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

Consistent, accurate and timely data are essential to the functioning of a modern organization. Managing the integrity of an organization's data assets in a systematic manner is a challenging task in the face of continuous update, transformation and processing to support business operations. Classic approaches to constraint-based integrity focus on logical consistency within a database and reject any transaction that violates consistency, but leave unresolved how to fix or manage violations. More ad hoc approaches focus on the accuracy of the data and attempt to clean data assets after the fact, using queries to flag records with potential violations and using manual efforts to repair. Neither approach satisfactorily addresses the problem from an organizational point of view. In this thesis, we provide a conceptual model of constraint-based integrity management (CBIM) that flexibly combines both approaches in a systematic manner to provide improved integrity management. We perform a gap analysis that examines the criteria that are desirable for efficient management of data integrity. Our approach involves creating a Data Integrity Zone and an On Deck Zone in the database for separating the clean data from data that violates integrity constraints. We provide tool support for specifying constraints in a tabular form and generating triggers that flag violations of dependencies. We validate this by performing case studies on two systems used to manage healthcare data: PAL-IS and iMED-Learn. Our case studies show that using views to implement the zones does not cause any significant increase in the running time of a process.

Definitions, Concepts and Scope of Engineering Asset Management, the first volume in this new review series, seeks to minimise ambiguities in the subject matter. The ongoing effort to develop guidelines is shaping the future towards the creation of a body of knowledge for the management of engineered physical assets. Increasingly, industry practitioners are looking for strategies and tactics that can be applied to enhance the value-creating capacities of new and installed asset systems. The new knowledge-based economy paradigm provides imperatives to combine various disciplines, knowledge areas and skills for effective engineering asset management. This volume comprises selected papers from the 1st, 2nd, and 3rd World Congresses on Engineering Asset Management, which were convened under the auspices of ISEAM in collaboration with a number of organisations, including CIEAM Australia, Asset Management Council Australia, BINDT UK, and Chinese Academy of Sciences, Beijing University of Chemical Technology, China. Definitions, Concepts and Scope of Engineering Asset Management will be of interest to researchers in engineering, innovation and technology management, as well as to managers, planners and policy-makers in both industry and government.

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.

Engineering Asset Management discusses state-of-the-art trends and developments in the emerging field of engineering asset management as presented at the Fourth World Congress on Engineering Asset Management (WCEAM). It is an excellent reference for practitioners, researchers and students in the multidisciplinary field of asset management, covering such topics as asset condition monitoring and intelligent maintenance; asset data warehousing, data mining and fusion; asset performance and level-of-service models; design and life-cycle integrity of physical assets; deterioration and preservation models for assets; education and training in asset management; engineering standards in asset management; fault diagnosis and prognostics; financial analysis methods for physical assets; human dimensions in integrated asset management; information quality management; information systems and knowledge management; intelligent sensors and devices; maintenance strategies in asset management; optimisation decisions in asset management; risk management in asset management; strategic asset management; and sustainability in asset management.

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

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.

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.