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In this Executive Update, the first of three related articles, we examine financial services as an example of a highly regulated industry and outline the regulatory landscape that creates points of tension for cloud adoption. We also incorporate perspectives from a differentiated range of stakeholders, including lawyers, technologists, compliance executives, and outsourcing managers.
December 20, 2016 | Authored By: Leslie Willcocks, Daniel Gozman
In the last Executive Update, I reported that Cutter Consortium recently conducted a survey of more than 170 companies from around the world.1 In
January 31, 2002 | Authored By: Paul Harmon
In this Executive Report we address these questions and explore what social networks mean to you and your enterprise. Specifically, we begin by examining the dark side of social networks, including dangers, risks, and privacy and security issues. We then look at the future of social networks, identifying and discussing several yet-to-be explored trends and your potential opportunities. We conclude by looking at the impact social networks have on the business and what IT can do to take advantage of its potential.
August 31, 2007 | Authored By: San Murugesan
What strategies do you apply to modernizing a product code base? What results do you get with those strategies? This Advisor takes a retrospective look at a past project, both to describe the strategies my colleagues and I used to rearchitect the product and to validate the effectiveness of those strategies with two technical debt assessments via Cutter's Technical Debt Assessment and Valuation practice. 
April 7, 2016 | Authored By: John Heintz
In this Advisor, we look at how teams can lose their coordination and their focus on delivering quality work while at the same time accumulating a significant amount of technical debt. This can be a hard balance to manage: delivering quality outcomes but sacrificing the longer-term success of the team by cutting corners and delaying necessary work until later for the sake of short-term delivery.
September 7, 2016 | Authored By: Gustav Toppenberg
Neural networks and other ML model development typically use large amounts of data for training and testing purposes. Because much of this data is historical, there is the risk that the AI models could learn existing prejudices pertaining to gender, race, age, sexual orientation, and other biases. This Advisor explores how the Data & Trust Alliance consortium created an initiative to help end-user organizations evaluate vendors offering AI-based solutions according to their ability to detect, mitigate, and monitor algorithmic bias over the lifecycle of their products.
December 14, 2021 | Authored By: Curt Hall