How big data can help governments understand citizens better

A city can have millions of data points and still fail to understand its people. 

This is one of the lessons that stayed with me from my time leading Jakarta Smart City. 

Cities generate enormous amounts of data every day through mobility, public services, complaints, transactions, sensors, and digital interactions. Yet having more data does not automatically make a city smarter. 

The question I increasingly ask is much simpler: Does this data help the government understand people better? 

For years, digital transformation has often been measured by the number of apps, dashboards, or datasets. But citizens rarely care about the technology behind the government. They care whether a problem is solved, whether a service is easier to access, and whether the government listens when they speak. 

That distinction matters. 

From collecting data to listening 

Almost every urban activity now leaves a digital signal. People move around the city, use transport, access services, and report problems. Sensors measure traffic, weather, and water levels. But these signals are often fragmented across institutions. One agency sees mobility, another complaint, another health, and another infrastructure.

When the data remains separated, the government sees pieces of the city, but not necessarily the citizens. 

Jakarta’s citizen relationship management (CRM) system offered us practical lessons. Citizen complaints coming through 13 official channels could include text, photographs, videos, and location information.  

Rather than seeing each complaint only as a case to be closed, the data could be integrated, routed to the responsible agency, monitored, and analysed. 

A complaint about road, waste, or flooding is therefore more than a complaint. It is an urban signal. 

When many such signals are considered together, they can tell the government something about where problems occur, what people experience, and where intervention may be needed. 

But there is an important caution here. A pattern in data is not automatically a human need. We still need context. We need to ask what people are doing, why they may be doing it, and what they actually need. 

This is where data must meet human judgement. 

Designing around people, not institutions 

The same thinking influenced JAKI, Jakarta’s super app platform. JAKI was designed to reduce fragmentation by bringing information, interaction, and transactions into a more integrated citizen experience.

This taught me another lesson: citizens should not need to understand the structure of government to receive a government service. 

Government may be organised into agencies, departments, and administrative responsibilities.

People do not experience life that way. They simply need to get to work, find healthcare, report a flooded street, or access a public service. 

Technology should reduce that complexity, not transfer it to citizens. 

This is also why human-computer interaction matters in the digital government. A technically sophisticated platform has limited value if people find it difficult to use, cannot understand it, or do not trust it. 

And trust is especially important as governments make greater use of big data and artificial intelligence (AI).

Citizen analytics should help us understand people, not surveil them. Privacy, security, transparency, and accountability cannot be added after a system is built. They are part of the public value we are trying to create. 

Keeping humans in the loop 

AI gives governments an opportunity to move from being reactive to responsive, and eventually towards more predictive and proactive services. Real-time data can tell us what is happening. Predictive analytics can help us consider what may happen next. AI, digital twins and simulation may help us intervene earlier. 

But I do not believe the future should be an automated government. 

Public decisions involve context, fairness, empathy, ethics, and accountability. Algorithms can identify patterns that humans may not see, but people must remain responsible for deciding what those patterns mean and what should be done. 

The model should therefore not simply be handing data to AI to make a decision. We need humans in the loop. 

The same principle applies to public communication. 

In my current role in communications and public relations, I increasingly see communication not simply as delivering government information. It should be a two-way bridge: listening, responding, explaining, and inviting participation. 

Technology gives us new ways to listen at scale. But listening has little value unless the government is willing to respond. 

The Jakarta experience has left me with five shifts: from data to citizens, technology to problems, silos to integration, assumptions to evidence, and applications to impact. 

For governments everywhere, perhaps the starting question should not be, “How much data do we have?” 

It should be, “What do we understand about the people we serve that we did not understand before?” 

Big data gives us signals. Analytics gives us insight. AI can help us anticipate. But people give technology its purpose. 

The smartest government is not the one with the most data. It is the one that understands its people best—and uses that understanding to make their everyday lives better.

The author is Head of the Bureau of Communications and Public Relations at Indonesia’s Ministry of Primary and Secondary Education. He previously served as Head of the Ministry’s Centre for Data and Information Technology and led Jakarta Smart City. He holds a DPhil in Cyber Security from the University of Oxford.