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Seventeen in a series of annual reports comparing business regulation in 190 economies, Doing Business 2020 measures aspects of regulation affecting 10 areas of everyday business activity.
Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. - New case studies include expanded coverage of textual management and analytics - New chapters on visualization and big data - Discussion of new visualizations of the end-state architecture
Powerful tools for using operations metrics to analyze companies in ways that go beyond traditional financial models and statements. Investors and analysts often need to look into a firm's operations more deeply than traditional financial statements and models allow. This book describes newly developed tools for using operations metrics to discern and influence the valuation of a firm. It is the first to present these techniques from a unified perspective: that of operations forensics, which looks at operations management not from the traditional point of view of a manager but from that of an investor or shareholder. After a discussion of financial statements and the useful but incomplete insights they provide, the book covers the three components of operations forensics: operational indicators, operations details that can predict future performance; operational due diligence, methods for verifying companies' claims about operational excellence and valuing their operational assets; and operational turnaround, an innovative approach to buyout and turnaround strategies. The text also offers brief reviews of operations management concepts, real-world examples of operations forensics, and a glossary. The mathematical material gradually increases in sophistication as the book progresses (but can be skipped without loss of continuity). Each chapter concludes with a "Takeaways and Toolkit" section, a brief summary of prior research, and suggestions for further reading. Operations forensics offers powerful tools and frameworks for financial analysts, private equity firms, managers, and consultants. This book provides a valuable resource for MBA students and practitioners. Downloadable supplementary material for instructors incudes figures form the text and 42 slides that can be used for class presentations.
Don't risk the dire consequences of your work processes becoming obsolete-discover a powerful model for constant, ongoing, enterprise-wide process evolution and optimization. If you have a great product, but don't have the operations in place to efficiently and effectively support it-production, manufacturing, sales, finance, human resources, etc.-you won't succeed. Product innovation is seen as flashier and so gets far more attention, but you can create an enduring competitive advantage by revolutionizing business operations. The problem is most attempts to improve business operations are reactive, sporadic, and siloed. Tony Saldanha and Filippo Passerini's Dynamic Process Transformation model provides a living model for constant, ongoing process evolution and optimization. The authors focus on maximizing three drivers of change. First, open market rules-each business process must be run as a separate business, instead of via monolithic mandates coming down from on high. Second, there must be unified accountability- outcomes must be clear and consistent across the company, instead of being siloed within departments. And third, there needs to be a dynamic operating engine, a methodology to convert the constantly changing business process goals into tactical day-to-day employee actions. With numerous examples from leading companies, this book shows how to proactively keep business processes across the company from becoming obsolete and take advantage of a neglected key to success.
The GHG Protocol Corporate Accounting and Reporting Standard helps companies and other organizations to identify, calculate, and report GHG emissions. It is designed to set the standard for accurate, complete, consistent, relevant and transparent accounting and reporting of GHG emissions.
This guide is an introduction to English language sources, in electronic and conventional print forms, dealing with Central and Eastern European business issues. It gives evaluative descriptions and costs of all listed sources, and concentrates on recent sources. Sources in respect of some of these countries can be difficult to locate, and the author provides guidance on how to go about finding them.