Reframing Open Big Data

Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two other categories that stem from computer science and engineering, namely ‘big/small’ and ‘open/closed’ to address the complex interplay between people and data, social interaction and technological operations. Thus conceived, this paper contributes an alternative approach for the study of open and big data as well as laying the theoretical groundwork for its future empirical research

Open Data Standards: Vertical Industry Standards to Unlock Digital Ecosystems

Standards are considered an essential means to facilitate value creation from open data. Despite this importance, we find that empirical studies of open data standards have not been conducted in proportion to its importance. In particular, the literature has insofar been silent about why specific standards are chosen and how these standards are implemented. To this end, we report from an action research project with the Swedish public transport industry, where open data standards were both chosen and implemented. Consistent with the literature, we find standards were selected based on expected increased attractivity for re-users. Also, and more surprisingly, we found that open data standards were chosen as a means to harness resources in adjacent digital ecosystems. Finally, our findings convey that implementing open data standards may hamper the possibility to publish datasets, with its original qualities

Evaluating Open Data Innovation: A Measurement Model for Digital Innovation Contests

Digital innovation contests emerge as important intermediaries in open data markets. However the understanding of how contests affect innovation value chains is low and there is a lack of innovation measurement frameworks to support the management of digital innovation contests. Therefore, in this paper we apply design science to design a measurement model for digital innovation contests from the organizer’s perspective that adds to the available knowledge of innovation measurement. We use a recent case of digital innovation contests to motivate the model and discuss its implications on the innovation value chain. The measurement model contributes with new knowledge in the area of open data innovation and provides support for practice in managing innovation through digital innovation contests. For future research we intend to enhance the model to also measure the effects on innovation ecosystems, to operationalize the measures and to evaluate the model in several digital innovation contests as well as to include the perspective of the participants

From Contest to Market Entry: A Longitudinal Survey of Innovation Barriers Constraining Open Data Service Development

Open data services have emerged as a research field. One important area of investigation within this field is exploration into how sustainable open data markets are created. Contests have become a popular method to propel and catalyse open data service development providing services to such markets. Recent research has identified numerous innovation barriers hampering development adjacent to the contest in developers’ effort to transform contest contributions to viable digital services based on open data. Little is however known about what innovation barriers over time constrain the post-contest process to transform initial innovations to finalized open data services ready for market entry. This paper presents a longitudinal survey of innovation barriers constraining teams performing open data service development after an innovation contest. The survey provides insights into 1) 24 innovation barriers constraining development, 2) a comparison of barrier importance based on team progress, and 3) a conceptualisation of phases structuring the process from contests to market entry, stipulating different innovation barriers impact open data service development. The results contribute to the understanding of how sustainable open data markets emerge and serve as a starting point for investigating how different stakeholders can manage innovation barriers constraining open data service development

Improving the Utilization of Digital Services - Evaluating Contest - Driven Open Data Development and the Adoption of Cloud Services

There is a growing interest in utilizing digital services, such as software apps and cloud-based software services. The utilization of digital services is increasing more rapidly than any other segment of world trade. The availability of open data unlocks the possibility of generating market possibilities in the public and private sectors. Digital service utilization can be improved by adopting cloud-based software services and open data innovation for service development. However, open data has no value unless utilized, and little is known about developing digital services using open data. Evaluation of digital service development processes to service deployment is indispensable. Despite this, existing evaluation models are not specifically designed to measure open data innovation contests. Additionally, existing cloud-based digital service implications are not used directly to adopt the technology, and empirical research needs to be included. The research question addressed in this thesis is: "How can contest-driven innovation of open data digital services be evaluated and the adoption of digital services be supported to improve the utilization of digital services?" The research approaches used are design science research, descriptive statistics, and case study. This thesis proposes Digital Innovation Contest Measurement Model (DICM-model) and Designing and Refining DICM (DRD-method) for designing and refining DICM-model to provide more agility. Additionally, a framework of barriers constraining developers of open data services from developing viable services is also presented. This framework enables requirement and cloud engineers to prioritize factors responsible for effective adoption. Future research possibilities are automation of idea generation, ex-post evaluation of the proposed artifacts, and expanding cloud-based digital service adoption from suppliers' perspectives.Comment: The abstract is summarized to fit arxiv's character length requirement; DSV Report Series, Series No. 18-00

The future of consumer data protection in the E.U. Rethinking the “notice and consent” paradigm in the new era of predictive analytics

The new E.U. proposal for a general data protection regulation has been introduced to give an answer to the challenges of the evolving digital environment. In some cases, these expectations could be disappointed, since the proposal is still based on the traditional main pillars of the last generation of data protection laws. In the field of consumer data protection, these pillars are the purpose specification principle, the use limitation principle and the “notice and consent” model. Nevertheless, the complexity of data processing, the power of modern analytics and the “transformative” use of personal information drastically limit the awareness of consumers, their capability to evaluate the various consequences of their choices and to give a free and informed consent. To respond to the above, it is necessary to clarify the rationale of the “notice and consent” paradigm, looking back to its origins and assessing its effectiveness in a world of predictive analytics. From this perspective, the paper considers the historical evolution of data protection and how the fundamental issues coming from the technological and socio-economic contexts have been addressed by regulations. On the basis of this analysis, the author suggests a revision of the “notice and consent” model focused on the opt-in and proposes the adoption of a different approach when, such as in Big Data collection, the data subject cannot be totally aware of the tools of analysis and their potential output. For this reason, the author sustains the provision of a subset of rules for Big Data analytics, which is based on a multiple impact assessment of data processing, on a deeper level of control by data protection authorities, and on the different opt-out model

