Questions/Answers

The demand for big data analysis in the public sector has increased as we enter the era of Big Data, and the public perception towards data-based administration has changed. Each government institution has carried out a Big Data analysis project in different categories e.g. safety, transportation and logistics and social welfare in line with its own needs. As these organizations conducted their own analysis project, non-standardized analysis models were created which led to similar and overlapping analyses and a waste of budget. Against this backdrop, the Korean Ministry of the Interior and Safety (hereinafter known as ‘Ministry’) began its effort to create “the Big Data Standard Analysis Model“ by subject fields in 2016. ”The Big Data Standard Analysis Model” of the Ministry refers to a reference model with “work-flow”, “source program”, “sample data”, and “manual” made available to the public so that they can be reused. The standard structure of the model was created through standardizing the data for analysis, procedures, and methodology. The Ministry, after building the standard analysis model, innovated the overall process by utilizing the model in the Big Data Analytics Platform (also known as the Hyean System), thereby allowing local public servants to access and use the administrative network at all times and for non-experts to use the platform by offering training programmes. The Ministry has reduced the budget by preventing the creation of similar and overlapping analyses through building and spreading the standard analysis model in areas closely related to people’s lives and shortening the buildup period. The Ministry also ensured that non-experts could conveniently conduct analyses, thereby enhancing work efficiency of public servants and improving the quality of public service through utilizing the outcome of analyses.

The Ministry is providing data-based administrative services by using advanced technologies including Big Data and Artificial Intelligence (AI). Indeed, it has reduced the budget by preventing duplicate investments in similar or overlapping areas through building and spreading the standard analysis model. The Ministry eliminates blind spots of public security through analyzing surveillance cameras, and it secures the golden hour for ambulances by analyzing the population and traffic data. It also analyzes complaints made by citizens to preemptively find problems that may cause inconvenience and resolve them, thereby creating an “Intelligent Government“. In particular, the Ministry has contributed to enhancing the efficiency and productivity of public servants through sharing the reference model which includes the source program of the standard analysis model within the Hyean System.

The public Big Data standard analysis model is used as a reference model to provide data-based administrative services through analyzing Big Data instead of relying on experience or subjective judgement of public servants, and building a just society for all. The model is also used to analyze apartment maintenance fees so that illegal and unfair activities could be effectively exposed early on, and the pattern of those who illegally receive unemployment benefits could be analyzed by using the model so that the illegal recipients could be sufficiently detected, and the government budget can be used transparently. The model is also used to analyze the pattern of delinquent taxpayers so that the efficiency of tax administration could be enhanced. As these examples demonstrate, the standard analysis model facilitates the effective and responsible public administration in a sustainable manner.

“The Employment Mismatch Resolution Model” was created in 2017 to resolve social problems, such as difficulties in finding jobs and finding employees which may be caused by mismatched job information. In particular, the vulnerable group in society such as low-income citizens or women who rejoined the workforce after temporarily leaving work for childcare was identified through analyzing the national pension data, and job information was preemptively provided to support their social independence. The Jeollanam-do Province and the City of Gimhae that took part in building the model actively utilize this particular model to resolve problems caused by a mismatch in the job market by creating employment policies for the vulnerable and recommending jobs catered to the needs of job seekers. As of 2018, the “Single-Person Household Senior Welfare Model” is being built to estimate the welfare demand of the single-person senior household which is on a steady rise every year, and plan for the welfare service so that welfare blind spots of the senior citizens are not created. This model is a preemptive measure to ensure all regions are well prepared to provide welfare services to the single-person senior household in the future.

The electronic document-based administration has become a foundation for e-Government as the information system has expanded, but the decision-making process has not actively involved data analysis. The development and expansion of the standard analysis model to vitalize data-based administrative activities has become instrumental in resolving social problems and enhancing people’s lives through facilitating scientific decision making via Big Data analysis instead of intuition or experience of public servants. In addition, the model reduced government spending by preventing the creation of similar or overlapping analyses, which was an innate problem of the task-centered Big Data analysis project. In particular, the Ministry, after building the standard analysis model, embedded the model in the Hyean systemso that local public servants can access and use the administrative network at all times. Non-experts could also easily use the platform, thereby allowing local governments to carry out Big Data analysis projects with a small budget. In the case of the surveillance camera model built in 2016, the initial development cost was approximately 46 million won, but a local government (the City of Cheonan, 2017) used it as a reference model and built its own with 25 million won, thereby reducing the spending by a whopping 45%. This case clearly demonstrates that the standard model was utilized to reduce the execution period of projects and the needed manpower.

The existing system of Big Data analysis projects sporadically carried out by local governments became more efficient through utilizing the open source-based Big Data standard analysis model built as a result of this project. In particular, the fact that core items (e.g. utilized data and the method of analysis) needed to analyze Big Data regarding frequently used topics were standardized, it makes the model unique and authentic. When local governments plan and conduct Big Data analysis projects, they can reduce the relevant budget which comes from the tax thanks to the standard model. As of November 19, 2018, the number of technical support and consulting services provided to local governments regarding the standard analysis model amounts to 1,989, which has benefited 185 organizations.

