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فرصتها و چالشهای سیاستگذاری حکمرانی داده در معاونت علمی، فناوری و اقتصاد دانشبنیان ریاستجمهوری | ||
| مدیریت راهبردی دانش سازمانی | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 15 تیر 1405 | ||
| نوع مقاله: مقاله پژوهشی با اصالت | ||
| شناسه دیجیتال (DOI): 10.47176/SMOK.2026.2028 | ||
| نویسندگان | ||
| زهرا فقیهی1؛ نسترن پورصالحی2؛ علیرضا نوروزی* 2 | ||
| 1دانشجوی کارشناسی ارشد مدیریت اطلاعات، گروه علم اطلاعات و مدیریت دانش، دانشکده مدیریت دولتی و علوم سازمانی، دانشکدگان مدیریت، | ||
| 2گروه علم اطلاعات و مدیریت دانش، دانشکده مدیریت دولتی و علوم سازمانی، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران | ||
| تاریخ دریافت: 04 خرداد 1405، تاریخ بازنگری: 28 خرداد 1405، تاریخ پذیرش: 12 تیر 1405 | ||
| چکیده | ||
| هدف: شناسایی، تعیین و اولویتبندی فرصتها و چالشهای سیاستگذاری حکمرانی داده در معاونت علمی، فناوری و اقتصاد دانشبنیان ریاستجمهوری. روش: پژوهش پیمایشی با رویکرد کمّی است. مؤلفهها و گویهها از طریق مطالعه کتابخانهای و اسنادی شناسایی و سپس پرسشنامه محققساخته میان مدیران، متخصصان داده و کارکنان فناوری اطلاعات مرکز همکاریهای تحول و پیشرفت (وابسته به معاونت علمی، فناوری و اقتصاد دانشبنیان ریاستجمهوری) توزیع شد. دادهها با استفاده از آزمونهای ناپارامتریک فریدمن، اسپیرمن، یومان ویتنی و کروسکال والیس در نرمافزار SPSS تحلیل گردید. یافتهها: ده مؤلفه اصلی سیاستگذاری حکمرانی داده مبتنی بر مدل انجمن مدیریت داده شناسایی شد. مؤلفه «مدلسازی و طراحی دادهها» همزمان پُرفرصتترین و پُرچالشترین مؤلفه بود. در بخش فرصتها، هفت مؤلفه وضعیت مطلوب و سه مؤلفه وضعیت نسبتاً مطلوب داشتند. در بخش چالشها، هفت مؤلفه وضعیت نامطلوب و دو مؤلفه وضعیت نسبتاً مطلوب داشتند. آزمون اسپیرمن نشان داد مؤلفهها با یکدیگر ارتباط معنادار و درهمتنیده دارند. متغیرهای جمعیتشناختی (جنسیت، سن، تحصیلات، رشته، سمت، سابقه کاری) تأثیر معناداری بر مؤلفهها نداشتند. بحث: مؤلفه «مدلسازی و طراحی دادهها» همزمان پُرفرصتترین و پُرچالشترین مؤلفه بود و باید برای رفع آن سیاستگذاری و برنامهریزی داشت. همچنین برای سایر مؤلفهها که در بخش فرصتها در شرایط مطلوب هستند باید یک سیاستگذاری جدی داشت تا فرصتهای آن تقویت شوند. در بخش چالشها مؤلفههایی که در وضعیت نسبتاً مطلوب قرار دارند و وضعیت آنها مناسبتر از مؤلفهها با وضعیت نامطلوب است و باید برای رفع آنها برنامهریزی شکل بگیرد. نتیجهگیری: مدلسازی و طراحی دادهها به عنوان محور سیاستگذاری حکمرانی داده نیازمند توجه فوری است. پیشنهاد میشود معاونت منتخب الگوی سیاستگذاری مبتنی بر اولویتبندی مؤلفهها طراحی کرده و تمرکز خود را بر رفع چالشهای مدلسازی دادهها و تقویت فرصتهای آن معطوف نماید. | ||
| کلیدواژهها | ||
| حکمرانی داده؛ سیاستگذاری حکمرانی داده؛ فرصتهای سیاستگذاری حکمرانی داده؛ چالشهای سیاستگذاری حکمرانی داده؛ معاونت علمی؛ فناوری و اقتصاد دانشبنیان ریاستجمهوری | ||
| عنوان مقاله [English] | ||
| Opportunities and Challenges of Data Governance Policy-Making in the Vice Presidency for Science, Technology and Knowledge-Based Economy | ||
| نویسندگان [English] | ||
| Zahra Faghihi1؛ Nastaran Poursalehi2؛ Alireza Noruzi2 | ||
| 1Department of Information Science and Knowledge Management, Faculty of Public Administration and Organizational Science, College of Management, University of Tehran, Tehran, Iran | ||
| 2Department of Information Science and Knowledge Management, Faculty of Public Administration and Organizational Science, College of Management, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Purpose: To utilize data governance, one must first attend to data governance policy‑making. Before drafting and designing a data governance policy, the opportunities and challenges of such policy‑making should be formulated according to the organization’s culture and data ecosystem, and the policy should then be developed with these opportunities and challenges in mind. Examining the opportunities and challenges of data governance policy‑making prior to its implementation enables the anticipation of the outcomes of data governance use and minimizes deficiencies during its execution. Given its specific circumstances, the selected Vice Presidency must also first engage in data governance policy‑making and, as a first step, identify and investigate the opportunities and challenges of this policy‑making. Since the selected Vice Presidency comprises numerous affiliated headquarters and institutions, each with its own distinct structure and culture, a single, uniform data governance policy cannot be designed for all of them. Consequently, the Office of Transformation and Progress Cooperation, one of the institutions affiliated with this Vice Presidency and the one most similar to it, was chosen as the case study for the present research. Accordingly, the main objective of this study is to identify, determine, and prioritize the opportunities and challenges of data governance policy‑making at the Center for Transformation and Progress