The Clinger-Cohen Act of 1996 was enacted to address many of the problems related to Fed. information technology mgmt. In 1997 GAO developed guidance that provides a method for evaluating & assessing how well a Fed. agency is selecting & managing its information technology resources & identifies specific areas where improve. can be made. The Info. Technology Investment Mgmt. (ITIM) framework enhances this guidance by identifying critical processes for successful information technology invest. & organizing these processes into a framework of increasingly mature stages. Chapters: overview, components, & uses of ITIM; uses of ITIM; & critical process for the ITIM stages. Charts & tables.

From the individual to the largest organization, everyone today has to make investments in IT. Making a smart investment that will best satisfy all the necessary decision-making criteria requires careful and inclusive analysis. This textbook provides an up-to-date, in-depth understanding of the methodologies available to aid in this complex process of multi-criteria decision-making. It guides readers on the process of technology acquisition ? what methods to use to make IT investment decisions, how to choose the technology and justify its selection, and how the decision will impact the organization.Unique to this textbook are both financial investment models and more complex decision-making models from the field of management science so that readers can extend the analysis benefits to enhance and confirm their IT investment choices. The wide range of methodologies featured in the book gives readers the opportunity to customize their best-fit solutions for their unique IT decision situation. This textbook is especially ideal for educators and students involved in programs dealing with technology management, operations management, applied finance, operations research, and industrial engineering.A complimentary copy of the ?Instructor's Manual and Test Bank? and the PowerPoint presentations of the text materials are available for all instructors who adopt this book as a course text. Please send your request to [email protected].

If managed wisely, investments in information technology (IT) can enrich people's lives and improve organizational performance. For example, during the last decade the Internet has matured from being a technical novelty to a national resource where citizens can visit the Library of Congress or file their tax returns. Some organizations have realized substantial improvements in processing data and information by switching from centralized mainframe computing to decentralized personal computers linked by local area networks. The ability of software applications to locate and correlate relevant data in a data warehouse permits organizations to discover unknown fiscal or physical resource relationships and thus provide appropriate assistance where there had been none. The Clinger-Cohen Act of 1996' was enacted to address many of the problems related to federal IT management.

This report addresses the Immigration and Naturalization Service's (INS) management of information technology (IT) investments. Each year INS invests hundreds of millions of dollars on IT systems and activities. We found that INS has established some important capabilities for managing these investments, but it has considerable work ahead to fully implement mature and effective processes. We are making recommendations to strengthen INS' investment management capabilities.

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.