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Building an Advanced Analytics and Data Science Team in Government Context Building an Advanced Analytics and Data Science Team in Government Context
It is widely known that data is highly valuable and crucial for the decision making process. At the same time, handling... Building an Advanced Analytics and Data Science Team in Government Context

It is widely known that data is highly valuable and crucial for the decision making process. At the same time, handling and deriving values from data requires a deep skill-set and expertise. Digital talent has been scarce, as always. This condition is even more challenging in the government context, where attracting tech talent is not as straightforward as prominent technology companies. This article will demonstrate several concrete strategies and initiatives from Jakarta Smart City to attract and nurture digital talents, including university students and fresh graduates. Lastly, I personally have a deep passion for education (e.g. giving lectures, advising curriculum updates, recommending education policies). I really wish Jakarta Smart City could produce high-caliber data talents to support exciting digital development in Indonesia and Asia region when building a data science team.

[ODSC APAC 2020 Schedule Released! See What to Expect Here]

Data Science Skills from Udacity Blog

Real Needs for Building a Data Science Team

In the government context, I have been seeing a great irony. On one hand, the government is handling numerous strategic domains with possible tremendous impacts on society. On the other hand, the limitations from processes (e.g. bureaucracy) and talent management are really significant as well. Based on my experience in government in the last several years, I realize one basic fact: a simple (from an industry perspective) concrete innovation can be seen as extraordinary and a total game-changer in the government context. More importantly, those “simple” initiatives can transform the lives of millions of people at the city-level or country-level. From my own experience, I am referring to several data-related initiatives such as: citizen 360-degree demography analysis (e.g. for subsidy distribution), image recognition for CCTV data, logistic optimization for medicine/social-aid distribution, causal impact analysis for policy evaluation, predictive model for COVID-19/flood/traffic jam.

Data Products & Services at Jakarta Smart City

Strategy and Practical Steps

In a nutshell, our strategy is straightforward, which is collaborating with as many partners as possible with a clear collaboration framework. As a government agency, we have the luxury of exposure, authority, and connection to “unlimited” institutions/organizations at national and international levels. These organizations can be other government agencies, international organizations, industry enterprises, universities, and research institutions. We have formulated and executed the practical initiatives based on our organization’s maturity level. Here are several key examples of concrete execution:

building a data science team

Jakarta Smart City Development and Collaboration Framework

In our early phase of building a data science team, there were so many rigid administrative requirements in hiring tech talents. The obstacles varied from the linearity requirement of professional experiences, a formal detailed recommendation from past 3 companies, and very selected options of university background. With this challenging limitation, practically we could only hire fresh graduates in most cases. However, this situation was a blessing in disguise. We were able to organize an exciting data hackathon for university students, establish robust partnership relations with key universities, and formulate a clear 1-2 months learning path for our fresh graduate team members. I can proudly share that within this 1-2 months period, our fresh graduate team members were able to develop a strategic management dashboard, learn fundamental statistics, understand basic data model development & deployment and present in a meeting with the head of agencies level audiences.

In the last 1-2 years, our situation is a lot better. At this point, we have been collaborating with several tech MNCs to upskill our digital knowledge, initiating data science trainee programs that attract professional candidates from overseas, contributing & exchanging ideas with international caliber researchers in various global conferences, and sharing our digital knowledge to other national ministries/institutions. Specific to my data division, I have formulated 1-2 year learning curriculum for our team members and ensured consistent weekly knowledge sharing for our internal data team. By having these small progressive steps, I have been witnessing significant improvement of our skill-sets/capabilities. As always, building and nurturing high performing advanced analytics and data science teams are challenging. Nevertheless, it is always encouraging and rewarding to reflect back on the impacts and progress that we have been contributing to our fellow citizens in the Jakarta context.

building a data science team

Internal Sharing Session Material about Clustering Techniques

Juan is a speaker for ODSC APAC 2020! Be sure to check out his talk, “Journey of Jakarta Smart City from Data Analytics Perspective: Building Advanced Analytics & Data Science Capabilities,” there and learn more about building a data science team.

In this session, we will learn from the journey and case study from Jakarta Smart City. Advanced analytics and data science techniques have been leveraged for video analytics (object detection), predictive modeling (COVID-19, transportation, flood, citizen aspiration), and clustering. Beyond the core technical modeling and execution, the speaker will elaborate on the digital talent management and cross-agencies collaboration as well. There will be well-rounded elaboration from technical, organizational, strategy, and cultural perspectives in driving analytics initiatives in the Jakarta context.

 


About the author/ODSC speaker:

Juan Kanggrawan is the current Head of Data Analytics at Jakarta Smart City. His key role is to fully utilize data to formulate public policy and to improve the quality of public services. Juan is currently working on several city-scale strategic analytics initiatives. He is actively analyzing complex, diverse and exciting urban data on a daily basis: citizen complaint/aspiration, transportation/mobility, health (COVID-19), CCTV, Open Data, weather-flood-river bank, subsidy utilization, food commodities price elasticity, etc. He is also developing and aligning strategic partnership framework between Jakarta Smart City with other government agencies, business enterprises, research agencies, and universities.

ODSC Community

ODSC Community

The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

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