Faramarz Sahraei
Abstract
IntroductionIn the digital transformation era, where technology and enterprise systems so much at the core of change for the way organizations and governments operate, data quality is considered one of the success factors for e-government. Due to particular dimensions and characteristics associated with ...
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IntroductionIn the digital transformation era, where technology and enterprise systems so much at the core of change for the way organizations and governments operate, data quality is considered one of the success factors for e-government. Due to particular dimensions and characteristics associated with data, data quality is vital for e-government. First, from the aspect of data quality, high-quality data records assist the Government in determining the right kind of service and addressing the needs of the citizens. Furthermore, data quality also plays an important role in government analysis and reporting. With quality data, the governments can carry out relevant and correct analyses and formulate their policies on factual evidence, real needs of the citizens, and information. Therefore, the purpose of this study was to examine the status of data quality in e-government and its impact on data governance.Literature reviewNumerous studies have been conducted in the e-governance models and structure. These studies can be categorized in three groups: studies related to e-governance and data governance, studies of data quality, studies that explain the role of e-governance in data governance. In the first group, Sarfarazi (2009); Nouri et al. (2019); and Zeynali Soume’eh, Pourazat, and Doodangah (2013) analyzed the e-governance models and frameworks. These studies identified factors, categories, concepts, functions and relationships between e-governance and developing new systems. In this regard, Meiyanti et al. (2018) had dealt with the problems inherent in the implementation of e-government in developing countries. They found organizational, managerial, human, and infrastructure challenges as the foremost with regard to e-government development in a given context.The second category is followed by studies that revolve around data quality and its dimensions. Previous research Mollaii and Tahmasebi (2019); Khodizadeh et al. (2020); identified accuracy, validity, timeliness, and completeness as the main dimensions of data quality. These are also stressed in contexts of e-government and data governance. The third category embraces those studies that treat data quality specifically for e-governments. For instance, Bonyadi et al. (2023) designed a data quality management model for data governance by using the meta-synthesis method. They ended up with 12 main categories (including data characteristics, data, data files, data value, primary data value, data models, datasets, data access, data integration, data formatting, metadata, and objectivity) and 47 subcategories relevant to data quality management towards effective data governance.MethodologyThis study was applied in nature and employed a qualitative content analysis method. Using a documentary approach, models of data quality, e-government models, data governance indicators, best practices for improving data quality, and prior research on data quality were reviewed. In the first section, the best practices in this area were studied through credible reference and information databases. Subsequently, upstream documents such as the National Strategic Plan for E-Government Development, the Sixth Development Plan of the Islamic Republic of Iran, the 20-Year Vision Document, the Law on Freedom of Information, the Comprehensive Scientific Map of the Country, the Transformation Plan for a Popular Government, the National Digital Transformation Document, Comprehensive Document of Electronic Government, and Strategic Document of the Comprehensive Information Technology System of the Islamic Republic of Iran were analyzed. Finally, data quality in e-government and its impact on data governance were analyzed, and recommendations were proposed for improving the National Strategic Plan for E-Government Development with a focus on data quality.FindingsThe research revealed that e-government dimensions include: electronic services, intelligent processes, IT infrastructure, open government, services-oriented government, process improvement, legal and regulatory aspects, data analytics, data protection, transparency, efficiency, data liberation, data accessibility, citizen participation, and others. Data quality dimensions differ with every dimension of the government. They have a direct bearing on efficiency, transparency, effectiveness of service, and consequent outputs. In respect of electronic services, data accuracy and integrity are severely considered. On the other hand, the study shows that in data governance, the policymaking process, access management and permissions to data, monitoring and evaluation, data security, integrity management of data, data completeness with regard to data-related policies and regulations, privacy, data ownership, transparency and accountability, stakeholder participation, data ethics and equity, data interoperability, and value creation from data are of paramount importance. In this regard, various dimensions of data quality lay at the very foundation of each of these components of data governance.Analysis of some of the key national policy documents in Iran shows that e-government is indeed considered as a major axis within the macro-level policymaking. They highlight transparency and ease of access to governments, which are part of the Law on Disclosure and Free Access to Information that also emphasizes transparency and better use of data. The quality of data—especially correctness, correctness, accessibility, and timeliness—is important in achieving this goal. It is clear from the dictates of these documents that processes in government such as taxation systems, health services, and civil registrations are all strongly dependent on the integration and standardization of data. Without access to high-quality data, inaccuracy becomes the order of the day in these systems, which are then prone to inconsistencies and errors in processing. It is worth noting that data security, integrity, and reliability are key components in achieving effective governance in e-government. The E-Government Transformation Document emphasizes the creation of intelligent systems, the elimination of redundancy, information accessibility, and the improvement of digital service quality. These goals can only be realized with accurate, up-to-date, and integrated data. In line with this, the Sixth Development Plan points to the development of information infrastructure and the participation of the private sector in electronic services. In this context, data quality not only requires trustworthiness and security but also the establishment of data exchange standards between public and private sector organizations.ConclusionThe findings of this article indicate that various factors influence data quality in Iran’s governmental systems. One major factor affecting data quality is poor policymaking. Governments lacking clear policies for collecting, processing, and updating data often suffer from unstructured, outdated, and duplicate data, resulting in reduced accuracy and quality. A data-driven culture influences the recognition of the importance of accurate and reliable data in decision-making, ultimately enhancing overall data quality. E-government requires accurate and up-to-date data to deliver better services. In this context, the role of citizens must not be overlooked, as a portion of the data is provided directly by them—for example, during registration on government platforms or when submitting online requests. If citizens do not enter their information correctly, the quality of the data deteriorates. Therefore, governments should encourage citizens to provide accurate and complete information. Overall, it could be said that data quality goes beyond technical aspects and also includes managerial, human, and process-related dimensions. This holistic approach is necessary to improve data quality, as poor-quality data can lead to challenges such as flawed decision-making, inefficiency in public services, and diminished public trust. In contrast, high-quality data not only improves e-government services but also enhances government transparency, accountability, and efficiency. Thus, governments must adopt appropriate policies and invest in both technical and human infrastructures to ensure the quality of their data.
