different policy domains have already existed, but traditional boundaries have started to fall as increasing complexity of development issues can no longer be addressed through mono-sectoral approaches. This is why we need to look at ways to better improve the effectiveness of our institutions and interventions.

For Pulse Lab Jakarta, we have seen that our impact is limited if we only work in the solution space (providing prototypes without strong partnerships needed to adopt and adapt our innovations). It became clear that our data innovations required partnerships across a range of actors and institutions. In PLJ’s early days, we were close to the periphery of the development ecosystem. This is when we focused on introducing and demonstrating novel approaches and methodologies, but the core components were not necessarily utilising our services. Today, we’ve started to move towards the extended sphere, and hope to further influence the way the core components operate and interact with each other.

Continuing with the example of the disaster risk reduction space, with so much investment into disaster risk reduction systems, it is unfortunate that the sum of the whole is not greater than the sum of the parts. This is where we started looking at the various institutions working in this particular space. It became clear through this process that whilst many institutions were delivering on their individual mandates — resulting in a plethora of platforms and tools — collaboration was often not strong enough and interconnectivity between platforms was weak to non-existent. Common protocols, standards and other governance mechanisms were not in place to achieve cohesion between efforts, and therefore competing interests and redundancy actually seem to hinder this sector from advancing.

Another way to measure and improve the health of the ecosystem is to improve data flows within and across agencies, as well as improve behaviours (including better collaboration and coordination). This is directly linked to the data strategy of the UN’s Secretary General which is focused on “building a whole-of-UN ecosystem that unlocks our full data potential for better decisions and stronger support to people and the planet”. At its core, this data strategy is reliant on the health not only of individual components, but also in terms of the strength of interconnections and collaboration, measured through the level of shared data and knowledge between these components.

We note that many important components in the national level development ecosystems are part of regional and global development ecosystems, and provide opportunities to interlink between different levels. This emphasizes the need for learning and feedback loops and mechanisms to ensure global agendas can be effectively implemented on the ground, whilst also providing insights to adapt and adjust regional and global strategies.

Are you working at a global level, regional or national level? What are your thoughts on the health of relationships and data flows between key components of the development ecosystem you work in? We’d love to hear your thoughts, email us at plj@un.or.id

Author: Petrarca Karetji (Head of Pulse Lab Jakarta)

With editorial support from Dwayne Carruthers (Communication Manager)

Pulse Lab Jakarta is grateful for the generous support from the Government of Australia

--

--

--

Accelerating Analytic Partnerships for Development and Humanitarian Action

Recommended from Medium

Starbucks Rewards App Offer Optimization — Which offer type suits you the best?

Alteryx: Self-Service Data Analytics tool

The emergence of data science in insurance

Data & Analytics Professionals Must Adopt A Discipline Of Testing… Each Other

Bringing Computer Vision Datasets to a Single Format: Step towards Consistency

Streamlining Health Insurance & Claims Processing : Tariff Digitization and Standardization(Part 2)

Data visualization of the Impact of flood and its effect on the Agricultural sector in Ibadan

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Pulse Lab Jakarta

Pulse Lab Jakarta

Accelerating Analytic Partnerships for Development and Humanitarian Action

More from Medium

Customer personas in the Mad Men era

Analyzing Airport Delays using Tableau

How Much Do Data Scientists Make In 2022?

Customer Segmentation for Arvato Financial Services