چارچوبی برای تحلیل حکمرانی داده در سطح ملی با استفاده از رویکرد فراترکیب

هدف اصلی پژوهش حاضر شناسایی مولفه‌های چارچوب زیست‌بوم حکمرانی داده در سطح ملی است. با وجود مطالعات متعددی که در زمینه حکمرانی داده انجام شده‌است. بااین‌حال بیشتر مطالعات در سطح سازمان بوده و مطالعات محدودی به بررسی زیست‌بوم حکمرانی داده در سطح ملی پرداخته‌اند؛ لذا در پژوهش حاضر به‌منظور شناسایی مولفه‌های چارچوب زیست‌بوم حکمرانی داده با روش مرور نظام‌مند و از طریق جستجو در پایگاه web of knowledge، در بازه زمانی 2020-2010،  تعداد 1803 سند استخراج شد که پس از بررسی‌های لازم 65 مورد از آن‌ها به‌عنوان اسناد مرتبط و معتبر انتخاب شدند. در ادامه با استفاده از رویکرد فراترکیب و کدگذاری اسناد منتخب، مولفه‌های چارچوب زیست‌بوم حکمرانی داده شناسایی شدند. در نتایج پژوهش 13 مولفه شناسایی شدند که شامل؛ مولفه‌های پایش و ارزیابی عملکرد، استانداردها، تامین مالی، چرخه عمر داده، زیرساخت‌ و فناوری، فراداده، فرهنگ، نقش‌ها و مسئولیت‌ها، کیفیت داده، امنیت و حفظ حریم خصوصی، آموزش و ترویج، سیاست‌ها و قوانین و اصول داده است. همچنین در طی بررسی اسناد منتخب، مجموعه نقش‌های اصلی زیست‌بوم حکمرانی داده شناسایی شدند که از جمله آن‌ها می‌توان به نقش‌هایی مانند تولیدکننده داده، کاربر داده، سیاستگذار، بازیگر مرکزی، مشاور داده اشاره کرد. از طرف دیگر چرخه عمر  داده نیز شامل مراحل مختلفی مانند خلق داده، ذخیره‌سازی داده، پردازش داده، حذف و امحاء داده است.

a framework for analyzing data governance at the national level using meta-synthesis

due to advances in information and communication technologies such as artificial intelligence and the internet of things, the volume of data has increased significantly in recent years. with increasing data volume, proper management is needed to maximize the value of this data. given that multiple actors are involved in the process of producing, delivering, using, and analyzing data, the ecosystem approach is appropriate to study data governance. in this study, in order to identify the components of the data governance ecosystem framework by systematic review method (meta synthesis approach), 1803 documents were extracted through a search in the web of science database where 65 of them were identified as relevant documents and were reviewed and coded. finally, the extracted codes were divided into 2 categories, which were related to data governance characteristics, data governance components, data ecosystem actors and roles of actors in the data ecosystem, respectively. among the identified components are data life cycle, data standard, data quality, metadata, etc.introductionsince data was introduced as a valuable asset, the issue of data governance has emerged as an emerging issue in the field of information systems. data governance is also a promising approach for institutions to improve and maintain data quality and use. given that several actors are involved in the process of production, supply, use, analysis, etc. of data; and considering that the weakness of each component and actors in the data governance space can lead to problems and poor performance of other components, taking an ecosystem approach to analyze data governance is appropriate. although the data ecosystem has become important, research on it is still in the early stages of development. in addition, in a few studies conducted in this field, some components have been mentioned that reflect a small part of the whole. it should also be noted that the issue of data governance depends on the context. therefore, the results of research conducted in different contexts are not applicable to each other. for example, the contexts of developing and developed countries differ in many respects in terms of data. these differences include low data volume, cultural weakness in relation to data, poor access to data, and so on. the present study seeks to provide a framework for analyzing data governance at the national level, due to the gap in the literature.theoretical frameworkthe concept of data governance has evolved over the past decade and since data was introduced as a valuable asset and has been seriously considered at the level of organizations and governments. that is why it is stated that the most important driver of data governance is paying attention to data as a valuable asset. data governance focuses on who has the decision-making power over data assets to ensure the quality, stability, usability, security, privacy, and availability of data. also in another definition, data governance exercises the exercise of joint authority, control, and decision-making (planning, oversight, and execution) over asset management. it includes a set of processes to improve data compatibility and accuracy, reduce data management costs, and increase security for data access; defines maps and determines accountability for decision areas and the activities of these maps; and the framework of decision-making and accountability rights to encourage desirable behaviors in the use of data. finally, in another definition, data governance is defined as a framework for decision-making and accountability rights in order to encourage desirable behaviors in the use of data.materials and methods1803 documents were extracted by systematic review method through search in web of science database, 65 of which were identified as relevant documents and coded using meta-synthesis method.