Damian Chapman

Humankind is entering a new phase in its evolution as data and digital tools become increasingly inseparable from our industries and domestic lives. The proliferation of Artificial intelligence automating collection, distribution, analysis and communication of data for business, utilities and social-media is driving machine learning with design processes now capable of functioning without sole reliance on human intervention. Cyber-physical systems now seamlessly interact via the Internet of Things, enabling humankind to enter a data driven knowledge phase in its evolution that is dynamic and reshaping the building blocks of social bonds in a collective emergent society.

Together, human and machine, are augmenting both life-enhancing efficiencies and energy industry productivity. This is happening through collaboration and language in the form of algorithms, interacting with new technologies and constructing human and machine hybridity. However, within digital networks, a new knowledge space is adapting and growing problems in data overload. With this problem arrives the need for developing improved insights for design to innovate user languages. Better insights from steering new approaches in methods design for understanding data, utilising smarter algorithm design from emergent interstices in new data visualisation is able to make sense of the increasing lakes of raw unstructured data requiring context.

This research focuses on identifying the growing needs for ‘making-sense’ of data through visualisation tools which require contextual understanding in both cyberspace and physical world experiences. The research explores challenges in the linkage between the cyber-physical new knowledge space of data emerging with the excessive amount of raw data and intending possibilities. The aim of the research is to improve User Interface Design through better visualisation infographics focused on remote applications in the energy industry.

This study develops effective measuring of data streams through a Design framework to identify benefits for management of complex systems. These systems are to enhance performance monitoring in the energy industry’s optimisation approaches of energy production as part of achieving NetZero.

The findings of the research show how a mapping approach could augment potential for users to visualize and make sense of layers of information. From streaming data inputs, which both consider patterns and behaviours from trace in workflow behaviours however importantly integrating emergent knowledge streams for design. Grounded Theory was used as a basis for an abductive constructionist methodology. This informed the development of a circular model by observing cause and effect through participatory design insights and feedback cycles. These methods enable and measure greater opportunity for new thinking, building collective intelligence for new knowledge in energy production systems design.

The research integrates participatory learning to involve more diverse and abductive input activities, ‘making’ using prototyping and reflective tools with a mixture of qualitative and statistical measurement approaches. From research feedback loops and insights developed from participatory design workshops, the research is integrating emerging patterns for designing visualisation prototypes. This increases visual languages that enable the building of new interactive apparatus, algorithms and dynamic cognition.

Key terms: Design, Organisational systems, Circularity, Data, Feedback