* Corresponding Author
Email : [email protected]
Volume 12 (2) 2021: 139-153 P-ISSN: 2087-0825, E-ISSN: 2548-6977
DOI: 10.23960/administratio.v12i2.235 Accredited by Kemenristek Number 85/M/KP/2020 (Sinta 4)
ARTICLE
Literature Study On The Use Of Big Data And Artificial Intelligence In Policy Making In Indonesia
Eko Eddya Supriyanto1*, Hardi Warsono2, Augusin Rina Herawati3
1, Doctor of Public Administration, Universitas Diponegoro
2,3 Program Departement of Public Administration, Universitas Diponegoro
How to cite: Supriyanto, E.E. Warsono, H. Herawati, A.R (2021). Literature Study On The Use Of Big Data And Artificial Intelligence In Policy Making In Indonesia. Administratio: Jurnal Ilmiah Administrasi Publik dan Pembangunan, 12(2)
Article History
Received: 17 Oktober 2021 Accepted: 1 Desember 2021
Keywords:
Big Data,
Artificial Intelligence, Decision-Making
ABSTRACT
The use of big data and artificial intelligence in decision-making in Indonesia is still rarely implemented. But in the business world, big data and artificial intelligence are very commonplace to boost targets. This study discusses the use of big data and artificial intelligence in policy Making in Indonesia. The method used in this paper is qualitative research with a literature study approach. The result of this research is that the dynamics in the implementation of public services require appropriate and fast decision making, considering that this is a community demand.
Therefore, public leaders need to disrupt themselves in public services so that these services can be served quickly. In conclusion, big data and artificial intelligence can help public leaders make decisions to deliver the best policies. This research implies that it can be used as a reference for policymakers that big data and artificial intelligence can be used in decision-making to warn Policymaking.
A. INTRODUCTION
In industrial revolution 4.0, all elements are required for fast processing from upstream to downstream. The development of the current structure and governance process is no longer relevant because it is disrupted by the digital age that forces the government to disrupt itself with the development of technology that is always new. (Supriyanto, 2016).
Public leadership is also not spared from digital disruption, so public leaders must be accustomed to and capable of digital literacy, a new habit in the government environment.
(Mureddu et al., 2020). But the adoption of technology does not have to be all swallowed raw. There needs to be a screening of every decision taken in digitization because of the linkage with digital data security that is prone to be perverted. (Fachriandi & Dirgahayu, 2021). Then in terms of the digital gap, public leaders should also be able to map those who have resources with those who do not have access to digitalization. (Purbokusumo &
Katangga, 2021).
So quickly, the current globalization that disrupts all lines of life forces the government to change the electronic government roadmap. The need for the public to flow swiftly information in public services must be addressed by the government wisely.
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The Government of Indonesia has launched a roadmap of an electronic-based government system (SPBE), which is implementing government using information and communication technology. This is supported by the Presidential Regulation of the Republic of Indonesia Number 95 of 2018 concerning Electronic-Based Government Systems. SPBE aims to realize clean, effective, transparent, and accountable governance and quality and reliable public services. (Ibrahim et al., 2020).
Innovation in information and communication technology in public services has been widely carried out in several regions in various public affairs (Rafinzar & Kismartini, 2020).
But the government's commitment to conducting this information and communication technology revolution is an opportunity for the government to innovate in developing the state apparatus through implementing an electronic-based government system.
There is very good for the climate of public openness in Indonesia because the implementation of the SPBE is aimed at the effectiveness and efficiency of public services and the transparency of data owned by the government such as human development index, economic development index and so on.
To support SPBE, there needs to be keeping data that can accommodate large amounts of data, commonly called big data. Big data itself is an extensive collection of data that continues to grow in number (Supriyanto et al., 2021). Wu (2013:30) provides terminology related to big data, which manages significant information assets. High data access speed and complexity can help organizations manage data more effectively and encourage innovation in decision management and insight improvement.
Big data is a breakthrough due to the industrial revolution 4.0 that facilitates the large quantities of data management, storage, and analysis of data from various sources (Martin et al., 2019). The existence of big data also supports the government's efforts in the policy of one national data listed in Presidential Regulation of the Republic of Indonesia Number 39 of 2019 concerning One Data Indonesia.
