정보통신기술(ICT)이 발달함에 따라 데이터 양이 폭발적으로 증가하고 있다. 최근에 이러한 빅데이터를 활용하여 새로운 가치를 창출하려는 시도가 많아지고 있다. 이미 타산업분야나 외국에서는 빅데이터를 활용하여 생산효율성 증대, 수급예측뿐만 아니라 사물인터넷(IoT)이나 인공지능(AI) 단계까지 발전을 꾀하고 있다.
국내 농림업분야는 빅데이터 활용을 시도하고 있으나 아직 초기단계이다. 빅데이터 활용 수준이 낮은 주요 이유는 빅데이터 활용에 대한 이해도가 낮으며 데이터 수집 및 분석에 어려움이 있기 때문이다.
국내 농림업분야도 타산업분야 및 외국 수준에 맞추어 빅데이터를 활용도를 높일 수 있는 방안이 필요하다. 이러한 배경에서 본 연구는 농림업분야의 빅데이터 활용 현황 및 실태, 국내외 활용사례 분석, 빅데이터 수요조사를 통해 빅데이터 활용도를 제고할 수 있는 방안을 제시하기 위해 수행하였다.
Background of Research
In today’s world of business, there has been a tremendous interest in developing the uses of Big Data to create new values. The domestic agricultural sector has also tried to apply Big Data in political decision-making and conducting research projects. However, Big Data application in the agriculture sector is still at an early stage compared to other industries in the country. Several developed countries such as the US and Japan have widely been applying Big Data in the agriculture sector to help in agricultural decision-making. Thus, there is a need to enhance the application of Big Data in the agricultural sector to provide more valuable information to decision makers in agriculture and other industries. A comprehensive analysis of data, not only from the agriculture sector but also from the massive amount of transactions being accumulated in real-time and from other departments and private sectors is needed.
The purpose of this study is to identify the current conditions and utilization of Big Data in the agricultural sector and to propose solutions for a viable use of Big Data by analyzing both domestic and overseas practical application examples of Big Data, and investigation of the demand for Big Data utilization.
Method of Research
The research methods included the examination of related literature and materials, an online search for both domestic, and overseas practical uses of Big Data, and evaluation for each indicator. These were used to categorize both national and foreign examples of practical use of Big Data, and to diagnose the level of domestic utilization compared to that used overseas, and to propose directions. Also, a survey was conducted of 158 people including Big Data experts, agricultural information experts, industrial experts, and policy makers among others to investigate demands for Big Data utilization in the agricultural industry. At the same time, the important elements for the effect of latent demand were drawn by Ordered Logit model assumption based on the results of the survey. Furthermore, restrictions and improvement for the agriculture industry, an improvement plan for Big Data utilization, an in-depth analysis of the practical use of Big Data were concluded by a thorough analysis of findings from the survey of the experts.
Results and Implications
The significant restrictions for utilization of Big Data include low quality of data, insufficient data and difficulties gathering it, and lack of development of Big Data for the domestic agriculture industry. It is necessary to set the target for uses of Big Data in the domestic agriculture sector similar to that in the field of IoT or AI and to establish cloud-based infrastructure for farming management consulting, information on crop volume, production history, and environment management.
To improve the understanding of Big Data usage, first, it is necessary to systematically promote the concept, its practical use, necessity, and value of use by expanding the education and sensitization of Big Data utilization. To achieve this understanding, it is necessary to publish and distribute case studies of Big Data uses that include examples of practical use and application in the sector. Secondly, when considering the idea whether to conduct analysis depending on the purpose of the data’s use, it will require many trials of scenarios of Big Data analysis for enhancing uses of Big Data and promoting Big Data to the public. Finally, by investigating and benchmarking the examples of practical use of Big Data in other domestic industries and overseas utilization, the outcome of practical use for Big Data can be increased. Business entities may benchmark examples of the U.S.A’s C3 and C8, and Japan’s D2, D3, and D7. For improvement of productivity, one may benchmark Japan’s D1, D2, D3, and D4, and other domestic industries’ B1, B3, and B4 for the marketing sector.
To increase the possibility of successful analysis of Big Data, restrictions on expansion on open and sharing data, improvement of data quality, and data utilization must be improved. Firstly, one may benchmark district level (e.g., Si, Gun, and Gu) statistical data, examples of their use, examples for practical use of the National Statistical Office (NSO)’s data center; examples of expansion of practical use by technical process for data in order to expand open and sharing data. Also, the government needs to improve appropriate systems actively. Secondly, it is necessary to standardize data and newly establish and correct missing data to improve the data quality. Finally, the difficulties of gathering data may be solved by providing or utilizing data by deleting personal identification information and doing this with related associations or producer groups rather than accessing it individually. If one tries to gather and analyze Big Data, the utilization will increase significantly when a professional institute provides services such as Big Data gathering and provision and Big Data analysis.
The measures to improve utilization of Big Data in the agriculture sector can be classified into the following categories: education and promotion for improving utilization of Big Data; expansion of open and sharing data system; improvement of data quality, and systems development. To achieve this growth, the government, public entities, and private organizations need to work together cooperatively. Most of all, the government needs to play an important leading role in education and promotion to improve utilization of Big Data; in developing the system for expansion of open and sharing data system; in data management for upgrading, standardization, and consistency of Big Data; and in the establishment and operation of Big Data dedicated institutes. This approach will achieve improved understanding of the utilization of Big Data.
To improve expertise in utilizing data in the agriculture industry, a Big Data special organization must be created. Since data from the agriculture industry differs from that of other industries regarding quality attributes and uniformity, an expert institution, which specializes in the agriculture sector, is needed. This dedicated organization needs to gather Big Data, establish an open system for sharing, prepare guidelines for open data of public entities, and enhance integration with national-level Big Data-holding organizations. To improve utilization of Big Data, systems improvement and amendment of regulations need to be conducted by reviewing and revising laws and regulations which restrict open data sharing.
The public sectors need to play an important role in improving utilization of Big Data through gradually collaborating with other areas such as research and business to expand the data scope and extent of sharing. Public entities should actively cooperate with governments to establish new policies for developing information standards and data sharing. The roles of the private sectors in understanding the needs of Big Data analysis, and acknowledging several challenges in Big Data analysis should be defined. The National Agricultural Cooperative Federation and farming households should identify the importance of Big Data utilization, and find the value from data sharing.
Since usage of Big Data in the domestic agriculture industry is at an early stage, improvement of the utilization value of Big Data and potential consumers' understanding of it are required. Mid and long-term plans targeting the level of parity with IoT and AI should be established and supported to invigorate the utilization of Big Data, in the long run.
Researchers: Kim Kyungphil, Koo Jachoon, An Hyunjin and Han Junghoon
Research Period: 2016. 1. ~ 2016. 10.
E-mail address: email@example.com