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    Intelligence aspects of big data analytics for Kenya national security

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    Full-text thesis (2.078Mb)
    Date
    2020-11
    Author
    Njoroge, Ann Wangechi
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    Abstract
    In the age of rapid technological advancement, the range of threats to national security have evolved, becoming more complex and diverse, while the duty of the government remains that of securing its country. With the exponential rise in amount of structured, semi-structured and unstructured data generated every day, big data has become a core competence for the government which is linked to national security and the operations of the intelligence community. Big data provides intelligence organizations the opportunity to increase their investigative capabilities to combat threats to national security by enabling them to collect, analyze and disseminate information at a pace which could not be as effective in the traditional era. Governments will however be faced with the challenge of developing new capabilities to exploit and manage big data, which will require a rigorous review of the existing intelligence models and processes. The aim of this study is to examine the scope of applicability of big data and analytics in the functioning of the intelligence community, with a focus on the intelligence cycle. It also looks at the ethical and technical issues that limit the use of big data and analytics for national security. An exploratory research design was used to provide insight from the national security organs and intelligence community in Kenya on the applicability of Big Data and Analytics for national security. The study established that big data and analytics have a statistically significant effect on the intelligence cycle and national security organizations should embrace this new technology since it provides a lot of actionable insights. Based on this study it is recommended that the government enact more legislation to help develop an efficient and effective policy infrastructure for the various stakeholders in the intelligence community.
    URI
    http://hdl.handle.net/11071/12174
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    • MDIS Theses and Dissertations (2020) [8]

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