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Data-Driven Innovation

Selected e-articles

Abstract: Artificial Intelligence (AI) represents a collection of tools and methodologies that have the potential to revolutionise various aspects of human activity. Earth observation (EO) data, including satellite and in-situ, are essential in a number of high impact applications, ranging from security and energy to agriculture and health. In this paper, we present the AI4Copernicus framework for bridging the two domains within the European context to enable data-centred innovation. In order to achieve this goal, AI4Copernicus has developed and enriches the European AI-on-demand platform with a number of application bootstrapping services and tools to accelerate uptake and innovation, whilst it provides integration over AI-on-Demand services and the Copernicus ecosystem, targeting the highly successful Data and Information Access Service (DIAS) Cloud platforms. More specifically, by employing procedures for onboarding and validating models and tools, and by utilising a host of meticulously reviewed and supervised open calls-enabled projects, and containerisation best-practices, AI4Copernicus deployed and made available several products on DIAS platforms. Moreover, these products and resources have been made available on the AI-on-Demand platform catalogue for discovery, use and further development. The AI4Copernicus framework is being used by a number of business-driven projects and SMEs spanning several application domains. This article provides an overview of the European AI and EO context as well as the AI4Copernicus technological framework and tools offered. Further, we present real world use-cases as well as a community-centred evaluation of our framework based on usage and feedback received from several projects.

Abstract: There is an increasing emphasis on utilizing ICT to drive global governmental transformation to enhance efficiency and cost-effective service delivery. Smart governance represents a novel and data-driven progressive approach, prioritizing intelligence in operations, upholding an exceptional standard of public administration, and contributing to the development of smart cities and nations. A smart city uses advanced technology and innovation to augment urban life and efficiency to ensure sustainability and a smart nation extends these principles across regions. Although smart governance is a priority in building smart cities and nations, its challenges and strategies are still not well-defined from the perspective of developing a smart nation and city. Smart Bangladesh is an inclusive digital transformation initiative and a grand vision of the government, advancing towards becoming a developed, prosperous, and smart nation by focusing on four key pillars: smart citizens, smart government, smart economy, and smart society. This study involved interviews with multi-level stakeholders and served as a preliminary step toward providing insights into and understanding the significant challenges and priorities in transforming a smart country and building smart cities. The research identifies fourteen prime challenges of smart governance that are pivotal for transforming Bangladesh into a smart nation and creating smart cities. Among these, stakeholders particularly emphasize the need for administrative reform, robust smart infrastructure, finance, uninterrupted electricity, strong data privacy and security, and effective big data management as crucial to the success of the country's vision. The analysis proposes a conceptual framework based on stakeholders' priorities that can serve as a practical guideline for practitioners to develop a strategic roadmap for effective preparedness to transition to a smart nation and build smart cities. The study fills a research gap in governance theory concerning the evolution of transformative technology-based governance, particularly emphasizing the significance of smart governance in the development of smart nations and cities.

Abstract: Quantum computing is a new paradigm that will revolutionize various areas of computing, especially cloud computing. Quantum computing, still in its infancy, is a costly technology that can operate in highly isolated environments because of its rapid response to environmental factors. This makes quantum computing a challenging technology for researchers to access. These problems can be solved by integrating quantum computing into an isolated remote server, such as a cloud, and making it available to users. Furthermore, experts predict that quantum computing, with its ability to swiftly resolve complex and computationally intensive operations, will offer significant benefits in systems that process large amounts of data, like cloud computing. This article presents the vision and challenges for the quantum cloud computing paradigm that will emerge with the integration of quantum and cloud computing. Next, we present the advantages of quantum computing over classical computing applications. We analyze the effects of quantum computing on cloud systems, such as cost, security, and scalability. Besides all of these advantages, we highlight research gaps in quantum cloud computing, such as qubit stability and efficient resource allocation. This article identifies the advantages and challenges of quantum cloud computing for future research, highlighting research gaps.

Abstract: Integrating Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often must comprehensively address the multifaceted dimensions of the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. The study uses a meticulous approach guided by established methodologies to achieve this aim. The chosen methodology ensures the rigour and validity of the review, aligning with PRISMA 2020 guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field. Our analysis shows that integrating AI with IoT improves operational efficiency, service personalisation, and data-driven decisions in healthcare, manufacturing, and urban resource management. Real-time machine learning algorithms and edge computing solutions are set to revolutionise IoT data processing and analysis by improving system responsiveness and privacy. However, increasing concerns about data privacy and security emphasise the need for new regulatory frameworks and data protection technologies to ensure the ethical adoption of AI-driven IoT technologies.