Faramarz Sahraei
Abstract
Digital transformation is one of the new areas that is key in almost all innovation and change processes which has been adopted by institutions and organizations due to its positive effects on increasing workflow efficiency and reducing errors, improving performance and quality productivity and, as a ...
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Digital transformation is one of the new areas that is key in almost all innovation and change processes which has been adopted by institutions and organizations due to its positive effects on increasing workflow efficiency and reducing errors, improving performance and quality productivity and, as a result, increasing customer satisfaction, this term has been considered in different fields and sectors. Despite all the advantages of using new technologies, the digitization of various departments of organizations has faced many challenges to governments. On the one hand, the rapid growth of technologies and the management of data generated in new environments have caused changes in many government processes. Data protection and citizens' privacy are among the issues that need to be given more attention in the transformation of the digital government. This article, with an analytical and applied approach, examines the comprehensive electronic government document of the Islamic Republic of Iran from the perspective of indicators and components of the digital government as well as data governance. In the first part, the article explains the need to pay attention to digital transformation and the category of data governance and their impact on government processes. In the following, the main components of data governance are considered in order to apply digital transformation in e-government. In this regard, various dimensions of data governance such as data protection, data processes, laws, standards and related indicators are analyzed. Then, the comprehensive electronic government document of the Islamic Republic of Iran is evaluated from the perspective of the components of the digital government and with an emphasis on data governance. In this regard, components such as the transformation of government services, paying attention to user-oriented and data-oriented approaches, creating a government cloud, greater clarity and transparency of processes and adopting data-related approaches (data quality management, data security and protection management, content management and data warehouses, database process management) are emphasized. In the end, suggestions for improving data governance processes in the country are presented, emphasizing the components of digital government. In general, digital transformation has changed the expectations of governments and led to user-centric and data-centric approaches. Digital transformation does not mean only the digitization of resources and services; rather, transformation should be made in all goals, processes, procedures and structures. Considering the role of transformation and digital transformation in the development of governments, in this article, it is suggested to formulate a comprehensive and integrated data governance strategy based on data governance maturity models. It is necessary to adopt new strategies to protect, manage and develop data as a valuable organizational asset. Reviewing and updating proposed laws, regulations or policies related to electronic transactions, digital signatures and identification and leveraging digital technologies to bring together stakeholders from all levels of government and outside government to deliver better outcomes and develop individual and collective capacities to strengthen the impact of digital government Digital government is another necessity. In general, digital transformation has changed the expectations of governments and led to user-centric and data-centric approaches. Digital transformation does not mean only the digitization of resources and services; rather, transformation should be made in all goals, processes, procedures and structures. Considering the role of transformation and digital transformation in the development of governments, in this article, it is suggested to formulate a comprehensive and integrated data governance strategy based on data governance maturity models. It is necessary to adopt new strategies to protect, manage and develop data as a valuable organizational asset. Reviewing and updating proposed laws, regulations or policies related to electronic transactions, digital signatures and identification and leveraging digital technologies to bring together stakeholders from all levels of government and outside government to deliver better outcomes and develop individual and collective capacities to strengthen the impact of digital government Digital government is another necessity. The use of data governance models requires coherent and strategic planning for the use of digital technologies in all areas and at all administrative levels. Governments must ensure that their risk management capabilities, norms, structures and models are aligned with their digital government strategic vision. It is essential that governments also understand the level of organizational maturity of the public sector in relation to project management methods and approaches and can achieve appropriate levels of maturity in digital government investment. The failure of governments to transition to the new digital environment can have significant consequences, including poor service delivery, inadequate distribution of funds, privacy violations, security breaches, and loss of citizen trust. For this reason, effective digital government strategies must be responsive to public expectations in terms of economic and social value, data openness, innovation, personalized service delivery, and dialogue with business citizens.
