Data governance factors
| Factors | Description | Sources |
|---|---|---|
| Accountability | For data governance, a data stewardship approach based on accountability is advised. The accountability aspect of data governance involves relationships between risk and trust, accountability, privacy and security controls | Felici and Pearson (2015) |
| Citizen participation | Since the output of data governance affects citizens services in direct and indirect ways, citizen participation is an important element of data governance. Changing needs of citizens also require modifications to the existing governance framework and associated information flow | Zoonen (2020) |
| Organization perspective | Data sharing between organizations is a feature of the inter-organizational approach to data governance. Collaborative governance mechanisms enable co-value creation by linking the internal perspective with the external sphere of an organization | Lis, Dominik, Otto, and Boris (2020) |
| Objectives | The major objectives to drive data governance are: data value and its alignment; performance measurement; accountability; and risk management. Data governance ensures alignment between business technology and expected results | Rifaie, Alhajj, and Ridley (2009) |
| Organization strategy | For data governance implementation, organization strategy is critical. The organization strategy for data governance includes data security, data integration and further development scope as per government guidelines | Ramadhan, Tajudeen, and Jaafar (2024) |
| Cross divisional issues | For data governance applicability, participation from all levels of the organization is essential to reconcile priorities, encourage the data quality support and expedite conflict resolution | Cheong and Chang (2007) |
| Organizational and technological factors | For data governance, the success factors at organizational level are clear roles and responsibilities definitions, executive sponsorship, involvement of technology with in business, integration competency and data integration life cycle automation | Al-Ruithe et al. (2016) |
| Clear data roles and responsibilities | The role and responsibility and mapping with individuals for the data activities in the organization are critical for data governance. Ambiguity in roles and responsibilities for data activities have adverse effect on the data governance | Alhassan et al. (2019b) |
| Flexible data tool and technologies | This includes all tools and technologies for the storage, presentation, use and sharing of data. Within data governance, flexible data tools and technologies, appropriate information technology infrastructure is recommended | Alhassan et al. (2019a) |
| Data quality management | A flexible data governance model of organization consists of data quality management guidelines through data quality decision, roles and responsibilities | Weber, Kristin, Otto, Boris, Österle, and Hubert (2010) |
| Policy and process | The basis of effective data governance is establishment and enforcement of defined processes and policies around the data management | Panian (2010), Brous, Janssen, and Vilminko-Heikkinen (2016) |
| Data privacy | Despite any general policy limitations, data governance policies must be perceptive to privacy concerns | Tallon (2013) |
| Compliance | Data governance guides mechanisms for monitoring compliance and benchmarking against established baselines. Checks and balances are applied to the routine work processes to ensure compliance | Thompson, Ravindran, and Nicosia (2015) |
| Conformance and monitoring | Defining the data and the availability of audit reports to stakeholders. Detection and monitoring of activities related to data and visibility of output | Lee, Zhu, and Jeffery (2017) |
| Employee data competencies | Within organizations, human activities, including the skills and capabilities of employees, are critical during data governance activities. A definition of the required skills and competencies for each data role is required | Alhassan et al. (2019a) |
| Factors | Description | Sources |
|---|---|---|
| Accountability | For data governance, a data stewardship approach based on accountability is advised. The accountability aspect of data governance involves relationships between risk and trust, accountability, privacy and security controls | |
| Citizen participation | Since the output of data governance affects citizens services in direct and indirect ways, citizen participation is an important element of data governance. Changing needs of citizens also require modifications to the existing governance framework and associated information flow | |
| Organization perspective | Data sharing between organizations is a feature of the inter-organizational approach to data governance. Collaborative governance mechanisms enable co-value creation by linking the internal perspective with the external sphere of an organization | |
| Objectives | The major objectives to drive data governance are: data value and its alignment; performance measurement; accountability; and risk management. Data governance ensures alignment between business technology and expected results | |
| Organization strategy | For data governance implementation, organization strategy is critical. The organization strategy for data governance includes data security, data integration and further development scope as per government guidelines | |
| Cross divisional issues | For data governance applicability, participation from all levels of the organization is essential to reconcile priorities, encourage the data quality support and expedite conflict resolution | |
| Organizational and technological factors | For data governance, the success factors at organizational level are clear roles and responsibilities definitions, executive sponsorship, involvement of technology with in business, integration competency and data integration life cycle automation | |
| Clear data roles and responsibilities | The role and responsibility and mapping with individuals for the data activities in the organization are critical for data governance. Ambiguity in roles and responsibilities for data activities have adverse effect on the data governance | |
| Flexible data tool and technologies | This includes all tools and technologies for the storage, presentation, use and sharing of data. Within data governance, flexible data tools and technologies, appropriate information technology infrastructure is recommended | |
| Data quality management | A flexible data governance model of organization consists of data quality management guidelines through data quality decision, roles and responsibilities | |
| Policy and process | The basis of effective data governance is establishment and enforcement of defined processes and policies around the data management | |
| Data privacy | Despite any general policy limitations, data governance policies must be perceptive to privacy concerns | |
| Compliance | Data governance guides mechanisms for monitoring compliance and benchmarking against established baselines. Checks and balances are applied to the routine work processes to ensure compliance | |
| Conformance and monitoring | Defining the data and the availability of audit reports to stakeholders. Detection and monitoring of activities related to data and visibility of output | |
| Employee data competencies | Within organizations, human activities, including the skills and capabilities of employees, are critical during data governance activities. A definition of the required skills and competencies for each data role is required |
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