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In the digital marketing era, the strategic importance of data infrastructure has never been more pronounced. This chapter examines 34 case studies across various industries to uncover robust data infrastructures’ pivotal role in enhancing data-driven marketing campaigns. Among these, the selected five studies showcase organizations like Netflix, Amazon, Spotify, Airbnb, and Starbucks, demonstrating innovative uses of technology convergence, including artificial intelligence (AI), big data analytics, and cloud computing, to achieve marketing objectives. Their success stories are a testament to the power of robust data infrastructures, inspiring businesses to leverage their data assets more effectively. Through detailed analysis, this chapter identifies key challenges related to data quality, accessibility, and the integration of disparate technologies within marketing frameworks. It further explores solutions that have led to improved customer personalization, dynamic pricing models, and significant return on investment (ROI) enhancements. These findings provide practical insights that can be directly applied to your business. Additionally, this chapter highlights the emerging focus on sustainable infrastructure practices and the criticality of a unified data approach in optimizing marketing strategies. By synthesizing insights from leading market players, this study helps a comprehensive understanding of how modern data infrastructure underpins the success of data-driven marketing, offering valuable lessons for businesses aiming to leverage their data assets more effectively.

This paper describes the research underpinning the development and evaluation of a brain computer interface (BCI) system designed to be suitable for domestic use by people with acquired brain injury in order to facilitate control of their home environment. The purpose of the research is to develop a robust and user‐friendly BCI system which was customisable in terms of user ability, preferences and functionality. Specifically the human interface was designed to provide consistent visual metaphors in usage, while applications change, for example, from environmental control to entertainment and communications.

The research took a user centred design approach involving representative end‐users throughout the design and evaluation process. A qualitative study adopting user interviews alongside interactive workshops highlighted the issues that needed to be addressed in the development of a user interface for such a system. User validation then underpinned prototype development.

The findings of the research indicate that while there are still significant challenges in translating working BCI systems from the research laboratories to the homes of individuals with acquired brain injuries, participants are keen to be involved in the deign and development of such systems. In its current stage of development BCI is multi‐facetted and uses complex software, which poses a significant usability challenge. This work also found that the performance of the BCI paradigm chosen was considerably better for those users with no disability than for those with acquired brain injury. Further work is required to identify how and whether this performance gap can be addressed.

The research had significant challenges in terms of managing the complexity of the hardware and software set‐up and transferring the working systems to be tested by participants in their home. Furthermore, the authors believe that the development of assistive technologies for the disabled user requires a significant additional level of personalisation and intensive support to the level normally required for non‐disabled users. Coupled with the inherent complexity of BCI, this leads to technology that does not easily offer a solution to both disabled and non‐disabled users.

The research contributes additional findings relating to the usability of BCI systems. The value of the work is to highlight the practical issues involved in translating such systems to participants where the acquired brain injury can impact on the ability of the participant to use the BCI system.

Within smart homes, ambient sensors are used to monitor interactions between users and the home environment. The data produced from the sensors are used as the basis for the inference of the users' behaviour information. Partitioning sensor data in response to individual instances of activity is critical for a smart home to be fully functional and to fulfil its roles, such as correctly measuring health status and detecting emergency situations. The purpose of this study is to propose a similarity‐based segmentation approach applied on time series sensor data in an effort to detect and recognise activities within a smart home.

The paper explores methods for analysing time‐related sensor activation events in an effort to undercover hidden activity events through the use of generic sensor modelling of activity based upon the general knowledge of the activities. Two similarity measures are proposed to compare a time series based sensor sequence and a generic sensor model of an activity. In addition, a framework is developed for automatically analysing sensor streams.

The results from evaluation of the proposed methodology on a publicly accessible reference dataset show that the proposed methods can detect and recognise multi‐category activities with satisfying accuracy, in addition to the capability of detecting interleaved activities.

The concepts introduced in this paper will improve automatic detection and recognition of daily living activities from timely ordered sensor events based on domain knowledge of the activities.