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
Rapid and unprecedented changes to the unemployment laws have created both a tidal wave of transactions that need to be processed, and significant changes to the programmed business processes that execute those transactions. Those business processes are entombed in old COBOL code on old mainframe systems. People are starting to panic about needing COBOL programmers. Yes, but.…
Artificial intelligence (AI), machine learning (ML), and big data are expected to have a huge impact on how we live and what we choose to do. Most categories involving these technologies focus on specific lifestyle items like shopping, dining, movies, and so forth. But such customer-centric categories lack specificity from the investor’s eye. In this Advisor, we redrew the categories on which AI/ML and big data startups should focus.
Executive Update
Autonomous Machines Move to the Fast Lane
In a broad Arthur D. Little (ADL) study, we interviewed 30+ industry and technology experts along the automotive value chain (i.e., OEMs, suppliers, distributors, and end customers) in Europe, North America, and Asia. This Executive Update highlights the biggest challenges, barriers, and implications for vehicle design and the industry’s business models.
In Part X of this Executive Update series on customer experience (CX) management in the enterprise, we examine survey findings pertaining to how organizations view their CX efforts to date.
in this edition of The Cutter Edge, we explore decision making lessons in wartime medicine, the impact of COVID-19 on the business technology sector, managing risk with AI and machine learning, and more!
As a result of the pandemic, we are witnessing increasing interest by organizations for utilizing natural language processing and speech recognition solutions targeted at customer engagement and support — particularly in the form of smartbots, intelligent assistants, conversational computing, and other applications designed to automate customer requests and assist human agents with contact/call center operations. In this Advisor, we examine some of the types of NLP and speech solutions available to organizations and consider some of the issues to keep in mind when it comes to employing such offerings.
Many organizations today struggle with a strong disconnect between their business model and their IT systems, data distributed over a large number of nonintegrated IT systems, manual interfaces between incompatible applications, or difficulties with EU GDPR compliance. We can trace most, if not all, of these issues back to an abundance of unspecific, inflexible, and nonaligned data models underlying the applications these organizations use to conduct their business. Often, these data models have been developed with insufficient business involvement and in isolation from each other — or have been purchased from vendors with little concern for the actual needs of the organization. In this Advisor, we briefly describe the origins of some of these issues.
In my short series of Data Architecture Advisors, “Data Architecture — Containing the Lakehouse,” and “Data Architecture — Out of the Lakehouse, Into the Lake,” I first introduced the newcomer on the block — the data lakehouse — and then discussed the life of one of its parents, the data lake. What about the other parent, you may ask. You may have heard that it’s dead. To paraphrase Mark Twain’s alleged riposte: “reports of its death have been greatly exaggerated.”