Accelerating Digital Innovation Adoption in the Public Sector: Moving from Prototyping to Adoption
Pulse Lab Jakarta (PLJ) has led the way in developing data analytics and visualisation prototypes for humanitarian and development fields since 2012. By creating advanced prototypes of interactive visualisation tools, like Cyclomon, Hazegazer, MIND (Managing Information on Natural Disasters), DEMAND (Disaster Emergencies Mobile Analysis Data — Palu population movement), and PNG earthquake, we have introduced the art of the possible when using unconventional data and novel approaches to technical and non-technical audiences.
While it’s generally rare for prototypes to move into the production phase, we have successfully developed solutions like VAMPIRE (Vulnerability Analysis Monitoring Platform for Impact of Regional Events) and the Digital Diplomacy Tool that have transitioned into operational phases. However, the sustainability of these platforms relies heavily on the technical capacity of the parties that take over their management. Recognising this, we prioritised sustainability in our subsequent solutions to ensure our partners have the technical capacity to utilise, maintain, and even improve the customised tools we’ve initiated. This approach not only enhances the long-term impact of our work but also promotes collaboration and knowledge-sharing among our partners.
Recently, we shared insights into the development process for MEDIAN, a data analytics and visualisation platform we built that focuses on micro, small, and medium enterprises (MSMEs). The platform utilises a combination of human involvement and automatic techniques to facilitate the analytical pipeline from raw data to policy insights. It serves as a foundation of a home for MSME data that will continue to grow based on user needs in the future. We successfully handed MEDIAN over to Bappenas at the end of 2022.
In this article, we will explore the key features that made MEDIAN a valuable resource for data analysts and decision-makers, the unique characteristics distinguishing it from our previous platforms and the lessons we learned.
An Overview of MEDIAN’s Features
MEDIAN has three main components: Quick Search, Variable Interaction, and Information Catalogue. Quick Search is an essential feature that enables users to filter data according to predetermined variables. The Variables Interaction feature allows users to identify relational patterns. Lastly, the Information Catalogue is a knowledge repository.
One of the critical features of MEDIAN is its ability to display correlations using scatterplots to generate policy-relevant insights. This feature can be found in the Variable Interaction menu. By presenting the data on a two-dimensional plane, the scatter plot helps users quickly identify any outliers, trends, or correlations within the data. As a result, it makes it easier to pinpoint areas that require further investigation or action and to make informed decisions based on the insights gained from the analysis. This functionality lets users understand the data better and draw more nuanced insights.
An example of this is obtaining insights from correlation patterns. Although correlation does not imply causation, it can provide valuable analysis to make sense of development progress when adequately designed coupled with suitable interpretation. For instance, the scatter plot displayed in Figure 1 demonstrates the correlation between internet coverage and internet usage by enterprises at the district level. However, the plot reveals a rather complex pattern of correlation. Upon closer examination, it becomes apparent that few enterprises use the internet in areas with low internet coverage, potentially due to inadequate infrastructure that hinders MSMEs from going digital. Interestingly, the figure also shows that most enterprises still have low internet usage, even in high-internet coverage areas. Nevertheless, a closer analysis reveals a noteworthy distinction between enterprises in kabupaten and kota, with the latter exhibiting higher average values. This finding highlights the importance of policies that promote digitalization to support MSMEs in diverse geographic contexts.
Another benefit of using a scatter plot is that it enables users to quickly identify anomalies in the data. For instance, when comparing areas with similar levels of internet coverage, if one district has an unusually high percentage of enterprises using the internet compared to its peers, this may indicate either inaccurate data or an actual deviation that requires further investigation to determine why. To enhance the user experience, we added a functionality that allows them to visually compare the values of different districts within one or more provinces and to analyse patterns across different enterprise scales (micro, small, medium, and large).
Another important feature is the “Download Data” option in the Information Catalogue menu. This feature offers high flexibility for further data analysis. Users can easily download clean data in CSV or Excel format that has already been aggregated at the district level to conduct further analysis, create tables for reporting and presentations or to make sense of the data in their preferred ways. This feature empowers users to use the data without spending much time on pre-processing, which usually takes a certain level of data-wrangling skills, while still ensuring data quality as experts have already handled the workload behind the scenes.
MEDIAN has a modular design to give it a longer life cycle. One of the benefits of using such a design is flexibility in customising the user interface to fit users’ changing needs and preferences while minimising the clutter and complexity of the interface. The platform has also taken an essential step towards inclusivity by incorporating a disability widget, making it more accessible to all people including those with disabilities by assisting them in quickly and efficiently navigating the platform. This ensures people with disabilities are not left behind when accessing MEDIAN’s valuable data and insights and this commitment to inclusivity sets a positive example for other organisations to create more accessible and inclusive digital spaces moving forward.
Unique Insights and Key Lessons
MEDIAN is the culmination of our trial-and-error efforts to accelerate digital innovation in the public sector. We discovered that our previous visualisation and analytical tools had short life cycles due to various factors, such as data becoming obsolete and insufficient technical capacity of handover partners. Therefore, the Lab developed a combination of strategies for MEDIAN to address these issues, making the platform more effective and sustainable in catalysing data innovation within the public sector.
These strategies involved close collaboration with our government counterpart as the user, user research, modular design, support in recruiting and sufficiently training human resources, and adaptation of the tool to their business process through a service blueprint. This approach has shown promising progress in integrating new methods and resources into our innovation projects. However, we recognise that the impact of these strategies may not always be immediate or straightforward, and that there is still room for improvement.
Organisations increasingly rely on data to inform their decisions, which is why having reliable, effective, and user-friendly platforms is crucial. However, creating and maintaining such platforms is challenging and requires ongoing efforts to ensure their sustainability and adoption. PLJ’s experience and strategies underscore the importance of close collaborations with users, service design, modular design, and capacity-building support to achieve this goal. As an innovation lab, we remain committed to accelerating digital innovation adoption in the public sector and exploring new possibilities to better serve our partners’ evolving needs.
PLJ Project Team Members: Desi Vicianna (Government Partnership Coordinator), Faizal Thamrin (Data Innovation and Policy Lead), Rajius Idzalika (Data Scientist), Annissa Zahara (Data Engineer), Robbi Nugraha (Web Application Engineer), Muhammad Rheza (Full Stack Engineer), Angga Gumilar (Programme Assistant), Swastika Exodian (Multimedia Associate), Lia Purnamasari (Design Researcher), Rizqi Ashfina (Social Systems Researcher)
Author: Rajius Idzalika (Data Scientist) with editorial support from Andini Kamayana (Communication Manager).
Pulse Lab Jakarta is grateful for the generous support from the Government of Australia
