December 17, 2021
The next step in community-centric service delivery
Humanity is entering an era of unprecedented access to data on every facet of society. Two fundamental forces propel this change: (1) a remarkable increase in the volume and specificity of data, fueled by massive numbers of IoT-enabled devices coming online and high saturation of smartphone use; and (2) increasingly complex and sophisticated machine learning algorithms that allow swifter data analysis than ever before. These changes can be leveraged in two equally important ways. First, from a broad, generalist perspective, new data sources can help develop knowledge on and responses to issues that affect the future of humanity as a whole. This is the case for climate change, global trade, and, more evident now than ever, global health readiness. Second, more sophisticated, frequent, and granular data collection will allow governments to implement policies that adapt accurately to local cultures and geographies. These changes are not just in magnitude but rather the manner in which data is processed has fundamentally changed. Previously, technology was a tool to augment human capacity and efficiency without losing sight of what exactly the technology was doing. Now, humans will be increasingly separated from the intricate analysis process and mainly focus on obtaining new, improved data sets. This difference is challenging governments to completely rethink the way services are delivered to specific populations, not only from a practical perspective but from a legal and ethical one as well. Citizens interact with government-provided services on a daily basis in many areas where data-driven policy can significantly improve delivery: healthcare, education, waste management, transportation, utilities, land administration, and citizen security, for example.
The creation of strategies that tailor service delivery to a target group is often desired but prohibitively expensive and time-consuming. Only very large or very wealthy cities and countries have the necessary resources. Still, with recent advances in data collection and machine learning, significantly improving the portion of a population included in government service delivery is a short-term possibility. These advances will not only enhance the targeting of service delivery but will also improve the processes themselves, creating all sorts of efficiencies within government agencies and freeing up resources for reinvestment and new projects.
The only way to guarantee sustainable modernization of service delivery is to emphasize bottom-up approaches that take into account the different physical circumstances of each population center as well as their particular level of technological education, their openness to data collection and sharing, and their specific policy priorities. Sustainability comes from the disciplined deployment of new technology that boosts data collection and analysis at a local level. If these new tools remain high-end experiments only applied in select situations, then the gap in service quality between communities, even within a country, will continue to be an issue. Federal focus should be less on the specifics of deployment and more on governance, accountability, education, and funding. The future of digital government won’t arrive with the flip of a switch, but through a comprehensive roadmap that takes into account each area’s starting point, resources, and objectives.
The potential benefits of improved data collection and analysis are straightforward and powerful. While all public utilities tend to be essential, a useful example is access to clean drinking water, which is a challenge in developing countries and even in some regions of advanced economies. Community-specific data in real-time would allow better measurement of communities with poor potable water coverage and service history, more accurate supervision of maintenance and repair contractors, real-time analysis of budget inefficiencies, better engagement with concerned citizens, enhanced procurement processes to access more innovative solutions, and refined contingency plans, all based on precise local data. Service delivery at a community level has usually relied heavily on broader service models that are not finely tailored to each instance, and even those data sets require too much human-intensive activity to keep up to date.
The benefits of using targeted data to improve service delivery come with a fair degree of complexity and risk. Any given population generates data in a disproportionate way. For example, children, the elderly, and people living in poverty tend to interact less with the technology that collects data for decision-making today. In the case of clean drinking water, that asymmetry would mean basing policies for expanding coverage and improving existing service on potentially inaccurate data, or, even worse, data that excludes important sectors of the population from one of the most basic standards of living. Just as in the service delivery itself, these new forms of interaction with citizens come with a healthy number of legal and ethical responsibilities. Governments must design frameworks that allow for enhanced quality of service and accountability. At the same time, citizens should also be held accountable for their interactions so as to avoid incentives for efforts that seek to discredit governments without proper process and credibility. These new frameworks should also cover data rights and digital identification.
Several jurisdictions around the world have experimented with crowdsourcing service delivery data by measuring very specific problems. One example is easy-to-use mobile apps to identify and report road issues. It’s a straightforward, useful way to identify priorities for city managers. However, problems have come up precisely because of asymmetrical reports across neighborhoods. For example, wealthier areas with younger populations have more citizens with cars and smartphones, so they create more data more frequently. The algorithm then generates maintenance orders for the appropriate department based on that data, leading to faster repairs in wealthy neighborhoods than low-income ones. The complexity that city employees face is threefold: the lopsided maintenance efficiency itself; the reaction of citizens in areas with subpar results; and the question of modifying collected data to improve the accuracy of service delivery. The discussion of this last issue is not only practical but ethical. Is the crowdsourced data really objective if it’s modified? Is it objective if it is unmodified? Who decides how to modify it, and what criteria will guide them? How does one guarantee that modifications are fair?
Refined data collection and the use of machine learning algorithms will not only allow exponential improvement in the quality of government services, but also in citizen engagement. Just as services can benefit from being more targeted and personalized, so too can the interactions between local governments and the citizens they serve. In this, the potential benefits of more granular data use fall into two categories. On one hand, it allows improvements in how citizens participate by customizing their service requests, accessing information to make better day-to-day decisions, and having access to a broader range of transaction options. On the other hand, it creates the potential for greater government accountability through citizen action with more accessible data that allows for the discovery of fraud and corruption.
Data-driven, community-centric service delivery will require the following:
- Efficient, community-centric data collection that takes into account the specific characteristics of a given population;
- Adequate processes to evaluate the data being collected, decide whether modifications are necessary, and under what criteria they will be made;
- Targeted, responsive citizen engagement processes allowing for improved quality of service and increased accountability; and
- Legal and ethical frameworks that protect the citizen from misuse of their data and the government from artificial campaigns of discredit.
Communities are evolving; as is the data they generate, so government service delivery must evolve as well. Richer data comes with a wide array of opportunities and a proportionate number of risks, and finding the right balance could lead to a period of unprecedented range and quality of public services.