• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1117-1132
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• 2019

Recently, IT technology such as internet, mobile, and IOT has rapidly developed, making it easy to collect data necessary for business, and the collected data is analyzed as a new method of big data analysis and used appropriately for business. In this way, data collection and analysis becomes easy. In such data, personal information including an identifier such as a sensor id, a device number, IP address, or the like may be collected. However, if systematic management is not accompanied by collecting and disposing of large-scale data, violation of relevant laws such as "Personal Data Protection Act". Furthermore, data quality problems can also occur and make incorrect decisions. In this paper, we propose a new data governance maturity model(DGMM) that can identify the personal data contained in the data collected by companies, use it appropriately for the business, protect it, and secure quality. And we also propose a over all implementation process for DG Program.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.8
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• pp.323-330
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• 2019

In order to improve the level of the IT organization, CMMi played a lot of roles in improving the IT efficiency and quality step by step. In a university organization if you use a customized IT governance framework for the concept of CMMi, it will be a university IT governance to realize the vision of the university. In this paper, we propose Staged Framework for the university developed for applying to university with reference to this staged model of CMMi. The university has applied its own process step by step and it can be uses in the university field. So it can be applied to university IT plan and budget in compliance with not only the person in charge of IT service center but the person in charge of university headquarters. The staged framework is classified into the maturity and process of university IT project and suggests ways to apply it to the improvement of university IT system levels.