Foresights for big data across industries

IF 2.3 Q3 REGIONAL & URBAN PLANNING Foresight Pub Date : 2022-02-22 DOI:10.1108/fs-02-2021-0059
F. Almeida
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引用次数: 2

Abstract

Purpose The purpose of this study is to explore the potential and growth of big data across several industries between 2016 and 2020. This study aims to analyze the behavior of interest in big data within the community and to identify areas with the greatest potential for future big data adoption. Design/methodology/approach This research uses Google Trends to characterize the community’s interest in big data. Community interest is measured on a scale of 0–100 from weekly observations over the past five years. A total of 16 industries were considered to explore the relative interest in big data for each industry. Findings The findings revealed that big data has been of high interest to the community over the past five years, particularly in the manufacturing, computers and electronics industries. However, over the 2020s the interest in the theme decreased by more than 15%, especially in the areas where big data typically had the greatest potential interest. In contrast, areas with less potential interest in big data such as real estate, sport and travel have registered an average growth of less than 10%. Originality/value To the best of the author’s knowledge, this study is original in complementing the traditional survey approaches launched among the business communities to discover the potential of big data in specific industries. The knowledge of big data growth potential is relevant for players in the field to identify saturation and emerging opportunities for big data adoption.
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跨行业的大数据前瞻
目的本研究的目的是探索2016年至2020年间几个行业大数据的潜力和增长。本研究旨在分析社区内对大数据感兴趣的行为,并确定未来大数据采用潜力最大的领域。设计/方法论/方法这项研究使用谷歌趋势来描述社区对大数据的兴趣。根据过去五年的每周观察,社区兴趣以0-100为尺度进行衡量。共考虑了16个行业来探索每个行业对大数据的相对兴趣。调查结果显示,在过去五年里,大数据一直受到社会的高度关注,尤其是在制造业、计算机和电子行业。然而,在20世纪20年代,人们对这个主题的兴趣下降了15%以上,尤其是在大数据通常具有最大潜在兴趣的领域。相比之下,对房地产、体育和旅游等大数据不太感兴趣的领域的平均增长率不到10%。原创性/价值据作者所知,这项研究是对商业界为发现大数据在特定行业的潜力而推出的传统调查方法的补充。大数据增长潜力的知识与该领域的参与者相关,以确定大数据采用的饱和和新兴机会。
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来源期刊
Foresight
Foresight REGIONAL & URBAN PLANNING-
CiteScore
5.10
自引率
5.00%
发文量
45
期刊介绍: ■Social, political and economic science ■Sustainable development ■Horizon scanning ■Scientific and Technological Change and its implications for society and policy ■Management of Uncertainty, Complexity and Risk ■Foresight methodology, tools and techniques
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