The Experts below are selected from a list of 151233 Experts worldwide ranked by ideXlab platform
Ross Jeffery - One of the best experts on this subject based on the ideXlab platform.
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a Data Governance framework for platform ecosystem process management
Business Process Management, 2018Co-Authors: Ross JefferyAbstract:Platform ecosystem today is regarded as the key business concept of organizations to win market. Platform companies can grow fast through the Data contribution of multi-sided networks. Yet, they face difficulties in managing the Data resulted from complicated contribution, use and interactions between the multiple parties. The circumstance causes serious concerns about unclear Data ownership and invisible use of Data, and ultimately leads to Data abuse/misuse or privacy violation. To alleviate to this, a particular type of Data Governance is required. However, there is limited research on Data and Data Governance for platform ecosystems. We introduce a new Data Governance framework for platform ecosystems which consists of Data, role, decisions and due processes. The framework supports organizations in understanding to show how the risks should be dealt in the processes for business success. We compare 19 existing industry Governance frameworks and academic work with our framework to show current gaps and limitations.
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HICSS - Designing Data Governance in Platform Ecosystems
Proceedings of the 51st Hawaii International Conference on System Sciences, 2018Co-Authors: Sung Une Lee, Liming Zhu, Ross JefferyAbstract:As platform ecosystems such as Facebook or Twitter are rapidly growing through platform users’ Data contribution, the importance of Data Governance has been highlighted. Platform ecosystems, however, face increasing complexity derived from the business context such as multiple parties’ participation. How to share control and decision rights about Data assets with platform users is regarded as a significant Governance design issue. However, there is a lack of studies on this issue. Existing design models focus on the characteristics of enterprises. Therefore, there is limited support for platform ecosystems where there are different types of context and complicated relationships. To deal with the issue, this paper proposes a novel design approach for Data Governance in platform ecosystems including design principles, contingency factors and an architecture model. Case studies are performed to illustrate the practical implications of our suggestion
Marijn Janssen - One of the best experts on this subject based on the ideXlab platform.
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Data Governance: Organizing Data for trustworthy Artificial Intelligence
Government Information Quarterly, 2020Co-Authors: Marijn Janssen, Paul Brous, Elsa Estevez, Luís Soares Barbosa, Tomasz JanowskiAbstract:Abstract The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on Data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such Data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced Data Governance capabilities. This paper reviews challenges and approaches to Data Governance for such systems, and proposes a framework for Data Governance for trustworthy BDAS. The framework promotes the stewardship of Data, processes and algorithms, the controlled opening of Data and algorithms to enable external scrutiny, trusted information sharing within and between organizations, risk-based Governance, system-level controls, and Data control through shared ownership and self-sovereign identities. The framework is based on 13 design principles and is proposed incrementally, for a single organization and multiple networked organizations.
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Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles
2016Co-Authors: Paul Brous, Marijn Janssen, Riikka Vilminko-heikkinenAbstract:More and more Data is becoming available and is being combined which results in a need for Data Governance - the exercise of authority, control, and shared decision making over the management of Data assets. Data Governance provides organizations with the ability to ensure that Data and information are managed appropriately, providing the right people with the right information at the right time. Despite its importance for achieving Data quality, Data Governance has received scant attention by the scientific community. Research has focused on Data Governance structures and there has been only limited attention given to the underlying principles. This paper fills this gap and advances the knowledge base of Data Governance through a systematic review of literature and derives four principles for Data Governance that can be used by researchers to focus on important Data Governance issues, and by practitioners to develop an effective Data Governance strategy and approach.
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EGOV - Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles
Lecture Notes in Computer Science, 2016Co-Authors: Paul Brous, Marijn Janssen, Riikka Vilminko-heikkinenAbstract:More and more Data is becoming available and is being combined which results in a need for Data Governance - the exercise of authority, control, and shared decision making over the management of Data assets. Data Governance provides organizations with the ability to ensure that Data and information are managed appropriately, providing the right people with the right information at the right time. Despite its importance for achieving Data quality, Data Governance has received scant attention by the scientific community. Research has focused on Data Governance structures and there has been only limited attention given to the underlying principles. This paper fills this gap and advances the knowledge base of Data Governance through a systematic review of literature and derives four principles for Data Governance that can be used by researchers to focus on important Data Governance issues, and by practitioners to develop an effective Data Governance strategy and approach.
Alfredo Maldonado - One of the best experts on this subject based on the ideXlab platform.
