Expert Guidance to Ensure Business Agility & Top-notch Systems & Software
Make your software, systems, and software organization a source of sustainable competitive advantage in an era characterized by constant change. Cutter’s community of international experts provides a steady stream of alerts, updates, reports, and virtual events to keep your teams on the cutting edge of new developments in software engineering excellence, product management, and enterprise agility.
This Advisor describes what an organization needs to develop and implement the organizational skills and capabilities associated with big data.
In this Advisor, we discuss the social and emotional cognitive aspects of projects.
Sustainability means bringing new functionality to the market while keeping your underlying software assets healthy and nimble. To achieve such balance requires a form of data-driven design and decision making that balances commercial and technical considerations. In this Advisor, we review what is needed in terms of data, design, and decisions.
The IT skills shortage has been around long enough for some to have proposed solutions. Will these ideas work for Industry 4.0? In this Advisor, we explore some past approaches to the skills crisis and see if we can glean any lessons from them.
In Part IX of this Executive Update series on statistical project manangement, we return to the eight volatility metrics to see how we can assign them to project managers, object types, objects, phases, and other levels of analysis.
The right team is key to crafting software systems capable of supporting innovation. Software delivery talent, however, is extremely difficult to find for a multitude of reasons, which we explore in this Advisor.
As AI becomes more visible as a corporate strategic tool, organizations will have to incorporate issues surrounding AI as part of corporate strategy. Pavankumar Mulgund and Sam Marrazzo help us by providing a framework for developing an AI strategy. The authors discuss the “minimum viable model” approach to the development of the underlying AI/ML models, along with the platform on which these models run and the inevitable tradeoffs. They conclude their piece by examining some best practices for the successful implementation of AI initiatives.
The contributions in this issue of CBTJ will help us get up to speed with the current state of AI and to think about some of the issues raised when we look beyond systems that appear to work as intended. Our contributors span industry and academia, and their commentary provides a good way to gain an overview of the problem.