Asset data integrity is a critical aspect of every business, yet it is often overlooked. This book will not only provide an appreciation of this fact, it will also provide a road map to achieving value out of something most CEOs, managers, and workers often overlook. The authors bring many years of experience and hands-on expertise that cannot be obtained elsewhere. An assessment tool is provided so once the problem is recognized by the reader, areas of improvement can be easily recognized. A detailed appendix provides further clarity.

Reliability Centered Maintenance – Reengineered: Practical Optimization of the RCM Process with RCM-R® provides an optimized approach to a well-established and highly successful method used for determining failure management policies for physical assets. It makes the original method that was developed to enhance flight safety far more useful in a broad range of industries where asset criticality ranges from high to low. RCM-R® is focused on the science of failures and what must be done to enable long-term sustainably reliable operations. If used correctly, RCM-R® is the first step in delivering fewer breakdowns, more productive capacity, lower costs, safer operations and improved environmental performance. Maintenance has a huge impact on most businesses whether its presence is felt or not. RCM-R® ensures that the right work is done to guarantee there are as few nasty surprises as possible that can harm the business in any way. RCM-R® was developed to leverage on RCM’s original success at delivering that effectiveness while addressing the concerns of the industrial market. RCM-R® addresses the RCM method and shortfalls in its application -- It modifies the method to consider asset and even failure mode criticality so that rigor is applied only where it is truly needed. It removes (within reason) the sources of concern about RCM being overly rigorous and too labor intensive without compromising on its ability to deliver a tailored failure management program for physical assets sensitive to their operational context and application. RCM-R® also provides its practitioners with standard based guidance for determining meaningful failure modes and causes facilitating their analysis for optimum outcome. Includes extensive review of the well proven RCM method and what is needed to make it successful in the industrial environment Links important elements of the RCM method with relevant International Standards for risk management and failure management Enhances RCM with increased emphasis on statistical analysis, bringing it squarely into the realm of Evidence Based Asset Management Includes extensive, experience based advice on implementing and sustaining RCM based failure management programs

The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches.

This unique and practical book describes 42 real-life events and/or situations in the careers of the three authors from which they gained insights into the applicable best practices in maintenance and reliability. The authors explain the underlying philosophies where relevant, drawing on the teachings of the leading thinkers in leadership and management. Designed to share knowledge and experience with the readers, in a readily accessible fashion, this resource does not tell the readers what to do or how to do it; it merely explains the event or situation the authors faced, and how they dealt w.

In order to protect company's information assets such as sensitive customer records, health care records, etc., the security practitioner first needs to find out: what needs protected, what risks those assets are exposed to, what controls are in place to offset those risks, and where to focus attention for risk treatment. This is the true value and purpose of information security risk assessments. Effective risk assessments are meant to provide a defendable analysis of residual risk associated with your key assets so that risk treatment options can be explored. Information Security Risk Assessment Toolkit gives you the tools and skills to get a quick, reliable, and thorough risk assessment for key stakeholders. Based on authors' experiences of real-world assessments, reports, and presentations Focuses on implementing a process, rather than theory, that allows you to derive a quick and valuable assessment Includes a companion web site with spreadsheets you can utilize to create and maintain the risk assessment

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her "Ten Steps approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach—in which she has trained Fortune 500 clients and hundreds of workshop attendees—applies to all types of data and to all types of organizations.* Includes numerous templates, detailed examples, and practical advice for executing every step of the "Ten Steps approach.* Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices.* A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online.

Information Security and Optimization maintains a practical perspective while offering theoretical explanations. The book explores concepts that are essential for academics as well as organizations. It discusses aspects of techniques and tools—definitions, usage, and analysis—that are invaluable for scholars ranging from those just beginning in the field to established experts. What are the policy standards? What are vulnerabilities and how can one patch them? How can data be transmitted securely? How can data in the cloud or cryptocurrency in the blockchain be secured? How can algorithms be optimized? These are some of the possible queries that are answered here effectively using examples from real life and case studies. Features: A wide range of case studies and examples derived from real-life scenarios that map theoretical explanations with real incidents. Descriptions of security tools related to digital forensics with their unique features, and the working steps for acquiring hands-on experience. Novel contributions in designing organization security policies and lightweight cryptography. Presentation of real-world use of blockchain technology and biometrics in cryptocurrency and personalized authentication systems. Discussion and analysis of security in the cloud that is important because of extensive use of cloud services to meet organizational and research demands such as data storage and computing requirements. Information Security and Optimization is equally helpful for undergraduate and postgraduate students as well as for researchers working in the domain. It can be recommended as a reference or textbook for courses related to cybersecurity.

The book provides a comprehensive overview of cyber security in Industry 5.0, data security in emerging technologies, block chain technology, cloud computing security, evolving IoT and OT threats, and considerable data integrity in healthcare. The impact of security risks on various sectors is explored including artificial intelligence in national security, quantum com-puting for security, and AI-driven cyber security techniques. It explores how cyber security is applied across different areas of human life through computational modeling. The book concludes by presenting a roadmap for securing computing environments, addressing the complex interplay between advanced technologies and emerging security challenges, and offering insights into future trends and innovations for sustainable development. This book: • Analyzes the use of AI, support vector machines, and deep learning for dataclassification, vulnerability prediction, and defense. • Provides insights into data protection for Industry 4.0/5.0, cloud computing, and IoT/OT, focusing on risk mitigation. • Explores block chain’s role in smart nations, financial risk management, and the potential of quantum computing for security. • Examines AI’s applications in national security, including India’s AI strategy and securing smart cities. • Evaluate strategies for data integrity in healthcare, secure IoT platforms, and supply chain cyber security. The text is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

Digital Asset Valuation and Cyber Risk Measurement: Principles of Cybernomics is a book about the future of risk and the future of value. It examines the indispensable role of economic modeling in the future of digitization, thus providing industry professionals with the tools they need to optimize the management of financial risks associated with this megatrend. The book addresses three problem areas: the valuation of digital assets, measurement of risk exposures of digital valuables, and economic modeling for the management of such risks. Employing a pair of novel cyber risk measurement units, bitmort and hekla, the book covers areas of value, risk, control, and return, each of which are viewed from the perspective of entity (e.g., individual, organization, business), portfolio (e.g., industry sector, nation-state), and global ramifications. Establishing adequate, holistic, and statistically robust data points on the entity, portfolio, and global levels for the development of a cybernomics databank is essential for the resilience of our shared digital future. This book also argues existing economic value theories no longer apply to the digital era due to the unique characteristics of digital assets. It introduces six laws of digital theory of value, with the aim to adapt economic value theories to the digital and machine era. - Comprehensive literature review on existing digital asset valuation models, cyber risk management methods, security control frameworks, and economics of information security - Discusses the implication of classical economic theories under the context of digitization, as well as the impact of rapid digitization on the future of value - Analyzes the fundamental attributes and measurable characteristics of digital assets as economic goods - Discusses the scope and measurement of digital economy - Highlights cutting-edge risk measurement practices regarding cybersecurity risk management - Introduces novel concepts, models, and theories, including opportunity value, Digital Valuation Model, six laws of digital theory of value, Cyber Risk Quadrant, and most importantly, cyber risk measures hekla and bitmort - Introduces cybernomics, that is, the integration of cyber risk management and economics to study the requirements of a databank in order to improve risk analytics solutions for (1) the valuation of digital assets, (2) the measurement of risk exposure of digital assets, and (3) the capital optimization for managing residual cyber risK - Provides a case study on cyber insurance

The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration