Title: Big Data and Policy Design for Data Sovereignty: A Case Study on Copyright and CCL in South Korea

Authors: Hyejung Moon, Hyun Suk Cho

Abstract: The purpose of this paper is as follows. First, I am trying to conceptualize big data as a social problem. Second, I would like to explain the difference between big data and conventional mega information. Third, I would like to recommend the role of the government for utilization of big data as a policy tools. Fourth, while referring to copyright and CCL(Creative Commons License) cases, I would like to explain the regulation for big data on data sovereignty. Finally, I would like to suggest a direction of policy design for big data. As for the result of this study, policy design for big data should be distinguished from policy design for mega information to solve data sovereignty issues. From a law system perspective, big data is generated autonomously. It has been accessed openly and shared without any intention. In market perspective, big data is created without any intention. Big data can be changed automatically in case of openness with reference feature such as Linked of Data. Some policy issues such as responsibility and authenticity should be raised. Big data is generated in a distributed and diverse way without any concrete form in technology perspective. So, we need a different approach.

I.  INTRODUCTION

  The rise of big data has brought both infinite opportunities and serious risks in economic, cultural, and social sectors, nationally and internationally(Moon & Cho, 2012). Private companies and governments are obsessed with effective responses to opportunities and risks derived from big data. Obama administration decided to invest about $ 200 million in big data sector for implementation of Government 2.0 last 2012(DailyTech, 04/02/2012). Park Geun-hye government  in South Korea also have decided to invest $45 million to big data for government 3.0 project in next 5 years, in spite of tightened e-government budget(ZDNet Korea, 04/03/2013).
However, I wonder that these promotion efforts for big data could be just short hot wind once. Most governments are pushing big data projects in basic level without a specific direction in the same way ISPs and consulting firms promote big data for just private profits. Even though the interests in big data are heightened in private market, the way big data is conceptualized and utilized are no different from the existing application of mega information. IT venders such as IBM, SAP could not suggest a clear distinction between big data and mega information. Big data is just ambiguously conceptualized to have features like 3V (volume, variety and velocity) or 4VC (3Vs plus Value and complexity). Could we indeed distinguish big data from the conventional mega information both conceptually and practically? Without a clear conceptualization of big data, I have a doubt how we can respond effectively to both opportunities and risks which big data has brought.
Because of this reason, first, I try to conceptualize big data as a social problem. Second, I would like to explain how big data is different from existing mega information. Third, I try to suggest the role of the government for utilization of big data and accompanied responsibility in terms of a policy tools. Fourth, based on case studies of copyright and CCL, I would like to explain the regulation for big data in terms of data sovereignty. Finally, I would like to suggest a direction of policy design for big data age.
Table I summarizes the theoretical background and research concept for policy design stage in this study. Policy design includes both technical analysis and political reasoning as a policy process for achieving a particular purpose(Birkland, 2011). Process of policy design are "1) define the social issue, 2) understand causal relation, 3) set the policy target, 4) select policy tools, 5) conduct objectives".
 Zeleny(1987) had defined the conceptualization of data and information in his research about knowledge taxonomy. Rowley(2006) had explained the feature of (big) data in the research of wisdom hierarchy. I've referred the theory of public commons for setting the scope of problem about sovereignty of big data(Hardin, 1968). The theory of policy tools has been referred for selecting policy tools among available alternatives(Hood, 1983). I've referred a case study of regulation structure for conducting policy objectives(Lessig, 1999).

II. CONCEPTUAL DISCUSSION OF BIG DATA

The term of data was academically distinguished from information by Zeleny(1987) for the first time. He proposed the knowledge taxonomy consisting of four categories, that is, data, information, knowledge, and wisdom(DIKW taxonomy). He defined that data is all electronic signal done by computing process. In the past it was impossible to manage all data under resource constraints such as high costs and lack of technology. Therefore only a few important data were gathered, translated and managed into information under resource constrains in the last 20C. Most data which were not translated into information were deleted and impossible to use later. Only a few experts could access data and create information in this time. He said that sequential management in order by data → information → knowledge → wisdom for overcoming resource constrains in early information and technology age.
Big data age means that all electronic signal from computing has became a just huge size data seen from the definition of Zeleny(1987). He said that data had a feature of volatility to be saved and deleted temporally. Then how could the data become so huge volume? Behind this situation, the development of an information and communication technology has played an important role. The condition of data processing has been improved owing to technological progress of processor and network. Whole data could be saved permanently because of cost reduction for data processing and data access.
On the other hand, Rowley (2006) argues that, under the condition of rich resources and sufficient technology, wisdom hierarchy can be translated in dynamic and interactive way. So, the higher insight analysts have, the more value is extracted from utilization of data. Figure 2 describes the relative wisdom hierarchy with Human Input Structure and Computer Input Programmability.




Processing huge data needs high computing performance from technology perspective. Required performance has become lower for processing to wisdom side. On the other hand, handling the data required low performance in human efforts perspective. Understanding the wisdom side needs more hard effort from human capability.
Therefore I would like to conceptualize big data as following. Big Data is permanent data generated from whole electronic signal during computing process. It has features with volume, variety, velocity, complexity. It is basically opened, shared and published to be accessible to any amateur analyst. The values from big data are various according to analyzer's insight.