Meanwhile, artificial intelligence can assist the government in making the best decisions (Höchtl et al., 2016). With the help of A.I., the government will be presented with extensive data related to decisions used as public policy. A.I. itself is a machine or computer that synthesizes the human mind to solve problems. A.I. works based on computer systems to demonstrate intelligent human-like behaviours devoted to specific core competencies, such as perception, understanding, action and learning.
Based on some of the above, big data and artificial intelligence in decision-making on public policy in Indonesia can be considered.
B. LITERATURE REVIEW
Concerning digital literacy, the term big data emerged before the industrial revolution 4.0, which was introduced by Fremont Rider, a librarian from the United States, in 1914. At first Big data appeared in connection with the increasing volume of books, the difficulty of storing it, and how to use it as research material (Martin et al., 2019).
The era of big data then began to become a severe conversation again since social media increasingly brightened the internet world, such as Facebook, Twitter, Line, and Instagram.
Internet users are not only consumers of the available data but are producers of the data itself.
Big data can produce a pattern or form that makes new knowledge (Schroeder, 2014).
This significant data phenomenon is increasingly attractive to discuss when this data issue includes how this data is placed and found and how this data can be utilized and valuable for the benefit of human progress.
The government system uses big data to accelerate the implementation of government programs. Some of the benefits taken from Big Data in government can be utilized for
government programs, empowering citizens to increase transparency and participation of all stakeholders.
Big Data in the government system can create various faster, more accurate, and cheaper policies with multiple institutions in the government. Big Data uses information using an analytical approach so that the results become more structured. The role of Big Data for government or public services is significant because it uses analytics from Big Data so that it can transform external data into information. Then translate that information into a policy that will help government performance. The government can achieve some things by utilizing Big Data technology (Merhi & Bregu, 2020).
First, the increase in government performance is caused by the efficiency of work carried out by utilizing Big Data so that conventional work is reduced. The use of big data can also be a solution to funding problems in government. By using Big Data, the funding process can be cut down to be more efficient. With the increase in government performance, it is hoped that it will positively impact its survival and people. The government can take advantage of the collection of data in Big Data into information quickly, easily, accurately, and cheaply to determine policies that suit the needs of its people.
Second, the use of Big Data in the government system will increase state revenues. The use of Big Data will reduce the burden on infrastructure to reduce the amount of state spending. The use of Big Data technology will be able to analyze these data. It will also be helpful in various government sectors such as export-import, agriculture, trade, and even tourism, which has an impact on increasing state income.
Third, using Big Data in the government sector is the transparency of the data presented.
There will be very useful for the public to know more transparently the data regarding the government so that they can realize Open Government, which can increase public trust in the government.
Concerning technological literacy, the 4.0 industrial revolution is related to artificial intelligence and autonomous robotics. Artificial intelligence is a technique of imitating the intelligence possessed by humans so that computers have intelligence that matches humans.
With this Artificial Intelligence, humans are helped in dealing with various problems or complex environmental phenomena.
An autonomous robotic is a robot that can do work without having to be guided by humans. These robots can work in various situations, including land, air, and sea, to help humans work.
Artificial intelligence or artificial intelligence can be used in accelerating public services organized by the government. The implementation of artificial intelligence is a form of service transformation that includes e-services, strengthening community supervision, and strengthening the innovation ecosystem (Purbokusumo & Katangga, 2021).
Artificial intelligence in public services can be applied to the help desk in the service unit, analysis of service complaints, directing complaints to the intended agency, and even answering objections. In this very dynamic situation, it is necessary to accelerate the application of artificial intelligence. To support the activities and work carried out by the state civil apparatus (ASN). The work is technical like administration and data processing previously manual, can be switched by utilizing technology, making it more efficient and shortening time.
Artificial intelligence is used to achieve the provision of integrated and quality data and information. From the side of government administration, it can be applied as document processing such as speech recognition and text or script.
Improving service quality by implementing technology is also directed at overcoming bureaucratic blockages so that the government is more flexible and makes decisions more
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quickly. The setting of service standards and the business processes that follow are the keys to the length and length of the government bureaucracy.
The government must provide the best service through the bureaucracy by placing the community as a subject, not an object of service. "With this perspective, it is hoped that the level of public trust in the government bureaucracy will be built and strengthened.
We can see that the use of A.I. is still limited in the e-commerce, logistics and banking, and financial sectors. The National Strategy for A.I. that has been compiled will be important in driving economic development and public services. The Artificial Intelligence National Strategy compiled will be strengthened by a Presidential Regulation (Perpres) on Indonesia's strategy in the use of artificial intelligence in all aspects, which include the fields of A.I.
talent development, Ethics and A.I. policy studies, A.I. Infrastructure and Data, A.I. Industry Research and Innovation, as well as Priority and Quick win A.I. implementation.
The use of A.I. in the process of forming laws and regulations in the era of the industrial revolution 4.0 to minimize errors in the preparation of rules and regulations, but whether the use of A.I. can immediately replace the authority of the organs forming legislation as a whole, of course, this is still a matter of debate.
The era of the fourth industrial revolution was also marked by the emergence of technological breakthroughs in various fields, including robotics, artificial intelligence (A.I.), nanotechnology, quantum computing (quantum computing) biotechnology, internet of things (IoT), industrial internet of things. (IIoT), fifth-generation (5G) wireless technology, additive manufacturing/3D printing, and fully autonomous vehicle industry.
Artificial intelligence or Artificial Intelligence is present as a branch of science from Computer Science that promises many benefits in answering human needs in the future. For example, in the health sector, A.I. is currently being used to help develop a Covid 19 vaccine (Corona Virus Disease 2019), identifying people infected with COVID 19 with a 90%
success rate.
Physicists Stephen Hawking et al. state the implications of In the short term, A.I.
depends on who controls it, while in a long time, it depends on whether A.I. can be prevented or not. Conclusion: there should also be considered when A.I. begins to be used in the legal field, especially in forming laws and regulations.
The Ministry of Communication and Information as an accelerator, facilitator, and regulator of Indonesia's digital transformation, will, of course, continue to contribute to increasing the utilization and adoption of A.I. technology in a prudent, thrifty, and trustworthy manner, and following the national identity, through three strategic steps. The three strategic steps are preparing and developing digital talent capable of A.I. technology, secondly facilitating ecosystem development, and thirdly preparing regulations and governance. Regarding the development of A.I. qualified digital talent, it is carried out by focusing on the literacy aspect of A.I. technology and the technical development of A.I.
skills.
C. METHOD
Qualitative research methods become the methods used in this research with a library research approach. By reviewing books, national and international journal articles, and work papers that discuss the use of big data and artificial intelligence in decision-making a policy.
The data used in this study is secondary data such as e-government ratings, service levels, and public participation in public services by the government. We use data from articles on ScienceDirect and the Web of Science (WoS) to conduct a review analysis of reports that are relevant to this discussion. We reviewed 20 articles from reputable international journals.
D. RESULT AND DISCUSSION
Decision Making in Policy
Decision making in public policy is classified into two models: top-down (elitist) and bottom-up (pluralist) decision making. At the top down, the central government takes decisions without considering input from the community, while the bottom-up of the community becomes a determinant of policy. (Rochefort, 1997).
The decision-making stage in the policy cycle gained more attention in the early stages of policy science development, when analysts borrowed heavily from various decision- making models in complex organizations, as developed by experts in public administration and business organizations (Rasmussen, 2001).
The rational model was chosen as a model of how decisions should be taken. In contrast, the incremental model is described as the most practised model in government (Joss
&Brownlea, 1999). Some of these efforts are directed at synthesizing rational and incremental models. Others – including a decision-making model called 'garbage-can – focus on various logical elements of organizational behaviour to achieve alternative models other than rationalism and incrementalism (Anderson, 2011).
The choice of policy alternatives and the impact that may arise in the problems estimated at this stage is the most political stage when potential solutions to a particular issue must be won. Only one or more answers are chosen and used. The choices that are most likely not to be realized and deciding not to include a particular flow of action is a part of the selection when it finally decides the best.
Each model recognizes that the number of relevant policy actors decreases as the policy process progresses. Agenda-setting involves a large number of state actors and society. The number of relevant actors remains large at the policy formulation stage but only includes state and community actors who are part of the policy subsystem. The public policy decision- making stage involves fewer actors, as it excludes all non-state actors, including those from other levels of government. Only politicians and government officials can make authoritative decisions in the problem areas participating in this stage.
These models also recognize that in modern government, the degree of freedom enjoyed by decision-makers is limited by many rules governing the political and administrative office and limiting the choices of actions of those officeholders. These rules range from the country's constitution to specific mandates aimed at individual decision-makers through various laws and regulations. These rules usually determine what decisions are possible for agencies and government officials to make and regulate the procedures that must be followed to arrive at that decision. As Allison &halperin (1972) notes, these rules and operating systems provide 'action channels for decision-makers—a set of policies registered to produce specific types of decisions.
These rules and SOPs explain why the decision-making process in government has become so routine and repetitive (Aragona &De Rosa, 2019). While these rules and SOPs limit the freedom of decision-makers, there is still considerable discretion in individual decision-makers to arrive at their assessment of the best way to act following the circumstances. Decisions about what process happens next and what decisions are considered best vary due to a tug-of-war between decision-makers and the context in which these decision-makers operate.
The two most recognized models in public policy decision making are usually called rational models and incremental models. The first model is essentially a model of business decision-making applied in the public arena, while the second model is a political model applied in public policy. Other models attempt to combine rationality and incrementalism
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with different compositions for each model. On the contrary, in contrast to models that recognize rationality, although the degree is other; In the decision-making process, the garbage can model photographs the decision-making process as an essentially irrational (but not entirely irrational) process based on the propriety and decision-making behaviour that has become ritualized.
In the study of decision-making, rational models are rooted in early efforts to establish a discipline on organizational behaviour and public administration. Various elements of this model can be found in the works of early public administration experts such as Fayol (1916) in France and Gulick &Urwick (2004) in The United Kingdom and the United States. By making the idea put forward by studying France's coal industry towards the XX century and codifying a model, they had the best decision they could make. The PODSCORB model they developed implies that organizations can maximize their performance through Planning, Organizing, Decision Making, Choice, Coordinating, Hiring and Planned Budgeting.
Figure 1. Spectrum Various Decision-Making Styles
Then, analysts who carry this perspective begin to claim that this form of decision- making will only give maximum results if all possible alternatives and costs of each choice are considered before a decision is taken – this is called the 'rational comprehensive' decision- making model (Edwards, 1954). A renewed emphasis on comprehensive aspects proved problematic, and criticism soon sprung up. Conclusion: there are human limits that decision- makers have to complete in building alternatives and calculating the benefits and burdens that each alternative brings. In addition, there are also political and institutional restrictions that limit the selection of options and decision choices. The rational-comprehensive model is criticized as misleading. Some even consider it close to 'heretical'.
Despite accepting various theoretical possibilities for multiple decision-making styles, Lindbom, in his later works, rejected all alternatives to incremental models based on practical reasons. He argues that any notice analysis that seeks to reach decisions based on various maximization-oriented criteria will fail. All decision-making is based on what he calls 'incomplete and generalized' analysis. Cohen & Lindblom (1979) argues that incrementalism's essence is to systematize the various decisions achieved through this method by emphasizing the importance of reaching political agreement and learning from trial-and-error, rather than just struggling with arbitrary decisions.
If incremental models might provide an accurate description — which this claim is also debatable — about how public policy decisions are often made, critics have also found some errors as implications of the model's suggested review flow. First, the model was criticized for being very lacking in goal orientation. As the Fosters put it, incrementalism 'will get us across multiple intersections over and over again without knowing where we're going' (Forester, 1984).
Second, it was criticized for its inherent tendency toward conservatism, overly pessimistic about big-scale change and innovation. Third, the model has been criticized for
being undemocratic, as it limits decision-making to the bargaining of a small group of preferred people, senior policymakers say. Fourth, by not paying attention to systematic analysis and planning and, more or less, negating the need to seek new alternatives, this model is considered to encourage the emergence of decisions based on short-term calculations, which are feared to have long-term negative consequences. In addition, the model was also criticized for having only limited analytical capabilities. Dror (2017) exemplifies that incrementalism can only work when there is a persistent problem over a long enough period, which this problem seeks to be solved through a specific policy. This model also requires that the means needed to implement the policy are almost always usable. These conditions are rarely met. Incrementalism also has characteristics as a model of decision- making in a relatively stable environment and is somewhat tricky to apply to unusual situations, such as crises.
