Modes of Governance in Inter-Organizational Data Collaborations

Big data and data-driven innovation are drivers for economic growth. To capture this growth, data often need to be shared among organisations. However, many challenges to sharing data among organisations exist. This paper investigates how governance is organised in inter-organisational data collaborations. First, based on literature, four archetypical modes of governance are identified: Market, Hierarchy, Bazaar and Network. Subsequently, these theoretical modes are investigated empirically by exploring governance modes in four use cases. Based on a cross-case comparison, we find that major challenges to data sharing are the commercially sensitive nature of data and privacy risks. Due to legal implications, sharing of personal data always takes place hierarchically. Therefore, coordination and control over data need to be firmly in place before organisations engage in data sharing. Further research should look into how these aspects can be organised in inter-organisational data collaborations to foster innovation

Contemporary Issues of Open Data in Information Systems Research: Considerations and Recommendations

Researchers, governments, and funding agencies are calling on research disciplines to embrace open data—data that anyone can access and use. They have done so based on the premise that research efforts can draw and generate several benefits from open data because it might provide further insight and enable individuals to replicate and extend current knowledge in different contexts. These potential benefits, coupled with a global push towards open data policies, bring open data into the agenda of research disciplines, which includes information systems (IS). In this paper, we respond to these developments as follows. We outline themes in the ongoing discussion around open data in the IS discipline. The themes fall into two clusters: 1) the motivation for open data includes themes of mandated sharing, benefits to the research process, extending the life of research data, and career impact; and 2) the implementation of open data includes themes of governance, socio-technical system, standards, data quality, and ethical considerations. In this paper, we outline the findings from a pre-ICIS 2016 workshop on the topic of open data. The workshop discussion confirmed themes and identified issues that require attention in terms of the approaches that IS researchers currently use. The IS discipline offers a unique knowledge base, tools, and methods that can advance open data across disciplines. Based on our findings, we provide suggestions on how IS researchers can drive the open data conversation. Further, we provide advice for adopting and establishing procedures and guidelines for archiving, evaluating, and using open data

A Framework to Identify Data Governance Requirements in Open Data Ecosystems

Open data and open data ecosystems (ODEs) are important for stakeholders from science, businesses, and the broader society. However, concerns about data sharing and data handling are significant adoption barriers of ODEs that reduce stakeholder participation and thus the success of the initiative. Data governance (DG) is proposed as solution, but requirements of the three stakeholder groups combined are not clear and especially how they can be integrated in one DG concept. This paper develops a framework, supporting elicitation of DG requirements in ODEs. The framework builds on a series of stakeholder workshops and literature research resulting in DG requirements and DG mechanisms. The resulting framework includes five main dimensions: (1) data usability, (2) ethical and legal compliance, (3) data lineage, (4) data access and specified data use, and (5) organizational design

Preparing Future Business Data Sharing via a Meta-Platform for Data Marketplaces: Exploring Antecedents and Consequences of Data Sovereignty

Meta-platforms have received considerable Information Systems scholarly attention in recent years. Meta-platforms enable platform-to-platform openness and are especially beneficial to amplifying network effects in highly-specialized markets. A promising emerging context for applying metaplatforms is data marketplaces—a special type of digital platform designed for business data sharing that is vastly fragmented. However, data providers have sovereignty concerns: the risk of losing control over the data that they share through metaplatforms. This research aims to explore antecedents and consequences of data sovereignty concerns in meta-platforms for data marketplaces. Based on interviews with fifteen potential data providers and five data marketplace experts, we identify data sovereignty antecedents, such as (potentially) less trustworthy data marketplace participants, unclear use cases, and data provenance difficulties. Data sovereignty concerns have many consequences, including knowledge spillovers to competitors and reputational damage. This study is among the first that empirically develops a pre-conceptualization for data sovereignty in this novel context, thus laying the groundwork for designing future data marketplace meta-platform solutions

THE DATA COLLABORATION CANVAS: A VISUAL FRAMEWORK FOR SYSTEMATICALLY IDENTIFYING AND EVALUATING ORGANIZATIONAL DATA COLLABORATION OPPORTUNITIES