The government selected three models based on the frequency of usage (Golden Hour, Civil Complaints and Surveillance Camera) as its priority tasks for government innovation in 2018, and it will spread the models nationwide by phases. In case of the golden hour model, it is being adopted by 5 regional governments, and the civil complaints model and surveillance camera model are being adopted by 17 cities and provinces as joint official duties. In particular, the models closely related to public safety (the golden hour and the surveillance camera model) are primarily adopted, and the emergency medical environment for the vulnerable group (the senior and the disabled) and the safety in their living environment have improved. The Ministry will continue to diversify and spread the standard analysis models, which are highly needed by the public and are capable of enhancing the living environment of the vulnerable.

A considerable amount of the national budget and human resources was put into building the Big Data standard analysis model. For example, approximately 4.35 billion won was spent for three years starting from 2016 to develop the standard analysis model, and 10 people from the central government, 58 people from local governments, 8 people from public organizations, and 84 experts from the private sector were involved in the project. Indeed, a significant amount of financial and human resources was injected during the development stage. Yet, once the model was fully developed between 2016 and 2017, the standard model was embedded in the Hyean system, and the technical support team of merely two employees has been operating and expanding the system. As of October 2018, the number of technical support regarding the standard analysis model has reached 1,817. As local public servants actively utilize the mode every year, it is becoming a sustainable Big Data standard analysis model.

The demand for big data analysis in the public sector will continue to increase as we enter the era of Big Data, and the public’s perception towards data-based administration has changed. Therefore, if we identify each government organization’s task-centered Big Data analysis projects by subject field (safety, transportation and logistics, social welfare and ICT) and connect them with the standard analysis model project, we will be able to sustain the project designed to vitalize data-based administrative activities. In addition, the systemization of the reference model and the advancement of the service will be carried out to spread the public Big Data standard analysis model and enhance sustainability of the model. For example, the Korea Local Information Research and Development Institute (KLID) is thinking of ways to embed the Employment Mismatch Resolution Model in its local administration system (also known as the Saeol system) so that public servants in charge of providing employment support services within the local community can easily access the model. In addition, ways to connect the model with the Ministry of Labor’s Worknet for citizens are also in consideration so that job seekers and employers can get the information they need and use. The Big Data standard analysis model will be ultimately distributed across the entire public sector so that each standard model with distinctive content can be used by relevant public organizations.

Yes

“The vitalization of Big Data usage” was used as an indicator in the annual joint evaluation of local governments to assess each organization’s usage of the standard analysis model. The quantitative and qualitative evaluation is conducted based on the report on the utilization of Big Data available in as the Hyean system.

The indicator used for the joint evaluation of local governments in 2019 is “the vitalization of Big Data usage”, and the evaluation criteria are “the number of Big Data analysis” and “cases of using analyzed Big Data in public services”. “The number of Big Data analysis” is determined based on quantitative evaluation on local governments’ Big Data analysis projects, public contests, and the usage of the Hyean system and the standard analysis model. Local governments are obligated to include at least one use-case of the standard analysis model. “The use cases of analyzed Big Data” is evaluated qualitatively, and the effort made by organizations in charge, the innovativeness and effectiveness of projects are assessed.

The Ministry of the Interior and Safety, based on the evaluation outcome of the designated indicator, is coming up with a plan to vitalize the standard analysis model for next year and follow-up measures. It will ultimately create an internal and external evaluation system to evaluate the project in phases.

As the standard analysis model was developed and distributed, the partnership and cooperation amongst various stakeholders were crucial. Such partnership and cooperation included government and public organizations in charge of relevant tasks, organizations in charge of managing the development and the distribution, expert groups in the private sector, and businesses in charge of the development and distribution. In the process of selecting outstanding cases as follow-up projects after examining the analysis cases, there was collaboration between organizations in charge of the development (government and public organizations) and expert groups that participated in the case review. The Big Data Expert Committee made up of private-sector experts continuously provided expert advice from the task identification to task selection phase. The role of local governments that would manage the development of the standard analysis model after the selection of tasks was also important. The data needed to create the work process to develop the standard analysis model and the analysis model was provided. The cooperation with the National Information Resources Service, in charge of managing the Hyean system, was carried out to receive operational and technical support for the standard analysis model after it is embedded in the system. As such, various stakeholders (government ministries and public organizations) in a restricted environment actively cooperated with common goals to create businesses for public good.

Public servants have relied on their experience when making decisions so far. However, such traditions have created inefficiency in administration such as redundant and overlapping spending of the government budget, and such inefficiency was pointed out as a primary reason for the public’s decreasing trust in the government. Against this backdrop, the administrative activities based on public-sector data were called for to actively address these issues, and the standard analysis model to analyze public-sector Big Data was developed and distributed to take preemptive measures. The Ministry of the Interior and Safety managed to ensure objectivity and credibility to spend the government budget through the sustainable standard analysis model project, and it enhanced the public’s trust in the government through improving the quality of public service.