揭开流程挖掘数据管理的神秘面纱:德尔菲研究的启示

IF 8.2 2区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Information & Management Pub Date : 2024-05-07 DOI:10.1016/j.im.2024.103973
Kanika Goel , Niels Martin , Arthur ter Hofstede
{"title":"揭开流程挖掘数据管理的神秘面纱:德尔菲研究的启示","authors":"Kanika Goel ,&nbsp;Niels Martin ,&nbsp;Arthur ter Hofstede","doi":"10.1016/j.im.2024.103973","DOIUrl":null,"url":null,"abstract":"<div><p>Data governance is recognised as a new capability for organisations to maximize the value of data. Process mining is essential for the resilient growth of businesses, making process data a strategic asset for organisations. Even though the availability of reliable process data is vital for obtaining dependable insights into process mining techniques, there exists no framework that explains how to govern process data holistically. We address this gap by presenting the first data governance framework for process mining that was derived from a Delphi study conducted with a panel of academics and practitioners from around the world. The framework provides multiple avenues for future research.</p></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"61 5","pages":"Article 103973"},"PeriodicalIF":8.2000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378720624000557/pdfft?md5=91cc6c706f5cfbee767513d9c2592c17&pid=1-s2.0-S0378720624000557-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Demystifying data governance for process mining: Insights from a Delphi study\",\"authors\":\"Kanika Goel ,&nbsp;Niels Martin ,&nbsp;Arthur ter Hofstede\",\"doi\":\"10.1016/j.im.2024.103973\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Data governance is recognised as a new capability for organisations to maximize the value of data. Process mining is essential for the resilient growth of businesses, making process data a strategic asset for organisations. Even though the availability of reliable process data is vital for obtaining dependable insights into process mining techniques, there exists no framework that explains how to govern process data holistically. We address this gap by presenting the first data governance framework for process mining that was derived from a Delphi study conducted with a panel of academics and practitioners from around the world. The framework provides multiple avenues for future research.</p></div>\",\"PeriodicalId\":56291,\"journal\":{\"name\":\"Information & Management\",\"volume\":\"61 5\",\"pages\":\"Article 103973\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0378720624000557/pdfft?md5=91cc6c706f5cfbee767513d9c2592c17&pid=1-s2.0-S0378720624000557-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information & Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378720624000557\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information & Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378720624000557","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

数据管理被认为是企业最大限度地利用数据价值的一种新能力。流程挖掘对企业的恢复性增长至关重要,使流程数据成为企业的战略资产。尽管可靠流程数据的可用性对于获得流程挖掘技术的可靠见解至关重要,但目前还没有一个框架能解释如何全面管理流程数据。为了填补这一空白,我们提出了首个流程挖掘数据管理框架,该框架是由来自世界各地的学者和从业人员组成的小组通过德尔菲研究得出的。该框架为未来研究提供了多种途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Demystifying data governance for process mining: Insights from a Delphi study

Data governance is recognised as a new capability for organisations to maximize the value of data. Process mining is essential for the resilient growth of businesses, making process data a strategic asset for organisations. Even though the availability of reliable process data is vital for obtaining dependable insights into process mining techniques, there exists no framework that explains how to govern process data holistically. We address this gap by presenting the first data governance framework for process mining that was derived from a Delphi study conducted with a panel of academics and practitioners from around the world. The framework provides multiple avenues for future research.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Information & Management
Information & Management 工程技术-计算机:信息系统
CiteScore
17.90
自引率
6.10%
发文量
123
审稿时长
1 months
期刊介绍: Information & Management is a publication that caters to researchers in the field of information systems as well as managers, professionals, administrators, and senior executives involved in designing, implementing, and managing Information Systems Applications.
期刊最新文献
Cutting corners as a coping strategy in information technology use: Unraveling the mind's dilemma Cybersecurity end-user compliance: Password management versus update compliance Towards new frontiers: How attainment discrepancy affects exploratory behavior in crowdfunding What drives users to tip? The impact of contributor experience, content length, and content type on online video sharing platforms An ensemble deep learning model for fast classification of Twitter spam
×
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