Data architectures for an organizational memory information system

Dow, Kevin E.; Hackbarth, Gary; Wong, Jeffrey A.

doi:10.1002/asi.22848

Cited by 5 publications

(4 citation statements)

References 11 publications

(17 reference statements)

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“…Master data true(F9true): Master data represents the key business theme of the company that forms the basis of the company's business goals and should, therefore, be used uniformly and clearly throughout the organization (Otto, 2012). Data architecture true(F10true): Data architecture is a set of rules, policies, standards and models that defines the type of data collected and how to use, store, manage and integrate it within an organization and its database systems (Dow et al , 2013; Vilminko-Heikkinen and Pekkola, 2017). Technology true(F11true): Technology is a critical factor for data governance success (Fleissner et al , 2014) with the lack of it presenting as a barrier to any successful implementation (Al-Ruithe et al , 2019).…”

Section: Factors and Influential Activities Of Data Governancementioning

confidence: 99%

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A new fuzzy approach for managing data governance implementation relevant activities

Ahmadi¹,

Tavana²,

Shokouhyar³

et al. 2021

TQM

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PurposeThe purpose of this paper is to propose an approach for managing relevant factors and activities for implementing data governance in an organization. The process of assessing the establishment of data governance in an organization is intrinsically imprecise, due to the characteristics of new problem settings, particularly in relation to newly generated alternatives or vaguely defined qualitative assessment criteria.Design/methodology/approachTo reject the inherent subjectiveness and imprecision involved in the evaluation process, the authors use the concept of fuzzy logic in this approach for developing the assessment model and analyzing the model for allocating the management efforts in the most efficient way to improve the data governance deployment level.FindingsThis paper identifies relevant factors and activities for implementing data governance in an organization and evaluates the state of data governance based on causal relationships between influential factors. In this study, factors are prioritized for effective allocation of limited management efforts in any improvement plan.Research limitations/implicationsThe interrelationships among factors are contextual and based on the perceptions of experts who may be biased as per their background and area of expertise. Meanwhile, lack of a data governance plan may cause failure during its implementation in an organization, as the worth of an organization's data will not be determined precisely. The paper has tremendous practical implications for organizations that intend to implement the data governance program and evaluate its state to design an improvement plan.Originality/valueThe paper proposes an approach for implementing data governance in an organization faced with limited resources for improvement.

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Section: Factors and Influential Activities Of Data Governancementioning

confidence: 99%

“…Data architecture true(F10true): Data architecture is a set of rules, policies, standards and models that defines the type of data collected and how to use, store, manage and integrate it within an organization and its database systems (Dow et al , 2013; Vilminko-Heikkinen and Pekkola, 2017).…”

Section: Factors and Influential Activities Of Data Governancementioning

confidence: 99%

A new fuzzy approach for managing data governance implementation relevant activities

Ahmadi¹,

Tavana²,

Shokouhyar³

et al. 2021

TQM

View full text Add to dashboard Cite

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“…This case is an example of an attentive response to the interprofessional perspective of decision making. This lesson can be extended to include additional situations, also as a means of contributing to organizational memory (Dow, Hackbarth, & Jeffrey Wong, ; Nevo et al., ; Stacey et al., ).…”

Section: Allowing a Third Lung Transplantation: Case Studymentioning

confidence: 99%

“…Recent publications on the correlation between organizational memory and information behavior (Dow, Hackbarth, & Wong, ; Ferran‐Ferrer et al., ) emphasize the importance of constantly examining not only the content elements but also the associated meaning. In the context of the challenge presented in this case, medical evidence alone could not balance opportunities and threats.…”

Section: Allowing a Liver Transplantation From The Living: Third Casementioning

confidence: 99%

Towards a framework for untangling complexity: The interprofessional decision‐making model for the complex patient

Weinberger

Cohen

Tadmor

et al. 2014

Asso for Info Science & Tech

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Decision making for the complex patient is challenging for doctors because of increased complexity, such as multiple co-morbidities and interprofessionality for which evidence-based literature and guidelines are currently lacking. The consequent uncertainty causes vagueness, threatening patient safety and the quality of care. This article is motivated by the design science paradigm and describes the interprofessional decisionmaking model for the complex patient, namely, INDECO along with an example instantiation. Drawing on our experience in an intensive care unit of a tertiary hospital in Israel, the bi-dimensional view of this model includes the medical-and the interprofessional perspective. Retrospective assessment of 3 case studies of complex patients is used for assessing the usefulness of INDECO in decision making. The study reported here draws support from relevant literature, including the information science, information systems, and the medical domains. The findings resonate with emerging research developments focusing on healthcare decision making.

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How integration of cyber security management and incident response enables organizational learning

Ahmad

Desouza

Maynard

et al. 2019

Asso for Info Science & Tech

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Digital assets of organizations are under constant threat from a wide assortment of nefarious actors. When threats materialize, the consequences can be significant. Most large organizations invest in a dedicated information security management (ISM) function to ensure that digital assets are protected. The ISM function conducts risk assessments, develops strategy, provides policies and training to define roles and guide behavior, and implements technological controls such as firewalls, antivirus, and encryption to restrict unauthorized access. Despite these protective measures, incidents (security breaches) will occur. Alongside the security management function, many organizations also retain an incident response (IR) function to mitigate damage from an attack and promptly restore digital services. However, few organizations integrate and learn from experiences of these functions in an optimal manner that enables them to not only respond to security incidents, but also proactively maneuver the threat environment. In this article we draw on organizational learning theory to develop a conceptual framework that explains how the ISM and IR functions can be better integrated. The strong integration of ISM and IR functions, in turn, creates learning opportunities that lead to organizational security benefits including: increased awareness of security risks, compilation of threat intelligence, removal of flaws in security defenses, evaluation of security defensive logic, and enhanced security response.

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Data architectures for an organizational memory information system

Cited by 5 publications

References 11 publications

A new fuzzy approach for managing data governance implementation relevant activities

A new fuzzy approach for managing data governance implementation relevant activities

Towards a framework for untangling complexity: The interprofessional decision‐making model for the complex patient

How integration of cyber security management and incident response enables organizational learning

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