Abstract: Developing artificial intelligence (AI) and machine learning (ML) methods that can accelerate scientific discoveries and advance science has become one of the important research directions for the AI/ML research community. It has been gaining increasing attention from researchers in diverse scientific areas, including biomedical science, materials science, climate science, physics, chemistry, and many others. Data-driven AI/ML innovations to enable reliable predictions and optimal decision making for scientific discoveries face several critical challenges, among which are high system complexity, large search space, incomplete knowledge, and small data, all of which demand novel strategies to effectively address them. Meeting these challenges and thereby accelerating scientific discoveries and industrial innovations, calls for research that can take full advantage of the latest advances in AI/ML to integrate data-driven techniques with scientific knowledge and is able to execute them in modern high-performance computing (HPC) environments at scale. This Patterns special collection “Accelerating scientific discoveries through data-driven innovations” features articles that showcase the promising roles of AI/ML and data-driven modeling in accelerating scientific discoveries and may inspire the next wave of data-driven innovations in various scientific domains.

Abstract: In the race to achieve market dominance and stay competitive in a disruptive and rapidly changing business environment, many firms have invested significantly in data-driven innovation capabilities (DDIC). Surprisingly, the role that strategic market agility plays in achieving a firm's competitiveness through DDIC is understudied. Addressing this gap, we conceptualize the DDIC research model using the resource-based view, dynamic capability, market orientation and disruptive innovation theory. We empirically test the model with survey data from 312 Australian managers. We illuminate the significance of strategic market agility as a key mediator between DDIC and strategic competitive performance. We discuss the implications of our findings for theoretical contributions and managerial implications.

Abstract: Data management might not be the obvious poster child for business in 2020, but it's been critically important during the scramble to pivot and adapt in response to the pandemic. Overnight, organisations have had to deal with a massive influx of data, as digital engagement replaced in-person interaction. Data management has become critically important during the scramble to pivot and adapt in response to the pandemic. The enterprises that ‘win’ when it comes to data governance are those that take it in their stride and embed it in all that they do from the start. Ana Gillan of Cloudera explains the benefits that governance brings to businesses, such as making data consistent, of a higher quality and more accurate while supporting employees to focus on what really matters – innovation.

Abstract: Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.

Abstract: In recent years, strategies focused on data-driven innovation (DDI) have led to the emergence and development of new products and business models in the digital market. However, these advances have given rise to the development of sophisticated strategies for data management, predicting user behavior, or analyzing their actions. Accordingly, the large-scale analysis of user-generated data (UGD) has led to the emergence of user privacy concerns about how companies manage user data. Although there are some studies on data security, privacy protection, and data-driven strategies, a systematic review on the subject that would focus on both UGD and DDI as main concepts is lacking. Therefore, the present study aims to provide a comprehensive understanding of the main challenges related to user privacy that affect DDI. The methodology used in the present study unfolds in the following three phases; (i) a systematic literature review (SLR); (ii) in-depth interviews framed in the perspectives of UGD and DDI on user privacy concerns, and finally, (iii) topic-modeling using a Latent Dirichlet allocation (LDA) model to extract insights related to the object of study. Based on the results, we identify 14 topics related to the study of DDI and UGD strategies. In addition, 14 future research questions and 7 research propositions are presented that should be consider for the study of UGD, DDI and user privacy in digital markets. The paper concludes with an important discussion regarding the role of user privacy in DDI in digital markets.

Abstract: The EU General Data Protection Regulation (GDPR) introduces a new right to data portability, which allows users to move their personal data to other platforms, potentially affecting competition between rival platforms offering similar (homogeneous/substitute) products or services within the European Union. However, it is still unclear what effects this new regulation could have on competition and, consequently, on innovation in digital markets. Therefore, this paper analyzes the effect of data portability driven by competition on the data-driven innovation response of online platforms such as Spotify, Google, and Facebook. We conduct an empirical analysis of Spotify, which is an online platform facing competition within the EU, and perform a comparison between data portability to number portability of the telecommunication sector to predict the future impact of the new regulation. Finally, we compare the observations on Spotify with Facebook and Google, which are companies in winner-takes-all markets. We argue that online platforms like Spotify, which face competition within the EU, will invest in two forms of data-driven innovation due to the effect of data portability. These types are ‘exploitation-innovation,’ by improving the existing technology, and ‘exploration-innovation’ by developing new technology. In ‘exploitation-innovation,’ firms, like Spotify, will increase investments in data-driven innovation to enhance users' engagement and retention to avoid churn. In ‘exploration-innovation,’ these firms will invest in data-driven innovation to develop new algorithms to include data from customers acquired from their competitors. On the contrary, online platforms, like Facebook or Google, which do not face real competition, will not have a substantial need to invest in data-driven innovation solely due to data portability.

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