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This chapter provides an integrated overview of digital implementation in smart city ecosystems, focusing on the data ecosystem, the data value chain, and the digital implementation in smart cities. It acknowledges the opportunities and challenges presented by the growth of big data and its impact on delivering public services and coordinating local ecosystems in smart cities. This chapter introduces the concept of a smart city data ecosystem and explores the implementation of digital solutions, highlighting the role of local management and internal and external providers. It examines the digitalization of processes and services, technological infrastructure, and social media in the context of the smart city, emphasizing the interaction between local and national data ecosystems. This chapter incorporates interviews with smart city managers, policymakers, and technicians involved in the digital development process, offering valuable insights into the practical implementation and usage of data management within smart cities and the potential impact on urban development.

The multimedia data are also known as interactive data. The multimedia is progressively turning into the “greatest big data” which are the most imperative and important hotspot for bits of knowledge and data. The multimedia data also provide incredible open door for the multimedia computing in the big data centric as a functioning disciplinary research field. As per current technological usage in terms of Internet or smart devices, the data manipulate in the form of digital. Massive multimedia data have been produced in the different forms like text, image, video, and audio which is shared among vast number of people. The multimedia data are real-time unstructured, heterogeneous, and multimodal. It has vast scope to mine model, learn, and analyze the service provided by multimedia. Of course, some primarily level challenges need to be addressed like analysis, storage, retrieval, and data processing. The most complicated thing in multimedia big data (MMBD) analytics is that the computer cannot understand higher level of semantics. The quality of experience (QoE) is the most evolving part of MMBD which are directly intended with storage and performance. MMBD are highly resource intensive. They often require dedicated processing capabilities in terms of graphical processing unit (GPU). An advance-level storage-related mechanism is also needed for efficient parallel processing, transmission, and presentation. Generally, non-multimedia data are always forming in text which is normally understood by machine. The multimedia data always in the form of videos are easily understood by human compared to textual data, but it is more complex task to make it understandable to machines. The MMBD performs the task by converting the human language to computer language in an efficient manner. This chapter is also introducing salient features of MMBD. The main aim of this chapter is to cover the fundamentals for MMBD computing and feasibility study. The chapter explores the technical problems and challenges to be addressed. It also focuses on methodologies and approaches that are available from the perspectives of MMBD computing life cycle. The chapter may be beneficial for the readers to understand the features, importance and application of MMBD.

Nowadays, there are billions interconnected devices forming Cyber-Physical Systems (CPS), Internet of Things (IoT) and Industrial Internet of Things (IIoT) ecosystems. With an increasing number of devices and systems in use, amount and the value of data, the risks of security breaches increase. One of these risks is posed by open data sources, which are databases that are not properly protected. These poorly protected databases are accessible to external actors, which poses a serious risk to the data holder and the results of data-related activities such as analysis, forecasting, monitoring, decision-making, policy development, and the whole contemporary society. This chapter aims at examining the state of the security of open data databases representing both relational databases and NoSQL, with a particular focus on a later category.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

By the end of this chapter, you should be able to demonstrate an understanding of:

The importance of big data in the information revolution.

The resource-based view of the firm and dynamic capabilities as they relate to big data.

The use of big data in marketing decisions.

Consumer security concerns over the storage and processing of big data.