Actualizing big data analytics for smart cities: A cascading affordance study

Role of big data and technological advancements in monitoring and development of smart cities

Driven by rapid advancements in technology and data science, a revolutionary transformation is sweeping across environmentally friendly cities worldwide. This surge stems from a pressing need to tackle the intricate complexities of urban sustainability, encompassing everything from infrastructure and governance to fragmented design and technological solutions. To effectively manage these complexities and accurately measure, assess, and optimize their sustainability performance, sustainable communities are increasingly tapping into the potential of smart city technologies, particularly big data and its fictionalized applications. This trend culminates in the emergence of smart cities. This article delves into the current state of research surrounding data-driven, environmentally conscious smart cities, aiming to assess the extent to which these two concepts are currently being integrated and identify potential gaps in this field. Through a strong emphasis on evidence-based research, the study underscores the potential of big data technologies to offer innovative approaches for monitoring, comprehending, evaluating, and ultimately managing sustainable urban development. It further highlights the crucial role of data-driven advancements in formulating strategic development policies and operational management procedures, ensuring that environmentally conscious cities can continue to contribute to sustainability goals even amidst rapid urbanization.

Characterizing stewardship and stakeholder inclusion in data analytics efforts: the collaborative approach of Kansas City, Missouri

Purpose

Local governments face increasingly complex challenges related to their internal operations as well as the provision of public services. However, research on how they embrace evidence-based approaches such as data analytics practices, which could help them face some of those challenges, is still scarce. This study aims to contribute to existing knowledge by examining the data analytics practices in Kansas City, Missouri (KCMO), a city that has become prominent for engaging in data analytics use through the Bloomberg’s What Works Cities (WWC) initiative with the purpose of improving efficiency and enhancing response to local constituents.

Design/methodology/approach

This research conducted semistructured interviews with public servants who had data analytics experience at KCMO. Analysis looked for common and emerging patterns across transcripts. A conceptual framework based on related studies is built and used as the theoretical basis to assess the evidence observed in the case.

Findings

Findings suggest that data analytics practices are sponsored by organizational leadership, but fostered by data stewards who engage other stakeholders and incorporate data resources in their analytical initiatives as they tackle important questions. Those stewards collaborate to nurture inclusive networks that leverage knowledge from previous experiences to orient current analytical endeavors.

Research limitations/implications

This study explores the experience of a single city, so it does not account for successes and failures of similar local governments that were also part of Bloomberg's WWC. Furthermore, the fact that selected interviewees were involved in data analytics at least to some extent increases the likelihood that their experience with data analytics is relatively more positive than the experience of other local government employees.

Practical implications

Results suggest that data analytics benefits from leadership support and steering initiatives such as WWC, but also from leveraging stakeholder knowledge through collaborative networks to have access to data and organizational resources. The interplay of data analytics sponsored activities and organizational knowledge could be used as means of assessing local governments’ existing data analytics capability.

Originality/value

This study suggests that data analytics practices in local governments that are implementing a smart city agenda are knowledge-driven and developed incrementally through inclusive networks that leverage stakeholder knowledge and data resources. The incrementality identified suggests that data analytics initiatives should not be considered a “blank slate” practice, but an endeavor driven and sustained by data stewards who leverage stakeholder knowledge and data resources through collaborative networks.

A novel framework for bringing smart big data to proactive decision making in healthcare

Big data have shown their great potential value to serve many aspects of human life. Due to complexity of the medical and healthcare big data in real life, traditional big data analysis methods are difficult to be dealt with. Therefore, a single method is unable to analyze and manage heterogeneous big data sources. To utilize data fully from the perspective of decision-making, we propose a novel framework which guides the healthcare big data to be smartly and proactively processed for decision-making without user interventions. The framework contains five stages, which are intelligent data cleaning, customized data fusion, analysis mapping, exploratory visualization analysis, and generation of decision-making reports. It also enables learning from the data and correlating them with the existing human knowledge. Subsequently, a smart big data-driven application exhibits innovative management in intelligent healthcare. The proposed framework provides the guidelines of the best practices of big data-driven analysis for intelligent healthcare according to our practical applications. The platform provides the appropriate reference for the big data-driven innovation of management in intelligent healthcare.