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Autor(en):

Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Built Environment Project and Asset Management, , n. 4, v. 9
Seite(n): 503-514
DOI: 10.1108/bepam-07-2018-0098
Abstrakt:

Purpose

Today, Big Data plays an imperative role in the creation, maintenance and loss of cyber assets of organisations. Research in connection to Big Data and cyber asset management is embryonic. Using evidence, the purpose of this paper is to argue that asset management in the context of Big Data is punctuated by a variety of vulnerabilities that can only be estimated when characteristics of such assets like being intangible are adequately accounted for.

Design/methodology/approach

Evidence for the study has been drawn from interviews of leaders of digital transformation projects in three organisations that are within the insurance industry, natural gas and oil, and manufacturing industries.

Findings

By examining the extant literature, the authors traced the type of influence that Big Data has over asset management within organisations. In a context defined by variability and volume of data, it is unlikely that the authors will be going back to restricting data flows. The focus now for asset managing organisations would be to improve semantic processors to deal with the vast array of data in variable formats.

Research limitations/implications

Data used as evidence for the study are based on interviews, as well as desk research. The use of real-time data along with the use of quantitative analysis could lead to insights that have hitherto eluded the research community.

Originality/value

There is a serious dearth of the research in the context of innovative leadership in dealing with a threatened asset management space. Interpreting creative initiatives to deal with a variety of risks to data assets has clear value for a variety of audiences.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1108/bepam-07-2018-0098.
  • Über diese
    Datenseite
  • Reference-ID
    10367230
  • Veröffentlicht am:
    27.08.2019
  • Geändert am:
    27.08.2019
 
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