A taxonomy of data governance decision domains in data marketplaces

2023 | Electronic Markets | Citations: 0

Authors: Abraham, Rene; Schneider, Johannes; vom Brocke, Jan

Abstract: Commercializing data and data-related services has gained in importance in recen ... Expand

Abstract: Commercializing data and data-related services has gained in importance in recent years. Driven by digitalization and the Internet-of-Things (IoT), companies and individuals continuously generate vast amounts of data. Data marketplaces have emerged to support these data providers in selling their data to different data consumers. However, data marketplaces face challenges in different data governance decision domains that inhibit their adoption. To get a better understanding of how data marketplaces counteract these challenges, this paper develops a taxonomy of data governance decision domains in data marketplaces. We used a taxonomy development method to inspect 13 data marketplaces from eight countries. The resulting taxonomy shows an overview of mechanisms concerning data quality, data security, data architecture, metadata, data lifecycle, data storage, and data pricing. We discuss common instantiation patterns, highlight gaps, and propose possible solutions. The taxonomy sets a foundation for further research and theory-building on data marketplaces. Practitioners can use the taxonomy to develop customized data governance strategies for data marketplaces. Collapse

Topics: data governance price management system development method computer data storage website

Methods: design artifact data modeling design methodology design science company material

Data Sharing Frames: How Scientists Understand the Work of Sharing Scientific Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Amagyei, Nana Kwame; Engesmo, Jostein; Panteli, Niki

Abstract: The curation of data is fundamental to their wider dissemination and use. This ... Expand

Abstract: The curation of data is fundamental to their wider dissemination and use. This paper investigates the frames of workers who perform data curation in scientific contexts. We view data curation as a sense-making practice, where workers collaborate to disseminate meaningful data to a broad set of prospective users. Previous Information Systems investigations have suggested that data-related activities are dependent on workers' understanding of their local work context. We expand this with an evolving and longterm view. We use a stepwise-deductive induction method to examine how scientists understand the work involved in curating scientific data for public sharing. We draw on frames as the theoretical lens of the study that enables us to identify three data sharing frames -the object, curation, and aligning frames -as important frames that shape how scientists curate data for public sharing. Our analysis provides a deeper understanding of the nuances of managing scientific data for public access. Our main contribution is the articulation of an evolving and long-term view of how workers approach their tasks in getting data ready for long-term public use. Collapse

Topics: data management Python data governance datafication strategic management

Methods: survey qualitative interview personal interview field study theoretical contribution

Towards Open Data Impact Evaluation Framework - An Empirical Analysis of the Demand-side Response

2023 | HICSS | Citations: 0

Authors: Ahmadi Zeleti, F.; Kerins, R.; Campbell, H.

Abstract: Open data is widely presumed to have a social, environmental, political, and ec ... Expand

Abstract: Open data is widely presumed to have a social, environmental, political, and economic impact; however, the evidence to that impact has remained scarce. The impacts must be explored and quantified to give a reasonable insight into the supply-side activities and demand-side responses. Collapse

Topics: open data online review data governance societal evaluation criteria decision making

Methods: case study survey survey design product review

Data governance and the secondary use of data: The board influence

2023 | Information and Organization | Citations: 0

Authors: Black, Stuart; Davern, Michael; Maynard, Sean B.; Nasser, Humza

Abstract: The business analytics and strategic management literatures suggest that organiz ... Expand

Abstract: The business analytics and strategic management literatures suggest that organizations should seek to exploit data as a key mechanism for competitive advantage. However, the rules of engagement are evolving, the regulatory landscape is becoming increasingly complex, and ex­ amples of poor outcomes are increasingly common. The board – in its role of setting and moni­ toring risk appetite – needs to be able to govern the risk/reward trade-off of the data asset. Contemporary data governance approaches are inadequate: they are overly rigid and risk ori­ ented, limited in scope to an organization’s self-interest rather than considering the broad set of stakeholders, and do not provide a platform for the board to manage this critical risk. This paper uses a unique set of informants – 41 board directors – to demonstrate that differences in board perspectives influence how organizations explore the secondary use of data. Furthermore, this paper identifies a set of relevant individual, organizational and environmental factors and pre­ sents empirically based configurations of these factors that lead organizations to consider (or neglect) the secondary use of data as a critical enabler of competitive advantage. Collapse

Topics: data governance competitive advantage IT governance fuzzy logic strategic management

Methods: qualitative interview qualitative coding theory development mixed method personal interview

Theories: contingency theory fuzzy set theory transaction cost economics dynamic capabilities theory theory of everything

Managing Customer Data in Data-driven Service Innovation: A Framework of Data Principles

2023 | International Conference on Business Informatics | Citations: 0

Authors: Blöcher, Katharina; Reinhold, Olaf; Alt, Rainer

Abstract: While customer data has been collected in enterprise systems since decades, the ... Expand

Abstract: While customer data has been collected in enterprise systems since decades, the emerging consumer technologies create new sources of data. Although the need to co-create services with customers has been recognized, a systematic approach of how to include this sensitive source of innovation in the service innovation process is still lacking. This research explores the potential of data governance practices for data-driven service innovation. Data principles for the governance of customer data are collected and assessed by practioners in order to provide conceptual support for organizations and to facilitate the service innovation process. The results of this research integrate data principles of different research streams and offer a framework of data principles that can be applied in the design and management of data-driven services. Collapse

Topics: innovation management business process innovation data governance database system data management

Methods: qualitative interview personal interview archival research theoretical contribution synthesis

