Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
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Explainable artificial intelligence (XAI) is becoming a critical component of operations undertaken in the financial industry. It stems from the growing sophistication of state-of-the-art AI models and the desire for them to be deployed in a safe, understandable manner. Responsible artificial intelligence (RAI) principles ensure that machine learning technology is applied in a transparent way while safeguarding the interest of each player in the financial ecosystem. Not surprisingly, banking and financial services regulators have shown an interest in adopting XAI and RAI techniques to help them meet the need for model governance, operational servicing, and compliance in the digital world.
Tim Giuliani discusses how digital twins are being used for regional planning by the city of Orlando, Florida, USA. Here, digital twins are employed via virtual reality to offer an immersive environment so users can experience the impact of various scenarios. The article shows how organizations are bringing together vendors and partners to integrate data, digital twins, and emerging technologies.
Colin Dominish highlights a variety of revenue opportunities that could be realized by applying digital twins to real estate and buildings. He discusses improving building performance and some opportunities to enhance tenant experiences.
Ruth Kerrigan and her colleagues describe the application of digital twins to building-performance twins at the University of Glasgow, Scotland. They discuss tracking electricity and heating performance in campus buildings and lessons learned, many of which are organizational in nature, not technological. The authors conclude with a methodology for the deployment of performance digital twins and recommendations for addressing some of the issues they encountered.
Carl Faulkner presents a mining industry case study with a focus on data collection, integration, and storage challenges. The article includes lessons learned from the application of a solution designed to facilitate user-friendly access to digital twins as well as the importance of connecting digital twins to other business systems to get the most value.
Sustainability has become a recent focus for digital twins. David McKee and Tim O’Callaghan present a case study from a UK town using digital twins to achieve its net zero obligations. The authors discuss the use of tools to visualize historical data and utilizing new data from various sources to simulate possible outcomes and manage risk.
This issue of Amplify takes a lessons-learned approach — revisiting the concept of digital twins with an eye toward how organizations are using digital twins, the implementations, and the challenges encountered. The articles in this issue were selected to provide important lessons about real-world deployments and case studies. They also provide insights about industries where digital twins have gained early traction and what types of organizations are adopting digital twins, including those focused on sustainability and those seeking to enable Metaverse scenarios.
Alexander Weber discusses the use of digital twins in radar systems. This is a good example of using digital twins to simulate products that are costly to build (especially if they are built incorrectly) and their use in addressing compliance requirements. Weber explains how the model was verified and how the simulated data corresponds to the real data.