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Data Governance
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Data Governance
This book is a comprehensive resource designed to demystify the complex world of data governance for professionals across various sectors. This guide provides in-depth insights, methodologies, and best practices to help organizations manage their data effectively and securely. It covers essential topics such as data quality, privacy, security, and management ensuring that readers gain a holistic understanding of how to establish and maintain a robust data governance framework. Through a blend of theoretical knowledge and practical applications, this book addresses the challenges and benefits of data governance, equipping readers with the tools needed to navigate the evolving data landscape. ...
Data Driven
Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.
Molecular Modeling of Proteins
Molecular Modeling of Proteins, Second Edition provides a theoretical background of various methods available and enables non-specialists to apply methods to their problems by including updated chapters and new material not covered in the first edition. This detailed volume opens by featuring classical and advanced simulation methods as well as methods to set-up complex systems such as lipid membranes and membrane proteins and continues with chapters devoted to the simulation and analysis of conformational changes of proteins, computational methods for protein structure prediction, usage of experimental data in combination with computational techniques, as well as protein-ligand interactions, which are relevant in the drug design process. Written for the highly successful Methods in Molecular Biology series, chapters include thorough introductions, step-by-step instructions and notes on troubleshooting and avoiding common pitfalls. Update-to-date and authoritative, Molecular Modeling of Proteins, Second Edition aims to aid researchers in the physical, chemical and biosciences interested in utilizing this powerful technology.
Data Quality
Data Quality: The Accuracy Dimension is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it. Likewise, improving the accuracy of data in information systems is fast becoming a major goal as companies realize how much it affects their bottom line. Data profiling is a new technology that supports and enhances the accuracy of databases throughout major IT shops. Jack Olson explains data profiling and shows how it fits into the larger picture of data quality.* Provides an accessible, enjoyable introduction to the subject of data accuracy, peppered with real-world anecdotes. * Provides a framework for data profiling with a discussion of analytical tools appropriate for assessing data accuracy. * Is written by one of the original developers of data profiling technology. * Is a must-read for any data management staff, IT management staff, and CIOs of companies with data assets.
Structural Health Monitoring
Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-s...
The Path to Becoming a Data-Driven Public Sector
Twenty-first century governments must keep pace with the expectations of their citizens and deliver on the promise of the digital age. Data-driven approaches are particularly effective for meeting those expectations and rethinking the way governments and citizens interact. This report highlights the important role data can play in creating conditions that improve public services, increase the effectiveness of public spending and inform ethical and privacy considerations. It presents a data-driven public sector framework that can help countries or organisations assess the elements needed for using data to make better-informed decisions across public sectors.
Information Security Governance
IT Security governance is becoming an increasingly important issue for all levels of a company. IT systems are continuously exposed to a wide range of threats, which can result in huge risks that threaten to compromise the confidentiality, integrity, and availability of information. This book will be of use to those studying information security, as well as those in industry.