Forester argues that the following conditions must be met for decision-making according to a rational model. First, the number of agents (decision-makers) should be limited, if necessary, as little as possible. Second, the executive order for decisions must be as simple and closed from the influence of other policy actors. Third, the problem at hand must be clearly defined; In other words, scopes, horizons, value dimensions, and consequence chains must be fully known and understood. Fourth, information must be as perfect as possible known, in other words, complete, accessible, and understandable. Lastly, there should be no insistence on making a decision as soon as possible; That is, the time available to decision- makers must be available in unlimited numbers so that they can consider all contingencies that may occur along with the current and future consequences. When these conditions can be met perfectly, rational decision-making can be expected.
If these five conditions are not met, which is often the case in practice, Forester argues that we will find other decision-making models. Thus the number of agents can increase to an unlimited number; existing order may include a variety of different organizations and be relatively open to external influences; the problem faced will be ambiguous or multi- interpretation; information is incomplete, misleading or intentionally manipulated' and the time available may be limited or consciously controlled. The following chart describes the various parameters of decision making.
Table 1 Various decision-making parameters
Variable Dimension
1 Agent Single - Many
2 Setting Single, closed – Many, open
3 Problem Clearly defined – Multi- interpretation, ambiguous 4 Information Perfect – tested
5 Time Unlimited – manipulated
Source: adapted from John Forester: 'Bounded Rationality and the Politics of Muddling Through'. Public Administration Review 44, 1 (1984)
From this perspective, Forester argues that there are five possible decision-making models: Optimization, Satisfycing, Search, Bargaining, and Organizational. Optylation is a strategy used when the terms of a comprehensive rational model, as described above, are
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fully met. The prevalence of other models depends on how many conditions are not met.
When the limitations are cognitive, it would be better if we use the satisficing model for various reasons previously stated. The other models suggested by Forester overlap each other making it difficult to distinguish and expose them one by one. The Search Model is one of the models that can be used when the problem is not clearly defined. A bargaining chip is a model that can be found when various actors have to make decisions in situations of complete information absence and urgent timing. Organizational models involve a variety of settings and actors, who, although resources of time and information are available, are faced with diverse problems. In short, these decision-making models involve a more significant number of actors, more complex settings, more varied issues and fog, incomplete and distorted information, and limited and urgent time.
Although Forester's thinking is an important step forward in providing classification and taxonomy and certainly provides a useful alternative option, in addition to rational, incremental and garbage-can models, what it does is only the first step in building a better decision-making model. A big problem in the taxonomy it makes is its disconnection from its arguments. A closed-door review of his discussion of the various factors influencing decision-making reveals that one would expect to find more than one model that might emerge from the five choices of combination models and permutations of the variables it presents. Although various categories, in practice, cannot be sorted and, on numerous occasions, are not very useful for analytical purposes, the reason why one should use one of the models he put forward remains unclear.
We can develop the Forester model by redesigning its variables. The study of 'agents' and settings can be perfected by focusing on policy subsystems while thinking about 'problems', 'information', and 'time' can be seen as thoughts related to the types of constituency faced by policymakers. Thus, the two main variables here are (1) the complexity of the policy subsystem that addresses existing problems and (2) how much the train must be faced. The complexity of a policy subsystem affects the likelihood of successfully reaching an agreement or opacity of choice in that subsystem. Some preferences are considered to be in line with the central values held by members of the subsystem. In contrast, others are not, so the complexity of these subsystems structure decisions in the category of hard or soft choices.
Similarly, decision making is relatively limited by information and time, as well as clarity of issues. The following chart illustrates the four basic models of decision making.
These four models appear as the basis of the two dimensions described in this analysis: the complexity of the subsystem and the degree of the constrain.
Tabel 2. Basic Model of Disconnection
The complexity of policy subsystems Degree of
constraint
Tall Low
Tall Incremental adjustment
Satisfying Search Low Optimizing
adjustment
Rational Search
Source: modified according to Martin J. Smith, 'Policy Networks and State Autonomy,' in Political Influence of Ideas: Policy Communities and the Social Sciences, eds. S. Brooks and A.-G. Gagnon. New York:
Praeger, 1994.
In this model, complex policy subsystems will give rise to adjustment strategies rather than search strategies. Situations of high degrees of constraint tend to result in a bargaining
approach in decision making, while problems where low content levels tend to give rise to optimisation or rational activity.
Combined, these two variables produce four basic models of decision-making.
