Asia is driving the pace of urbanization globally. According to the United Nations Economic and Social Council for Asia and the Pacific, 17 of 28 of the world’s megacities are found in Asia-Pacific. By 2050, two-thirds of Asia will live in cities.
The public sector, which serves as the bedrock of a society, plays a gamut of roles from service provider and administrator to enabler and facilitator. Digital transformation in the public sector has led to increased citizen expectations. For example, the public sector is expected to not just facilitate and encourage innovation through the use of analytics in the private sector, but also find ways to deploy analytics across the public sector for better societal outcomes.
Governments and policy-makers need to think long-term, forecast trends and consider future scenarios that would impact the nation’s economy and citizenry. As forecasts stretch further into the future, how can the public sector ensure the accuracy of insights?
The power of data
In the process of engagement with citizens, the public sector creates records and captures data from various activities in society and the economy. Connecting data sets can allow the public sector to create new insights to understand present behaviors, but also combine present-day data with known growth drivers to better predict the future, and use data-driven models to inform future policies and decision-making.
With increased use of analytics for planning, there will be an appreciation that predictive analytics and forecasting necessarily encompass unknowns and uncertainty. To manage this, tools and techniques such as simulation explore scenarios not just for today, but in disparate environments of the future.
This provides immediate insights into many public sector planning questions, such as whether the resources required to provide public services in a city when its population doubles will translate to economies of scale or result in exponential complexity.
Cities, whether with an aging or growing population, have pressures to efficiently allocate resources for future childcare planning, health care and citizen retirement. External factors, and even nuances in demographics, economic development, and government schemes and policies can shift outcomes drastically.
For the sustainability of public resources and longevity of government programs, EY has constructed a long-term predictive analytical projection model that factors in major user behaviors and demands. This equips policy-makers with directional insight into how policy changes can benefit citizens to maximize the number of members that can be provided for in the long term.
Recognizing the challenges
Even with an operational model, there are still challenges to grapple with, such as data availability and quality, and the need for realistic assumptions and practical application of insights.
Data availability differs considerably across markets, but certainly more data is captured every day in every market. The upside to untapped data is clear -- sufficiently large data sets translate into the ability to conduct meaningful analysis.
Similarly, data quality may vary wildly depending on where, when, what and how data is collected. Issues around data quality can be dealt with using industry standard data-mining methodologies, and with increasing availability and abundance of data, it is progressively easier to obtain sufficient clean data for analysis.
Yet, it is not uncommon that not all data requested is available. Analytics is most valuable when used to provide direction for a better future, including embracing fundamental uncertainty in looking at yet-to-happen scenarios in the absence of data. To do so, it is necessary to make realistic assumptions to explore alternative scenarios and sensitivities, typically resulting in more flexible and robust models for the future that are thoroughly tested.
Further, government and public sector initiatives should always bear in mind the degree of practical application. For instance, a model that utilizes traffic conditions and travel speeds in Singapore might be more reliable on a daily basis than an equivalent model developed for Jakarta or Manila.
Notwithstanding that concepts, such as average travel speed may be the same, the daily realities in the latter markets may present dramatic fluctuations that make planning for average responses less relevant.
Getting it right the first time
For many of the key public services, such as health care, social services and education, governments cannot take a trial and error approach due to the repercussions of the intangible costs. Unlike the private sector, governments typically do not have the luxury to experiment, test and “fail fast” to drive innovation.
Policy changes therefore should be well-considered and prepared to maximize success. Take for example how the Australian government engaged EY to deploy advanced data and analytics to help investigate, analyze and predict outcomes for child protection services. Organizations were then able to identify those in greatest need and make proactive interventions to help decrease generational poverty and ultimately achieve better life outcomes for the children involved.
In another example, EY constructed a resource simulation and incident response model for an Asian emergency services provider. This “crystal ball”’ provided insight into the future, based on actual incident response performance. It enabled the service provider to optimize placement of resources, such as ambulances to continually meet calls for assistance as demand shifted across the week, and predict the required resources to meet future performance levels. This was achieved without the need for real-life experimentation, which is simply not an option when saving lives is the daily mission.
Clearly, as part of the public sector’s arsenal of digital tools, analytics plays an instrumental role in robust policy-making and planning, enabling the optimization and simulation of services, processes and systems before policies are brought to reality.
The authors are Andre Toh and Wouter F van Groenestijn, partner and executive director, Transaction Advisory Services at Ernst & Young Solutions LLP, respectively. The views reflected in this article are the views of the authors and do not necessarily reflect the views of the global EY organization or its member firms.