Data Analysis With Map Reduce Programming Paradigm

Abstract In this thesis, we present a summary of our activities associated with the storage and query processing of Google 1T 5-gram data set. We rst give a brief introduction to some of the implementation techniques for the relational algebra followed by a Map Reduce implementation of the same operators. We then implement a database schema in Hive for the Google 1T 5-gram data set. The thesis will further look into the query processing with Hive and Pig in the Hadoop setting. More specially, we report statistics for our queries in this environment

Platforms as service ecosystems: lessons from social media

The growing business expansion of social media platforms is changing their identity and transforming the practices of networking, data and content sharing with which social media have been commonly associated. We empirically investigate these shifts in the context of TripAdvisor and its evolution since its very establishment. We trace the mutations of the platform along three stages we identify as search engine, social media platform and end-to-end service ecosystem. Our findings reveal the underlying patterns of data types, technological functionalities and actor configurations that punctuate the business expansion of TripAdvisor and lead to the formation of its service ecosystem. We contribute to the understanding of the current trajectory in which social media find themselves as well as to the literature on platforms and ecosystems. We point out the importance of services that develop as commercially viable and constantly updatable data bundles out of diverse and dynamic data types. Such services are essential to the making of the complementarities that are claimed to underlie ecosystem formation

Challenges of Utilizing Open Data in Global Organizations Big Data Analytics

Useat organisaatiot ovat viime vuosina ryhtyneet avaamaan dataansa kaikkien vapaasti saataville. Avoimelle datalle ei ole vielä olemassa vakiintunutta määritelmää, mutta usein avoimella datalla tarkoitetaan dataa, johon kuka tahansa voi avoimesti päästä käsiksi, käyttää, muokata ja jakaa sitä mihin tahansa käyttötarkoitukseen. Big data on avoimen datan tavoin hyvin uusi ilmiö, jolla tarkoitetaan erityisen suurten ja järjestämättömien tietomassojen keräämistä, säilyttämistä sekä analysointia tietoteknisten ratkaisujen avulla. Tutkimuksessa keskityttiin suurten ja sisällöltään vaihtelevien avoimien data-aineistojen tarkasteluun, jolloin puhutaan avoimesta big datasta. Tutkimusongelmana oli tietämättömyys siitä, mitä haasteita esiintyy avoimen datan hyödyntämisessä osana globaalin organisaation big data analytiikkaa. Tutkimus toteutettiin tarkastelemalla avoimien datalähteiden saatavuutta, kokonaisuutta ja laatua, dataformaattia, arkkitehtuurikuvausta ja rajapintoja, käyttöehtoja, kustannuksia sekä metadatan kuvaamista. Lisäksi tarkasteltiin data-aineiston maantieteellistä kattavuutta, havaintotarkkuutta sekä ajanjaksoa. Tutkimus toteutettiin laadullisena dokumenttisanalyysinä, joka hyödyntää eksploratiivisen tutkimuksen strategiaa. Tutkimuksen havaintoina määriteltiin seitsemän haastetta: tiedostomuotojen eroavaisuudet, puutteet metadatassa, erot havaintotarkkuuksissa, maantieteelliset rajoitteet, heikko arkkitehtuurikuvaus ja rajapinnat, eroavaisuudet datan laadussa sekä heikko saatavuus ja löydettävyys. Avoimien datalähteiden yhdisteleminen on haastavaa ja työlästä eikä aineistojen sisältämää dataa kuvata usein tarpeeksi tarkalla tasolla. Ratkaisuvaihtoehtona avoimen datan yhtenäistämiselle tutkimus esittää erillisen avoimen datan standardin määrittämistä. Standardin tulisi pitää sisällään yksiselitteinen määritelmä avoimelle datalle sekä ehdot sille, miten avointa dataa tulisi tarjota ja avata uudelleenhyödynnettäväksi

Bounded generativity: contextualising interdependencies between architecture, ecosystem, and environment in digital product innovation

Existing theorisation on digital product innovation remains predicated on a particular architectural form (modularity) and mode (unbounded generativity) of organising at scale participation of heterogeneous actors in an ecosystem. Despite the widely accepted role of product architectures in organising digital product innovation there has been limited academic engagement beyond the dynamics of modular design and its proximate context of the ecosystem. While contextualist research within information systems acknowledges the existence of wider systemic conditions underlying IS innovation, this has not received adequate attention within digital product innovation. This thesis builds on existing literature to understand the nature of interdependencies between the architecture, its proximate context of the ecosystem, and the distant context of the wider environment with the aim of developing a contextualised theory of digital product innovation for an alternative architectural form. To augment and extend existing theory, this research studies the design and development of an agent-based simulation model for forced displacement. It uses Kleine’s Choice Framework, adapted for this study, to understand how different conditions of possibility within the proximate and distant contexts shape operational and substantive choices within a digital product’s ongoing development. It follows a process research approach to unpack the sequence of events, its constituent elements, and causal trajectories over time. It is based on an in-depth case study constructed through year-long field work with the development team along with the study of associated documents and reports. The research contributes to the theory on digital product innovation by unpacking how this trilateral interdependency creates opportunity structures at different stages of the development process which shape and bound the generative potential of digital products. This thesis demonstrates how this occurs through complementary resource-relationship configurations which negotiate the systemic conditions of multiple environmental drivers and technical conditions of a hybrid digital architecture