Incremental adjustments of the Lindblom style tend to appear when existing subsystems are complex and have high degrees of constraint. In such situations, we can expect that there will rarely be large-scale and high-risk decisions. In the opposite scenario, when the existing policy subsystem is simple and the degree of constraint is low, a rational search approach and significant changes are highly likely. When there is a complex subsystem and a low degree of training, there will be an adjustment strategy. Still, this time it is aimed at achieving optimization. Lastly, when the degree of content is high, but the complexity of the subsystem is low, satisfying strategies will become a common trend.
The Use of Big Data and Artificial Intelligence in Policy Making in Indonesia Governments worldwide have provided e-government services to their citizens with varying degrees of success (Adjei-Bamfo et al., 2020; Al-khamayseh et al., 2006). One of them is through the e-government media portal as an integrated service centre, which can facilitate citizens and business people to complete transactions with the government without face-to-face (Bekkers & Homburg, 2007; Heeks, 2001; Hernández-Bolaños & Rodríguez- Díaz, 2016; McClure, 2000; Moon, 2002). So in the current development, the implementation of e-government has become an important strategy to achieve effectiveness and efficiency in administrative governance and public services (Gil-Garcia & Martinez-Moyano, 2007; Hu et al., 2014; Pardo & Styrin, 2010).
However, challenges in the adoption of e-government cannot be undertaken in a limited period but instead require an integrative architectural framework approach to placing government information and services online (Weerakkody et al., 2016). This condition can be known from most countries that have released e-government strategies and set various approaches in its development, but the expected results are still not maximal (Panigrahi &
Srivastava, 2016). In addition, based on the E-government Survey report conducted by the United Nations (2020), which noted that of 133 countries, there are still 39.5% at the level of Low EDGI and Middle EDGI in 2020.
In addition, the Determination of E-Government Development Index (EDGI 2020), conducted by the United Nations in two years, is based on a combined assessment of three essential dimensions of e-government, namely: the provision of online services, telecommunication connectivity, and human capacity (E-government Survey, 2020). The results of the e-government survey showed that most developed countries such as Denmark, the Republic of South Korea, the United States, and Canada were at the top of the EDGI 2020(E-government Survey,2020). They used various systems, platforms, data formats, procedures, and protocols (Hong et al., 2019). Then, in supporting e-government development implementation, countries with a high-value E-Government Development Index (EDGI 2020) overall use portal technology that provides high-quality services to citizens and contributes significantly to their success (Taylor et al., 2014).
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Table 3. Countries with e-Government Development
Source: EDGI UN 2020 Data
The success of leading countries ranked in the top E-Government Development Index (EDGI 2020) is influenced by three main dimensions in the development of e-government (OnlineService, Telecommunication Infrastructure, and Human Capital). The EDGI dimension catechism is related to the development of Interoperability, where the main focus of both on e-government focuses on solving the same case, including in the provision of well- integrated network services, system integration and activities extending to greater collaboration and integration between institutions (M.M. Kamal, V. Weerakkody, and S.
Jones, 2009) and providing substantial benefits (Z. Irani, M. Themistocleous, and P. E. D.
Love, 2008).
The application of big data and artificial intelligence to Policymaking in Indonesia certainly requires e-Governance innovation in decision-making.
Based on a literature review, the key components necessary for the proper functioning of a company that stimulates its innovation have been identified. To implement and manage the creation, an organization must have (1) resources (Mureddu et al., 2020); Munn et al., 2019;
Aragona & De Rosa, 2019) (2) innovation-based creativity by utilizing technology.
Figure 1. Model of value creation through innovation
Denmark Republic
of Korea Estonia Finland Australia Sweden United Kingdom
New Zealand
United States of America
Indonesia
EDGI 9706 10000 9941 9706 9471 9000 9588 9294 9471 6824 EDGI 9588 8997 9266 9549 10000 9471 9292 9516 9239 7342
EDGI 9979 9684 9212 9101 8825 9625 9195 9207 9182 5669
EDGI EDGI EDGI
Linear (EDGI ) Linear (EDGI ) Linear (EDGI )
In the table, it is an innovation process in using big data. Still, from the government side, it can adapt its business in the same way with a note that it regulates bureaucratic characteristics with the company. Along with the development of the deliberative approach in the decision-making process, experts raise several criticisms about the weaknesses of the application of the system. The contemplative practice is considered to have various limitations, especially external intervention and political interests in formulating public policies, and explained that the deliberative process would present multiple groups with multiple interests to interrupt the decision-making process. In addition to the decision-making process, the presence of these various groups will cause group polarization and potential conflict.