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automatic extraction of Data Governance knowledge from slack chat channels
OTM Confederated International Conferences "On the Move to Meaningful Internet Systems", 2018Co-Authors: Rob Brennan, Simon Quigley, Pieter De Leenheer, Alfredo MaldonadoAbstract:This paper describes a Data Governance knowledge extraction prototype for Slack channels based on an OWL ontology abstracted from the Collibra Data Governance operating model and the application of statistical techniques for named entity recognition. This addresses the need to convert unstructured information flows about Data assets in an organisation into structured knowledge that can easily be queried for Data Governance. The abstract nature of the Data Governance entities to be detected and the informal language of the Slack channel increased the knowledge extraction challenge. In evaluation, the system identified entities in a Slack channel with precision but low recall. This has shown that it is possible to identify Data assets and Data management tasks in a Slack channel so this is a fruitful topic for further research.
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OTM Conferences (2) - Automatic Extraction of Data Governance Knowledge from Slack Chat Channels
Lecture Notes in Computer Science, 2018Co-Authors: Rob Brennan, Simon Quigley, Pieter De Leenheer, Alfredo MaldonadoAbstract:This paper describes a Data Governance knowledge extraction prototype for Slack channels based on an OWL ontology abstracted from the Collibra Data Governance operating model and the application of statistical techniques for named entity recognition. This addresses the need to convert unstructured information flows about Data assets in an organisation into structured knowledge that can easily be queried for Data Governance. The abstract nature of the Data Governance entities to be detected and the informal language of the Slack channel increased the knowledge extraction challenge. In evaluation, the system identified entities in a Slack channel with precision but low recall. This has shown that it is possible to identify Data assets and Data management tasks in a Slack channel so this is a fruitful topic for further research.
Paul Brous - One of the best experts on this subject based on the ideXlab platform.
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Data Governance: Organizing Data for trustworthy Artificial Intelligence
Government Information Quarterly, 2020Co-Authors: Marijn Janssen, Paul Brous, Elsa Estevez, Luís Soares Barbosa, Tomasz JanowskiAbstract:Abstract The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on Data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such Data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced Data Governance capabilities. This paper reviews challenges and approaches to Data Governance for such systems, and proposes a framework for Data Governance for trustworthy BDAS. The framework promotes the stewardship of Data, processes and algorithms, the controlled opening of Data and algorithms to enable external scrutiny, trusted information sharing within and between organizations, risk-based Governance, system-level controls, and Data control through shared ownership and self-sovereign identities. The framework is based on 13 design principles and is proposed incrementally, for a single organization and multiple networked organizations.
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Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles
2016Co-Authors: Paul Brous, Marijn Janssen, Riikka Vilminko-heikkinenAbstract:More and more Data is becoming available and is being combined which results in a need for Data Governance - the exercise of authority, control, and shared decision making over the management of Data assets. Data Governance provides organizations with the ability to ensure that Data and information are managed appropriately, providing the right people with the right information at the right time. Despite its importance for achieving Data quality, Data Governance has received scant attention by the scientific community. Research has focused on Data Governance structures and there has been only limited attention given to the underlying principles. This paper fills this gap and advances the knowledge base of Data Governance through a systematic review of literature and derives four principles for Data Governance that can be used by researchers to focus on important Data Governance issues, and by practitioners to develop an effective Data Governance strategy and approach.
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EGOV - Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles
Lecture Notes in Computer Science, 2016Co-Authors: Paul Brous, Marijn Janssen, Riikka Vilminko-heikkinenAbstract:More and more Data is becoming available and is being combined which results in a need for Data Governance - the exercise of authority, control, and shared decision making over the management of Data assets. Data Governance provides organizations with the ability to ensure that Data and information are managed appropriately, providing the right people with the right information at the right time. Despite its importance for achieving Data quality, Data Governance has received scant attention by the scientific community. Research has focused on Data Governance structures and there has been only limited attention given to the underlying principles. This paper fills this gap and advances the knowledge base of Data Governance through a systematic review of literature and derives four principles for Data Governance that can be used by researchers to focus on important Data Governance issues, and by practitioners to develop an effective Data Governance strategy and approach.
Boris Otto - One of the best experts on this subject based on the ideXlab platform.
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One Size Does Not Fit All: Best Practices for Data Governance
2011Co-Authors: Boris OttoAbstract:Data Governance defines roles and responsibilities for the management and use of corporate Data. While the need for Data Governance is undoubted, companies often encounter difficulties in designing Data Governance in their organization. There is no one size fits all solution. As companies are different in terms of their business strategy, their diversification breadth, their industry, IT strategy and application system landscape, Data Governance must take into account this diversity. What works in company A does not necessarily work in company B. An example: A company from the chemical industry organizes Data stewardship as a virtual organization with solid reporting lines to the business functions (e.g. supply chain management, financial accounting) whereas a second company of similar size, product range and geographic presence establishes a shared service center to organize Data stewards.The presentation introduces a reference model for Data Governance design which was developed by the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen. The CC CDQ is an applied research program and intensively collaborates with industry partners. Among the partner companies are AstraZeneca, Bayer, Bosch, Beiersdorf, Deutsche Telekom, Nestle, Novartis, and Siemens. Based on industry best practices, the reference model forms a blueprint for Data Governance design.
