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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Sameer Kher starts us off with a discussion on the important topic of simulations. Simulations have been used for years in modeling and design. More recently, they have become a core aspect of digital twins, simulating outcomes during design phases and in using real data during operations. Kher describes the phases associated with applying simulation in digital twins as well as use of simulation in areas such as predictive maintenance. He concludes with describing how you build, validate, and deploy a simulated digital twin.
In Part II of this Executive Update series on information superiority, we take the concept further and examine the four dimensions of information superiority in greater detail.
It takes a village and ecosystem to provide all the capabilities found in digital twins. To highlight this challenge, the articles in this issue of CBTJ bring to life the various aspects of digital twins uses and applicability.
This edition of The Cutter Edge discusses why it's critical for business leaders to understand that success depends more on how they develop people than how they deploy technology; the patterns and techniques that are useful for keeping digital and data architectures open to changes, and more!
Federated learning is an emerging distributed machine learning (ML) development method that allows different organizations to collaborate on artificial intelligence (AI) projects while protecting their sensitive data. Federated learning is currently seen as especially applicable in healthcare/medicine and banking/finance; however, I believe it will prove important for many heavily regulated industries seeking to develop AI applications.
In a recent webinar, Andreas Schlosser, Alan Martinovich, and Philipp Seidel examined the state of the automotive industry and what it might look like after the pandemic. They urged industry players — manufacturers, dealers, distributors, OEMs, and the full supply chain — to make bold decisions right now to be ready for a “new normal.” In this Advisor, we share some of the answers to questions participants asked about the actions carmakers should take now to set themselves up to win in the post-corona era.
The emergence of the IoT has led to a need for different characteristics and technical capabilities in cellular networks, which have continued to develop and will, with the emergence of 5G, address the key needs of the IoT. And with more and more “things” connected, leading to significantly higher connection density, the risk of interference increases. This has been another historical challenge to the IoT that 5G will address. All this means that 5G will enable an entirely new range of applications.
While the goals of the traditional view of EA are still very valid today, this is not where the scope of EA should stop. If the EA organization wants to become a strategic partner with the business, the EA value measurement program must demonstrate that the EA team understands the key strategic metrics that the business values and can positively impact these key strategic metrics.