III. POLICY DESIGN IN DIGITAL AGE

 The key fact is openness of big data which has infinite value from these features. However there is also the potential risk resulted from openness of big data. Big data is owned by both all of us and none of us. Big data becomes a public commons like grass in 'tragedy of the commons' suggested by Hardin(1968). Anyone can use big data. But if they use big data without discernment, all members of society can be damaged. Therefore big data is a public good requiring public engagement. We need to pay attention to the assertion of May(1991) that government should engage social problems both actively and effectively.
Existing mega information is created for specific purpose in private sector. But, big data is coincidentally generated without any objectives over public area. Because of public properties of big data, potential risk could be increased as companies tried to seek private profit. Therefore big data is one of important asset which needs government engagement and governance system. So, big data needs to be understood and controlled as SOC from policy perspective.
Policy design is different according to state's own resources. Hood(1983) consists that  policy tools are distinguished to nodality, authority, treasure, organization in TABLE II.




Nodality gives government the ability to traffic in information on the basis of ‘figureheadeness’ or of having the ‘whole picture’(Simon, Smithburg & Tompson, 1950). The limiting factor is credibility and the ‘coin’-how government spends this resource- is message sent and received. Treasure gives government the ability to exchange, using the ‘coin’ of ‘moneys’ and subject to a limit of ‘fungibility’. Authority gives government the ability to ‘determine’ in a legal or official sense, using tokens of official authority as the coin, and subject to a limit of legal standing. Organization gives government the physical ability to act directly, using its own forces rather than mercenaries. The coin is ‘treatments’ or physical processing, and the limiting factor is capacity. ‘Nodality’ woks on your knowledge and attitudes, ‘treasure’ on your bank balance, ‘authority’ on your rights, status, and duties, and ‘organization’ on you physical environment or even on your person. Hood (1983) consists that policy for solving social problem should be designed focusing on nodality in digital age.
Almost solution has specified the shape of regulation for policy implementation. Lessig(1999) mentions that general regulation is combined with law, market, norm, architecture(a). Technology is one of representative regulation in architecture area. Regulation structure of Lessig(1999) is summarized in Figure 2.




Law based regulation are generally formed for solving traditional problem such as financing issue and contract(b). Architecture based regulation are more effective for new problem such as construction, traffics, Information & Technology issues(c).

IV. A CASE STUDY ON  DATA  SOVEREIGNTY

In this paper, I try to discuss the difference in policy design between big data and mega information to solve the data sovereignty based on a case study of copyright and CCL in big data era.





The Korean government made a decision that the National Library will have authority to manage copyright  policy since 2009. Ironically the National Library is now managing the copyright of online contents using CCL based on the idea of openness and sharing.




CCL is campaigning for data sharing in over 70 countries and 100 non-profit foundations(CC, 2013). I would like to suggest that CCL is appropriate regulatory framework for online contents in big data era.
The result of this case study is that there are four kinds of differences between big data and mega. Table IV summarizes the comparison in terms of background, cause, shape, usage perspectives.




Information is created from data for specific purposes. We can get effective ROI when a few necessary data should be translated into information under lack of resource and technology(A-1). Mega information is just enlarged information made from data. Therefore information was just  created for achieving specific purposes like profit seeking(A-2). The management of this information are centralized under control of private sectors(A-3). The concept of information was clearly defined in order to avoid duplication for the purpose of reducing the cost(A-4). So, only a few experts could access the information in private area.
As mentioned earlier, big data is just huge data from all electronic signal assembled during computing process. Assembling and processing big data has been possible by advanced information and communication technologies. Recently the cost has been reduced for computing and also network performance has been faster than before. So whole data can be saved and used permanently(B-1). Big data is generated without any intention, so the value from big data becomes different(B-2). Big data is published in an open and distributed way autonomously without any ownership or management(B-3). Any amateur can access and analyze big data on his own need(B-4).
The rapid development of technology became a background to distinguish big data from conventional mega information. The purpose of data generation was changing as market environments were evolving. Market trend have also influenced regulation law concerning data sovereignty. Data accessibility is no longer confined to few experts and spread to many amateur as a data analyst. The features of big data are described at TABLE V in terms of technology, law, market and organization perspective.



Big data leads public opinion. We can find new opportunity and risk using big data such as weak signal. Utilization's effectiveness of big data is growing up. In this case anyone can access and use big data for free. Big data never has been exhausted. Therefore big data became public goods under government's control.




V. CONCLUSION

AS for the result of this case study, policy design for big data should be distinguished from policy design for mega information to solve the problem about data sovereignty.
In law system perspective, big data autonomously is generated. It has been opened and shared without any intention. So there is no owner of big data. But, problems occur always when we use any data for both profit seeking and risk prevention. So, we need to discuss about data sovereignty.
In market perspective, big data is created without any intention. Mega information is generated for special purpose with consideration of budget and cost. In other hand, as big data has been generated inconsistently, the value of data becomes variable according to analyzer's insight. Therefore,  proper management is needed to enhance individual value when to make a analysis of big data in contrast to analyzing   mega information needing high cost.
Big data has not any purpose, so it makes falsification with any intention. Or it can be changed automatically with reference feature such as Linked of Data. So some issues can be raised about responsibility and authenticity.
Big data is generated in open and distributed way without any formation seen from technology perspective. So, we need fresh conceptualization of big data to use it. We also need to understand new technology about big data such as HDFS, NoSQL.
Figure 3 describes the regulation structure of big data and mega information from law, market, norm, and architecture perspective.



Implications of this study are as follows. First of all, policy for big data should be designed focused on technical architecture under highly advanced technology environment. Next, it is necessary to establish an organization staffing technical experts for designing technology-based policy. Finally, policy making needs to be implemented on a global dimension beyond individual states in big data era.




Big Data and Policy Design for Data Sovereignty: A Case Study on Copyright and CCL in South Korea by Hyejung Moon is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
Based on a work at www.itpolicy.co.kr.