Theories: dominant logic

A process mining maturity model: Enabling organizations to assess and improve their process mining activities

2023 | European Conference On Information Systems | Citations: 0

Authors: Brock, Jonathan; Löhr, Bernd; Brennig, Katharina; Seger, Thilo; Bartelheimer, Christian; Enzberg, Sebastian von; Kühn, Arno; Dumitrescu, Roman

Abstract: Organizations employ process mining to discover, check, or enhance process mode ... Expand

Abstract: Organizations employ process mining to discover, check, or enhance process models based on data from information systems to improve business processes. Even though process mining is increasingly relevant in academia and organizations, achieving process mining excellence and generating business value through its application is elusive. Maturity models can help to manage interdisciplinary teams in their efforts to plan, implement, and manage process mining in organizations. However, while numerous maturity models on business process management (BPM) are available, recent calls for process mining maturity models indicate a gap in the current knowledge base. We systematically design and develop a comprehensive process mining maturity model that consists of five factors comprising 23 elements, which organizations need to develop to apply process mining sustainably and successfully. We contribute to the knowledge base by the exaptation of existing BPM maturity models, and validate our model through its application to a real-world scenario. Collapse

Topics: process mining business process management knowledge base ease of use organizational value

Methods: maturity model design science survey event log literature study

Future directions for scholarship on data governance, digital innovation, and grand challenges

2023 | Information and Organization | Citations: 0

Authors: Davidson, Elizabeth; Wessel, Lauri; Winter, Jenifer Sunrise; Winter, Susan

Abstract: This introduction to the special issue on Data Governance, Digital Innovation, a ... Expand

Abstract: This introduction to the special issue on Data Governance, Digital Innovation, and Grand Chal­ lenges highlights the importance of data governance when seeking to address grand challenges through the innovative use of digital technologies. The benefits, risks, and consequences of data, ubiquitous in today’s data-rich world, can be harnessed for innovation and societal good. How­ ever, there are no guarantees that (only) desirable outcomes will develop. The creation and exploitation of vast data stockpiles raise substantial concerns about privacy, data security, equity, and the potential for harm from data misuse. Meaningful approaches to data governance within and across organizations are critically important to facilitate digital innovation and to balance social, economic and technical benefits and risks for individuals, organizations, and societies. In this introductory paper, we reflect on foundations established to date in information systems (IS) research and highlight possible future directions for scholarship on data governance across multiple levels to enhance digital innovations for transformation and societal good. Collapse

Topics: data governance digital innovation innovation management IS economics economic surplus

Methods: archival research

Theories: socio technical theory theory of economic development

Artificial intelligence for decision-making and the future of work

2023 | International Journal of Information Management | Citations: 0

Authors: Dennehy, Denis; Griva, Anastasia; Pouloudi, Nancy; Mäntymäki, Matti; Pappas, Ilias

Abstract: ... Expand

Abstract: Collapse

Topics: artificial intelligence human computer interaction human in the loop data governance data quality

Methods: chatbot case study theory development analytic hierarchy process trade-off analysis method

Theories: institutional theory

Identifying governance mechanisms for data sharing in collaborative platform ecosystems

2023 | European Conference On Information Systems | Citations: 0

Authors: Costabile, Carolina; Øvrelid, Egil

Abstract: The fish farming industry is one of the fastest-growing industries and will be ... Expand

Abstract: The fish farming industry is one of the fastest-growing industries and will be one of the most important businesses in the future. However, more salmon than ever died prematurely in Norway in 2021, mainly due to disease and lice treatments. To improve this situation, sharing data related to lice is crucial. However, to facilitate data sharing, a platform for storing data, data comparability, and trustworthy relationships must be secured. Sharing data poses challenges that require proper governance. Based on a longitudinal, qualitative case study of a collaborative platform ecosystem in the Norwegian aquaculture industry, we identify governance mechanisms that enable data sharing and develop a model for the gradual maturation of data sharing. Our insights from a collaborative platform ecosystem within a traditional industry complement the extant research, mainly focused on centralized platform ecosystems. We also provide guidance for practitioners who want to enable data sharing in their ecosystems. Collapse

Topics: digital ecosystem business-to-business business-to-consumer data governance application programming interface

Methods: case study qualitative interview business process modeling longitudinal research archival research

Is Your Organization Ready to Share? A Framework of Beneficial Conditions for Data Sharing

2023 | International Conference on Information Systems | Citations: 0

Authors: Fassnacht, Marcel; Benz, Carina; Leimstoll, Jannis; Satzger, Gerhard

Abstract: In a constantly evolving digital sphere, surmounting organizational boundaries ... Expand

Abstract: In a constantly evolving digital sphere, surmounting organizational boundaries and sharing data offers the opportunity to realize a multitude of mutual benefits, such as advanced analytics and innovative services. Organizations aspire to share data. However, they struggle to identify and establish beneficial conditions for data sharing, and research still offers little support to exploit the potential of data sharing. We apply an exploratory research approach to develop a framework of beneficial conditions for data sharing. By combining ten expert interviews and a systematic literature review, we aggregate 23 characteristics that constitute beneficial conditions into eight categories and apply and validate the framework in a real-world case. Thus, we contribute to research by providing a fundamental understanding of beneficial conditions for data sharing and a compact target picture. Additionally, we enable practitioners to systematically assess an organization's current condition to set the course toward exploiting the full potential of data sharing. Collapse

Topics: human resource management data governance database system data literacy digital ecosystem

Methods: qualitative interview structured literature research focus group literature study qualitative coding

Theories: theory of everything