We then built a command centre and monitoring system capable of monitoring all complaints on a big-screen in real-time and evaluated the performance of government officials by the time it took for them to complete or handle citizen requests.
Lastly, we implemented and developed a big data analytics platform to support our descriptive, predictive, and prescriptive analytics efforts.
As of 2019, our solutions, services and technologies encompass: A command centre for real-time operation monitoring; a citizen relationship management system; 24 hour live CCTV; an open data portal; an open Jakarta financial/budgeting allocation website; a flood warning & monitoring system; a food commodities live monitoring portal; API Jakarta, that encourages developers to build applications; a geospatial regional development planning website; cross-domains consultation for various government agencies; and exploratory research with various institutions.
At Jakarta Smart City, the role of the analytics team is both straightforward and strategic.
Ultimately, we are here ‘to make better decisions, faster’. When you consider that numerous government officials come from diverse and non-technical backgrounds, technology, modelling, and complex analytics techniques are secondary. Hence, the key emphasis should always be on producing concrete recommendations that have a direct impact on the ground.
At Jakarta Smart City, decision making ranges from operational and day-to-day decisions all the way through to highly-strategic decisions with wide-ranging implications.
An example of an operational decision is how to react in real-time to complaints related to the accumulation of waste in certain regions. Thanks to our smart infrastructure, on an hourly basis we can assist in troubleshooting the root-cause (e.g. the road is too narrow and hence garbage truck can’t enter) and request a field operation team to resolve the issue.
A strategic decision might involve analysing the impact of integrated multi-modal transportation (e.g. Mass Rapid Transport (MRT), light rail, public buses, ride-hailing services, bicycles) and trying to answer key questions relating to their mid to long-term integration: Are there any redundant routes? Can we make cost and prices more efficient? Can we predict citizens’ future strategic meeting points? These are all questions data helps us to answer.
Making implementation a success
Like any other city or organisation, there are always challenges in driving and implementing analytics – and like in industry, the challenges are cultural, structural and technological.
Firstly, from a cultural perspective, it is not easy to drive a data-driven culture. There are always employees or departments that prefer to rely on experience and intuition when making decisions. It takes time to encourage them to explore the data, conduct initial analyses, and make basic recommendation off the back of them.
Typically, I will identify and engage with the teams and departments that are more open and that have more available data. Once benefits are demonstrated, it’s easier for me to get buy-in from others.
From a structural perspective, there is no direct authority from the analytics team towards other departments. Fundamentally, we are not in a position to give direct commands.
However, that is both a challenge and an opportunity. The art of persuasion and relationship-building are highly critical in this phase and it is important to build win-win/collaborative working relationships with other departments. Even if part of the job of the analytics team is to monitor others’ performance, it is important to execute it in a constructive manner.
From a technological perspective, data cleanliness and accessibility is our greatest challenge.
Unsurprisingly, data is almost always dirty and stored in silos. In order to tackle this, I have formulated a short, mid, and long term plan. In the short term, we will use interim techniques to clean the data and consolidate it into a staging layer.
In the long term, we are planning to introduce a Jakarta-scale data-hub and platform that will consolidate data from numerous agencies. To achieve this long-term goal, every legal, structural, resource and technological requirement must be identified.
Looking into the future, we are ultimately striving towards smart city 4.0. Again, the exact definition of this is debatable. But at the very least, we want to arrive at a situation where our citizens are not just consuming information or giving complaints.>
We want our citizens to bring more value and contribute to the functioning of our city. In essence, we want our city to be “co-creators?”, working together with the government, enterprises, universities and research institutions. We are currently putting a lot of effort in to establishing a partnership framework and executing it in a rigorous manner, especially potential partnerships with start-ups.
Secondly, we want to properly harness the unrivalled power of AI. We have explored various AI solutions from both big global technology companies and local start-ups. For us, the promising use cases for AI are facial recognition and high-fidelity video analytics for transportation.
When it comes to these technologies, we are still in the exploratory and evaluation phase. The key is to not simply be attracted to the latest technology: We want to ensure that it will integrate with our existing infrastructure and bring real benefits to the citizens of Jakarta.