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a morphology of the organisation of Data Governance
European Conference on Information Systems, 2011Co-Authors: Boris OttoAbstract:Both information systems (IS) researchers and practitioners consider Data Governance as a promisingapproach for companies to improve and maintain the quality of corporate Data, which is seen ascritical for being able to meet strategic business requirements, such as compliance or integratedcustomer management. Both sides agree that Data Governance primarily is a matter of organisation.However, hardly any scientific results have been produced so far indicating what actually has to beorganised by Data Governance, and what Data Governance may look like. The paper aims at closingthis gap by developing a morphology of Data Governance organisation on the basis of acomprehensive analysis of the state of the art both in science and in practice. Epistemologically, themorphology represents an analytic theory, as it serves for structuring the research topic of DataGovernance, which is still quite unexplored. Six mini case studies are used to evaluate the morphologyby means of empirical Data. Providing a foundation for further research, the morphology contributesto the advancement of the scientific body of knowledge. At the same time, it is beneficial topractitioners, as companies may use it as a guideline when organising Data Governance.
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ECIS - A Morphology of the Organisation of Data Governance
2011Co-Authors: Boris OttoAbstract:Both information systems (IS) researchers and practitioners consider Data Governance as a promisingapproach for companies to improve and maintain the quality of corporate Data, which is seen ascritical for being able to meet strategic business requirements, such as compliance or integratedcustomer management. Both sides agree that Data Governance primarily is a matter of organisation.However, hardly any scientific results have been produced so far indicating what actually has to beorganised by Data Governance, and what Data Governance may look like. The paper aims at closingthis gap by developing a morphology of Data Governance organisation on the basis of acomprehensive analysis of the state of the art both in science and in practice. Epistemologically, themorphology represents an analytic theory, as it serves for structuring the research topic of DataGovernance, which is still quite unexplored. Six mini case studies are used to evaluate the morphologyby means of empirical Data. Providing a foundation for further research, the morphology contributesto the advancement of the scientific body of knowledge. At the same time, it is beneficial topractitioners, as companies may use it as a guideline when organising Data Governance.
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Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers
Communications of the Association for Information Systems, 2011Co-Authors: Boris OttoAbstract:Many companies see Data Governance as a promising approach to ensuring Data quality and maintaining its value as a company asset. While the practitioners' community has been vigorously discussing the topic for quite some time, Data Governance as a field of scientific study is still in its infancy. This article reports on the findings of a case study on the organization of Data Governance in two large telecommunications companies, namely BT and Deutsche Telekom. The article proposes that large, service-providing companies in general have a number of options when designing Data Governance and that the individual organizational design is context-contingent.Despite their many similarities, BT and Deutsche Telekom differ with regard to their Data Governance organization. BT has followed a more project-driven, bottom-up philosophy; Deutsche Telekom, on the other hand, favors a rather constitutive, top-down approach. The article also proposes a research agenda for further studies in the field of Data Governance organization.
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One Size Does Not Fit All---A Contingency Approach to Data Governance
Journal of Data and Information Quality, 2009Co-Authors: Kristin Weber, Boris Otto, Hubert ÖsterleAbstract:Enterprizes need Data Quality Management (DQM) to respond to strategic and operational challenges demanding high-quality corporate Data. Hitherto, companies have mostly assigned accountabilities for DQM to Information Technology (IT) departments. They have thereby neglected the organizational issues critical to successful DQM. With Data Governance, however, companies may implement corporate-wide accountabilities for DQM that encompass professionals from business and IT departments. This research aims at starting a scientific discussion on Data Governance by transferring concepts from IT Governance and organizational theory to the previously largely ignored field of Data Governance. The article presents the first results of a community action research project on Data Governance comprising six international companies from various industries. It outlines a Data Governance model that consists of three components (Data quality roles, decision areas, and responsibilities), which together form a responsibility assignment matrix. The Data Governance model documents Data quality roles and their type of interaction with DQM activities. In addition, the article describes a Data Governance contingency model and demonstrates the influence of performance strategy, diversification breadth, organization structure, competitive strategy, degree of process harmonization, degree of market regulation, and decision-making style on Data Governance. Based on these findings, companies can structure their specific Data Governance model.
