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
A recent survey by Arthur D. Little and Blockchain in Transport Alliance confirms that blockchain users within the transport industry believe that the technology facilitates greater transparency and efficiency, resolving key challenges around inefficiency and information asymmetry among supply chain players.
Enterprises need access to data-driven insights faster than ever before. Analytics use cases have evolved from traditional, precanned reports to self-service and guided analytics. Enterprises exploring solutions to these challenges are steadily embracing the cloud data warehouse. These platforms provide agility, scalability, and simplicity that enable the enterprise to focus on data solutions rather than spending valuable effort on peripheral overheads.
The edition of The Cutter Edge explores how agile budgeting can create value as a revenue stream generator, why communication is so essential in a digital shift, how to improve software system design, and more.
The number of Internet of Things (IoT) devices — ranging from connected consumer products like smart speakers, TVs, refrigerators, and stoves to sensor-enabled industrial machinery and business equipment — continues to proliferate, but many of these devices lack strong built-in security features. This Advisor considers the application of AI and blockchain technologies for strengthening the cybersecurity capabilities of IoT devices and networking environments as well as future developments in this area.
Cutter Consortium Senior Consultant Barry M. O’Reilly introduces the concept of residuality theory and its application to the complex relationships that exist between different risks in the modern business environment. Expanding on the issues of complexity versus complication in the world of enterprise software, O’Reilly shows how the principle of residuality enables organizations to anticipate the impact of chains of interconnected risks.
In the second installment of their webinar series, “Using AI/Machine Learning to Manage Risk,” Cutter Senior Consultants Michael Eiden, Craig Wylie, and Tom Teixeira answered some questions about new risk models that utilize artificial intelligence (AI) and machine learning (ML) to understand and respond to the changing business landscape.
While the software industry is currently grappling with ideas of complexity and resilience, there has been very little in the way of concrete actions or activities that software engineers can use to actually design systems. Residuality theory answers this need and draws on complexity science and the history of software engineering to propose a new set of design techniques that make it possible to integrate these two fields.
My studies of enterprise architecture (EA) practices in multiple diverse organizations have identified several consistent patterns describing the size and structure of architecture functions that companies tend to find optimal for their needs. We can use these empirically observed generalities to synthesize a simple, heuristic three-step approach for designing organization-specific architecture functions.