The use of big data is also considered a tangible form of data-driven policy. The presence of big data has perfected the approach to decision-making from evidence-based policy to data-based policy by offering excellent and effective Policy-making. Data-driven policies can lead to data-centric focused policies by creating systems that integrate data and evidence in one holistic view. Data-based policies provide input, output, productivity, and process data that can be stored and retrieved more comprehensively and more detailed than previous approaches.
Looking at the characteristics of big data, the use of big data is considered a very effective method because of its ability to collect information quickly and easily gain access, especially if the data is public and open. The leading cause of the development of big data is the high level of need for data collection, use, and sharing in the era of digitalization.
Digitization (transformation from analog to digital) is also increasingly favored by various sectors because it facilitates all organizational and research matters, from planning, analysis, and evaluation. The increasing need then requires the ability to manage large amounts of data. Therefore, using big data continues to grow and is used by various sectors to meet all decision-making requirements (Hong et al., 2019).
In its development, big data can influence the logic and structure of the bureaucracy in carrying out its governance. Extensive data analysis also contributes to government bureaucratic decision-making and policy-making because of the combination of large-scale data and knowledge, leading to an innovative, informative, and structured policy (Giest &
Mukherjee, 2018). In public administration, extensive data analysis provides benefits such as administrative reform, security, public infrastructure, economy and employment, policy modernization, and public services. However, comprehensive data analysis must also pay attention to the context and other data units so that the resulting decisions become more accountable. In addition, regulations regarding privacy and data protection must be respected.
The balance between the socially beneficial use of big data and the potential harm to privacy and other values is crucial in public administration. Big data has a lot of potentials, but on the other hand, it can also put people's freedom under pressure.
It is undeniable that several government systems are pretty dependent on extensive data analysis (BDA) in every decision-making. The United States (U.S.) government, for example, utilizes comprehensive data analysis to make decisions on the recruitment of national security forces (U.S. Army). The American government analyzes every government, commercial, and social media data to reveal any patterns/information related to American army applicants.
The results show that 21.7% of applicants have serious financial problems, domestic crimes, to drug use. In addition, the New Zealand Government also utilizes extensive data analysis in every decision-making to achieve a high-performing policy system. The New Zealand government has an agency to analyze the quality of each policy plan that will be passed. The analysis is carried out using policy measuring tools that utilize various data from various platforms. The agency will be involved in every policy formulation process in New Zealand
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and test the effectiveness of each policy plan that will be passed. The policy testing tools include a policy capacity and capability framework, a capability framework, and a quality framework.
Another example is the collection of infrastructure-related data through citizen participation from the Boston's Street Bump application which was carried out to measure the smoothness of car travel based on individual mobile phone movements. The data obtained helps the government find initial information regarding the implementation of appropriate policies, especially those relating to the identification of areas that receive priority for infrastructure improvement. The information can then be used in open policy discussions, helping to find the most efficient starting point for implementation. The presence of big data is proven to influence the policy-making process of a government, including changing procedural and substantive policy instruments.
E. CONCLUSION
The essential character in the public policy decision-making process is just like any other stage in the policy process. Similar to previous steps in the public policy process, the decision-making stage varies according to the nature of the policy subsystems involved in that process and the degree of consensus faced by decision-makers. As John Forester summarizes, what is rational for administrators and politicians is determined by the situation in which they work. Urged to provide recommendations immediately, then these decision- makers cannot conduct in-depth studies. When faced with competition and organizational competition, these decision-makers are perfectly rational to become more secretive. What is reasonable is determined by the context faced, both in everyday life and in public administration.
The presence of big data cannot be denied offering an exciting approach in the public sector, especially in the public policy formulation cycle. In addition to providing efficiency in the public sector (both in terms of budget and resources), extensive data analysis in public policy formulation also provides a new perspective in the form of openness from the public sector, which previously seemed more closed. After the era of big data as it is today, the exchange of information between the government and the community and the government and other agencies is running more dynamically. The public sector has also begun to provide more comprehensive access to every community to see their performance or activities.
ACKNOWLEDGMENT
Thank you, author, to all lecturers who master the article writing techniques; this article was successfully written because of the guidance of lecturers in this course. Hopefully, this paper can inspire policymakers to take advantage of big data and artificial dissing in decision- making for policymakers.
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