Cooperation, affiliated with the Vice Presidency for Science, Technology and Knowledge‑Based Economy. Methodology: This survey-based research is quantitative in terms of method and has an applied orientation. Its paradigm is positivism, and in terms of objective, it is a descriptive‑analytical study. Its strategy is also survey‑based. In this research, data collection was carried out through documentary (library) and field studies. Documentary study was used to review the theoretical foundations and gather data. The data and information presented in this research were obtained from two different sources. Primary data were collected through a questionnaire, while secondary data were obtained through the study of books, articles, dissertations, and other texts related to the opportunities and challenges of data governance policy‑making. The questionnaire of the present study was designed in four sections. The first section contains demographic questions; the second section measures the opportunities of data governance policy‑making across 10 components and 47 items; the third section measures the challenges of data governance policy‑making across 9 components and 53 items; and the final section assesses the influence of each component on the others. The main components of the questionnaire were extracted from the Data Management Association (DAMA) Body of Knowledge framework. Since the researcher‑developed questionnaire aims to examine and identify the opportunities and challenges of data governance policy‑making, the word “policy” is prefixed to each component to indicate their relevance to policy‑making. The components of the questionnaire include: data architecture policies, data modeling and design policies, data storage policies, data security policies, master and reference data management policies, metadata management policies, document and content management policies, data integration policies, data quality policies, and data lifecycle management policies, each consisting of different items. Typically, researcher‑developed instruments such as the questionnaire used in this study lack adequate validity and reliability; therefore, the researcher first sought to ensure their validity and reliability. To assess content and face validity, the questionnaire items were presented to supervisors, advisors, and data and data governance specialists, and the necessary revisions were made. Ultimately, the experts’ judgment on validity was positive. Thus, it can be expected that the instrument used in this study has sufficient content validity. To assess reliability, all questionnaires were distributed among members of the statistical population and completed by them; then the data were entered into SPSS software and Cronbach’s alpha was calculated. The Cronbach’s alpha coefficients for all components of the opportunities and challenges sections were above 0.7, indicating that the reliability of the evaluated items was confirmed. The statistical population of the present study consists of managers and data specialists at the Center for Transformation and Progress Cooperation, an institution affiliated with the Vice Presidency for Science, Technology and Knowledge‑Based Economy. The reason for selecting this center as the statistical population was the similarity of its activities to those of the selected Vice Presidency, as well as the greater feasibility of coordination and collaboration between this center and the researcher. In this study, due to the limited number of population members, no sampling was performed, and all managers and data specialists were surveyed. The number of participants in this study is 55. Results: The results indicate that the main components of data governance policy‑making include data architecture, data modeling and design, data storage, data security, master and reference data management, metadata management, document and content management, data integration, data quality, and data lifecycle management. These components were derived from the Data Management Association (DAMA) model. Each component encompassed both opportunities and challenges. Statistical tests revealed that the data modeling and design component was simultaneously identified as both an opportunity‑generating and challenge‑inducing factor. To examine the relationship among the components of data governance policy‑making in the opportunities and challenges sections, Spearman’s correlation test was used because all components were non‑normally distributed. In the opportunities section, the data security component was identified as a central hub, showing strong correlations with data modeling and design, data storage, master and reference data management, and data