建立共同的数据驱动文化:对大型公共部门机构在数据驱动过程中出现的紧张局势和新解决方案的纵向研究

IF 3.7 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
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引用次数: 0

摘要

近年来,为推动组织数据化,私营和公共部门都开展了以数据为重点的软件项目。数据驱动项目的不断增加带来了一系列新的社会技术挑战,但迄今为止,关于数据驱动项目如何影响在数据需求和使用方面存在显著差异的大型组织的实证研究却寥寥无几。本研究以纵向描述性案例研究的形式,介绍了挪威公共部门的数据驱动计划如何在一个非常庞大、复杂的组织中造成组织紧张局势。在18个月的时间里,我们进行了32次半结构式访谈,这些访谈代表了该组织中两个不同的数据密集型部门,它们已经形成了互不兼容的数据文化。我们的研究表明,这些文化差异造成了组织冲突,阻碍了数据驱动计划的实施。不过,研究结果也表明,通过自上而下的战略推动,在统一数据原则的基础上建立共同的数据驱动文化,是有可能克服这些冲突的,进而有可能改进决策和加强创新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a common data-driven culture: A longitudinal study of the tensions and emerging solutions involved in becoming data-driven in a large public sector organization

In recent years, the push to make organizations data-driven has led to data-focused software projects, both in the private and public sectors. The strive for increasing data-driven initiatives introduces a range of new socio-technical challenges, yet there are to date few empirical studies in terms of how data-focused initiatives affect large organizations with significant variations in terms of data needs and usage. This study presents a longitudinal descriptive case study of how data-driven initiatives in the Norwegian public sector cause organizational tensions in a very large, complex organization. We conducted 32 semi-structured interviews over a period of 18 months representing two different data-intensive parts of the organization that had developed incompatible data cultures. Our study shows that these cultural differences create organizational conflicts that hinder data-driven initiatives. The findings also suggest, however, that overcoming these is possible through the strategic, top-down facilitation of a common data-driven culture built on uniting data principles, in turn potentially leading to improved decision-making and enhanced innovation.

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来源期刊
Journal of Systems and Software
Journal of Systems and Software 工程技术-计算机:理论方法
CiteScore
8.60
自引率
5.70%
发文量
193
审稿时长
16 weeks
期刊介绍: The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to: • Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution • Agile, model-driven, service-oriented, open source and global software development • Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems • Human factors and management concerns of software development • Data management and big data issues of software systems • Metrics and evaluation, data mining of software development resources • Business and economic aspects of software development processes The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.
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