For organizations, the use of Big Data and data analytics provides the opportunity to gain competitive advantages and foster innovation. In most of these data analytics initiatives, it is possible that data from external stakeholders could enrich the internal data assets and lead to enhanced outcomes. Currently, no framework is available that systematically guides practitioners in identifying and evaluating suitable inter-organizational data collaborations at an early stage. This paper closes the gap by following an action design research approach to develop the Data Collaboration Canvas (DCC). The DCC was rigorously evaluated by practitioners from Swiss organizations in six different industries, instantiated in four workshops, and used to guide innovative data collaboration projects. This artifact gives practitioners a guideline for identifying data collaboration opportunities and an insight into the main factors that must be addressed before further pursuing a collaborative partnership

Design Principles for Institutionalized Data Ecosystems – Results from a Series of Case Studies

Sharing and collaborating on data across organizational boundaries is increasingly important for building a comprehensive data foundation for a variety of relevant analytical models and reports. We argue that a formalized set of rules and responsibilities - data governance - is needed to guide such data sharing ac-tivities and thus provide the foundation for an institutionalized data ecosystem. To this end, we propose a set of design principles. Based on three case studies from different application domains, we derive the design principles using Ser-vice-Dominant Logic as our theoretical lens. We distinguish between dynamic and static design principles. Our approach supports the delineation and specifi-cation of data governance structures for data ecosystems

Trust me, I’m an Intermediary! Exploring Data Intermediation Services

Data ecosystems receive considerable attention in academia and practice, as indicated by a steadily growing body of research and large-scale (industry-driven) research projects. They can leverage so-called data intermediaries, which are mediating parties that facilitate data sharing between a data provider and a data consumer. Research has uncovered many types of data intermediaries, such as data marketplaces or data trusts. However, what is missing is a ‘big picture’ of data intermediaries and the functions they fulfill. We tackle this issue by extracting data intermediation services decoupled from specific instances to give a comprehensive overview of how they work. To achieve this, we report on a systematic literature review, contributing data intermediation services

Data Sharing Fundamentals: Definition and Characteristics

The importance of data as a key resource is a universal theme dominating social and business life. In this regard, inter-organizational data sharing shines in a new light prompting businesses to leverage their potential. However, it is still unclear what data sharing actually entails, i.e., what it means, what its potentials are, and what barriers one must overcome. In short, it lacks conceptual clarity and a clear description of its characteristics. The conceptual ambiguity and the synonymous use with data exchange in the literature are particularly problematic, which prevents a targeted conceptualization and use. The paper starts precisely at this point as it proposes a unifying definition and characteristics of data sharing. We report on a systematic literature review characterizing data sharing and delineating it from data exchange

How to Share Data Online (fast) – A Taxonomy of Data Sharing Business Models

Data is an integral part of almost every business. Sharing data enables new opportunities to generate value or enrich the existing data repository, opening up new potentials for optimization and business models. However, these opportunities are still untapped, as sharing data comes with many challenges. First and foremost, aspects such as trust in partners, transparency, and the desire for security are issues that need to be addressed. Only then can data sharing be used efficiently in business models. The paper addresses this issue and generates guidance for the data-sharing business model (DSBM) design in the form of a taxonomy. The taxonomy is built on the empirical analysis of 80 DSBMs. With this, the primary contributions are structuring the field of an emerging phenomenon and outlining design options for these types of business models

Barriers to Data Sharing among Private Sector Organizations

In today’s digital world, sharing data among private sector organizations to realm mutual benefits, such as innovation and value co-creation, is considered a promising yet barely explored and realized approach. Although private sector organizations are pursuing data sharing, successful real-world examples are sparse due to a multitude of barriers. However, knowledge on barriers to data sharing among private sector organizations is scarcely existent in scientific literature. Therefore, we apply an exploratory research approach by triangulating insights from fourteen expert interviews and a systematic literature review to identify barriers which we group along five distinct perspectives. By exploring the multi-faceted barriers to data sharing among private sector organizations, our work contributes to a better understanding of data sharing in this field and lays the foundation for future studies. For practitioners, we identify key challenges to successful data sharing among private sector organizations and, hence call for additional endeavors in data sharing