architecture. Metadata management also played a key role in effectively relating to data architecture, data storage, document and content management, and data quality. Furthermore, document and content management was associated with many dimensions, including data modeling and design, data integration, data architecture, master and reference data management, and metadata management. These correlations indicated that, within the research data, data quality, data security, data architecture, metadata management, and document and content management were highly aligned with one another. Data integration also served as a bridging link between the components of data modeling and design, data storage, and data quality. In the challenges section, some components exhibited a network structure and played a pivotal role. The strongest relationships were observed between data integration and data quality, as well as between data security and master and reference data management, reflecting the interdependence of the control and infrastructural dimensions of data governance. Moreover, the significant relationship between data architecture and data modeling and design indicated that structural policies underpin logical data design. In contrast, metadata management and data lifecycle management lacked significant correlations with other components, which may indicate an institutional weakness in linking these dimensions to other data governance mechanisms. Focusing data governance policy‑making challenges on data integration, security, and architecture had a direct and effective impact on other dimensions of data governance. Discussion: The use of non‑parametric Mann‑Whitney and Kruskal‑Wallis tests, due to the non‑normality of the data, showed that demographic variables such as gender, age, education, field of study, organizational position, and work experience have no effect on the components of opportunities and challenges in data governance policy‑making. Furthermore, the prioritization of the components of opportunities and challenges indicated that the data modeling and design component is simultaneously the most opportunity‑rich and the most challenge‑rich component of data governance policy‑making. Spearman’s test was used to examine the interrelationships among the components, which revealed that the components are interconnected and intertwined. Conclusion: By identifying the components of opportunities and challenges in data governance policy‑making, it is possible to assist governmental institutions, organizations, and companies seeking data governance and its policy‑making in the future, and to clarify the path for them. The results of this study, along with the simultaneous examination of the opportunities and challenges of data governance policy‑making given the identical nature of the components, indicate that each component can concurrently entail opportunities and advantages or challenges and difficulties. This simultaneity signifies the importance and the degree of influence of each component in data governance policy‑making. Each component is like a double‑edged sword: if it remains in balance, it is highly beneficial, but if it falls out of balance, it can become challenge‑creating and problematic. The data modeling and design component has been identified simultaneously as the most opportunity‑rich and the most challenge‑rich component, underscoring its high significance for the selected Vice Presidency. All components of data governance policy‑making influence one another; if an opportunity arises, the Vice Presidency progresses, whereas the emergence of unresolved challenges leads to regression and difficulties. It is advisable for the Vice Presidency for Science, Technology and Knowledge‑Based Economy to develop a data governance policy‑making model, to address the mutual effects of these components in an appropriate policy, and to focus the greatest effort on resolving challenges and reinforcing opportunities. Regarding the limitations of the present study, it should be noted that this research was conducted within a single governmental institution with a limited statistical population, so the generalizability of its findings to other institutions is very low. Another limitation of this study was the lack of access to data governance policy‑making documents from other governments and countries, which introduced certain complexities. This limitation also existed domestically. | ||
| کلیدواژهها [English] | ||
| data governance؛ data governance policy‑making؛ policy‑making؛ opportunities and challenges of data governance policy‑making؛ Vice Presidency for Science, Technology and Knowledge‑Based Economy | ||
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