Big Data Analytics Maturity Model for SMEs

Matthew Willetts, Anthony S. Atkins
{"title":"Big Data Analytics Maturity Model for SMEs","authors":"Matthew Willetts, Anthony S. Atkins","doi":"10.5815/ijitcs.2024.02.01","DOIUrl":null,"url":null,"abstract":"Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability and increased efficiency and a survey in 2017 of 50 Fortune 1000 and leading firms’ executives indicated that 48.4% of respondents confirmed they are achieving measurable results from their Big Data investments, with 80.7% confirming that they have generated business. Big Data Analytics is adopted by only 10% of SMEs. The paper outlines a review of Big Data Maturity Models and discusses their positive features and limitations. Previous research has analysed the barriers to adoption of Big Data Analytics in SMEs and a scoring tool has been developed to help SMEs adopt Big Data Analytics. The paper demonstrates that the scoring tool could be translated and compared to a Maturity Model to provide a visual representation of Big Data Analytics maturity and help SMEs to understand where they are on the journey. The paper outlines a case study to show a comparison to provide intuitive visual model to assist top management to improve their competitive advantage.","PeriodicalId":130361,"journal":{"name":"International Journal of Information Technology and Computer Science","volume":"148 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Technology and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5815/ijitcs.2024.02.01","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability and increased efficiency and a survey in 2017 of 50 Fortune 1000 and leading firms’ executives indicated that 48.4% of respondents confirmed they are achieving measurable results from their Big Data investments, with 80.7% confirming that they have generated business. Big Data Analytics is adopted by only 10% of SMEs. The paper outlines a review of Big Data Maturity Models and discusses their positive features and limitations. Previous research has analysed the barriers to adoption of Big Data Analytics in SMEs and a scoring tool has been developed to help SMEs adopt Big Data Analytics. The paper demonstrates that the scoring tool could be translated and compared to a Maturity Model to provide a visual representation of Big Data Analytics maturity and help SMEs to understand where they are on the journey. The paper outlines a case study to show a comparison to provide intuitive visual model to assist top management to improve their competitive advantage.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
中小企业大数据分析成熟度模型
中小企业是全球经济的支柱,占所有企业的 90%。尽管大企业广泛采用大数据,并报告了包括提高盈利能力和效率在内的众多好处,2017 年对 50 家财富 1000 强企业和领先企业高管的调查显示,48.4% 的受访者确认他们正在从大数据投资中取得可衡量的成果,80.7% 的受访者确认他们已经创造了业务。只有 10% 的中小企业采用了大数据分析技术。本文概述了大数据成熟度模型,并讨论了这些模型的积极特点和局限性。先前的研究分析了中小企业采用大数据分析的障碍,并开发了一个评分工具,以帮助中小企业采用大数据分析。本文表明,该评分工具可转化为成熟度模型并与之进行比较,以提供大数据分析成熟度的可视化表示,帮助中小企业了解其所处的阶段。论文概述了一个案例研究,通过比较来提供直观的可视化模型,帮助高层管理人员提高竞争优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Enhancing Healthcare Provision in Conflict Zones: Queuing System Models for Mobile and Flexible Medical Care Units with a Limited Number of Treatment Stations A Machine Learning Based Intelligent Diabetic and Hypertensive Patient Prediction Scheme and A Mobile Application for Patients Assistance Mimicking Nature: Analysis of Dragonfly Pursuit Strategies Using LSTM and Kalman Filter Securing the Internet of Things: Evaluating Machine Learning Algorithms for Detecting IoT Cyberattacks Using CIC-IoT2023 Dataset Analyzing Test Performance of BSIT Students and Question Quality: A Study on Item Difficulty Index and Item Discrimination Index for Test Question Improvement
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1