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.
Recently Published
Many organizations lack a well-defined, rationalized business vocabulary as a basis for information management. As a result, the data they rely on results in many business challenges, as discussed in this Advisor.
Controls are only as good as their ability to be enforced. This Advisor reviews the architecture and enforcement mechanisms used to steer developers, DevOps, and infrastructure engineering staff through the use of compliance-as-code controls.
Cloud computing, with its scalability and relatively low cost, has traditionally been the technology environment of choice for supporting digital twins. Today, edge computing has emerged as a promising alternative. This Advisor explores the benefits of edge computing over cloud computing.
In this on-demand webinar, you'll learn about an agile approach tailored for IT and non-IT activities. Jon Ward’s Agile Lineout uses behavioral theory, lean principles and agile wisdom to help teams create high-value solutions quickly.
In a recent survey, Cutter Consortium looked into the technologies that companies are interested in using to support their intelligent process automation (IPA) initiatives. In this Advisor, we take a closer look at the top two: predictive analytics/machine learning (ML) and robotic process automation (RPA).
Architecting over time is even more challenging than architecting over space. Time is not just one more dimension. Time brings relative unknowns that make it difficult, even impossible, to define requirements in advance. How do we build for a universe that we cannot see?
Neural networks and other machine learning (ML) model development typically requires large amounts of data for training and testing purposes. Because much of this data is historical, there is the chance that the artificial intelligence (AI) models could learn existing prejudices pertaining to gender, race, age, sexual orientation, and other biases. This Advisor explores these and other issues around data that can also contribute to biases and inaccuracies in ML algorithms.
This edition of The Cutter Edge explores the promise and potential of quantum computing, how IT departments can convince finance and procurement about the benefits of the cloud, and more.