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| data management risk examples: Data Risk Management: Essentials to implement an Enterprise Control Environment Tejasvi Addagada , 2022-07-08 About the book (in English for listing the book on online portals in 100-150 words): You must hear this often if you manage any kind of risk - risk and value go together. And that's true, of course for data! Both data and its infrastructure must be managed for their benefits and risks. The purpose of the book is to elaborate on this need to formalize data risk management. Today, regulations drive enterprises to assess data related risks. Prioritizing and managing data associated with financial or operational risk has been the corner-stone of most regulations like BCBS, CCAR, GDPR to name a few. Nevertheless, data risks can extend beyond regulations to improve existing control environments in companies. By doing so, we will maximize the potential of data capabilities to reach 100%. Through structural alignment within the board and formalizing a data-risk function, the book focuses on managing data risks. Furthermore, the book explains quantitative and qualitative approaches to data risk assessments along with popular tools and techniques. Also, Tejasvi discusses a proven approach to managing data risks called capability-based assessment. As a technique, this can also be applied to data risk planning and formulating a data risk strategy. Twenty data risks and privacy risks are provided in this book by way of examples. These are accompanied by details such as a risk statements, scenarios, causes, and categories of impact if the data risks are to manifest |
| data management risk examples: Operational Risk Management Ron S. Kenett, Yossi Raanan, 2011-06-20 Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management. Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis. Key Features: The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines. Explores integration of semantic, unstructured textual data, in Operational Risk Management. Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies. Presents a comprehensive treatment of near-misses data and incidents in Operational Risk Management. Looks at case studies in the financial and industrial sector. Discusses application of ontology engineering to model knowledge used in Operational Risk Management. Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems. |
| data management risk examples: Data Management courseware based on CDMP Fundamentals Raymond Slot, 2021-08-01 Besides the courseware publication (ISBN: 9789401807999), you are advised to obtain the DAMA DMBOK publication (ISBN: 9781634622349). Optionally, you can use the publication Data management: a gentle introduction (ISBN: 9789401805506) as inspiration for examples and quotes about the field of data management. This material is intended to prepare participants for the CDMP exam by DAMA International. The courseware can only be ordered by partners and is based on the current version of the DAMA DMBOK. The material will be updated when new versions of DMBOK are published. DAMA DMBOK is the industry reference for data management. It is published by DAMA International and is currently in its second version. The DMBOK is developed by professionals and can be seen as a collection of best practices. The domain of data management is divided into functional areas which are discussed in terms of definitions (what is it), goals (what are we trying to achieve), steps (what are typical activities), inputs/outputs, and participating roles. Developing and sustaining an effective data management function is far from an easy task. The DMBOK framework is adopted by many organizations as the foundation for their data management function: standardized language and good practices speed up the learning process. After the training, you have an overview of the field of data management, its terminology, and current best practices. |
| data management risk examples: Practical Guide to Clinical Data Management Susanne Prokscha, 2024-07-03 The management of clinical data, from its collection during a trial to its extraction for analysis, has become critical in preparing a regulatory submission and obtaining approval to market a treatment. Groundbreaking on its initial publication nearly 14 years ago, and evolving with the field in each iteration since then, this latest volume includes revisions to all chapters to reflect the recent updates to ICH E6, good clinical practices, electronic data capture, and interactive response technologies. Keeping the coverage practical, the author focuses on the most critical information that impacts clinical trial conduct, providing a full end-to-end overview for clinical data managers. Features: Provides an introduction and background information for the spectrum of clinical data management tasks. Outstanding text in the industry and has been used by the Society for Clinical Data Management in creating its certification exam. Explains the high-level flow of a clinical trial from creation of the protocol through study lock. Reflects electronic data capture and interactive response technologies. Discusses using the concept of three phases in the clinical data management of a study: study startup, study conduct, and study closeout, to write procedures and train staff. |
| data management risk examples: Data Management courseware based on CDMP Fundamentals Alliance BV And More Group BV, 1970-01-01 Besides the courseware publication (ISBN: 9789401811491), you are advised to obtain the DAMA DMBOK publication (ISBN: 9781634622349). Optionally, you can use the publication Data management: a gentle introduction (ISBN: 9789401805506) as inspiration for examples and quotes about the field of data management. This material is intended to prepare participants for the CDMP exam by DAMA International. The courseware can only be ordered by partners and is based on the current version of the DAMA DMBOK. The material will be updated when new versions of DMBOK are published. DAMA DMBOK is the industry reference for data management. It is published by DAMA International and is currently in its second version. The DMBOK is developed by professionals and can be seen as a collection of best practices. The domain of data management is divided into functional areas which are discussed in terms of definitions (what is it), goals (what are we trying to achieve), steps (what are typical activities), inputs/outputs, and participating roles. Developing and sustaining an effective data management function is far from an easy task. The DMBOK framework is adopted by many organizations as the foundation for their data management function: standardized language and good practices speed up the learning process. After the training, you have an overview of the field of data management, its terminology, and current best practices. |
| data management risk examples: Principles of Database Management Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens, 2018-07-12 This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL, MongoDB, Neo4j Cypher, and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management. |
| data management risk examples: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data management risk examples: Data Protection Implementation Guide Brendan Quinn, 2021-09-02 The complexities of implementing the General Data Protection Regulation (GDPR) continue to grow as it progresses through new and ever-changing technologies, business models, codes of conduct, and decisions of the supervisory authorities, and the courts. This eminently practical guide to implementing the GDPR – written in an original, problem-solving style by a highly experienced data protection expert with equal knowledge of both law and technology – provides a step-by-step project management approach to building a GDPR-compliant data protection system, assessing, and documenting the risks and then implementing these changes through processes at the operational level. With detailed attention to case law (Member State, ECJ, and ECHR), especially where affecting high-risk areas that have attracted scrutiny, the guidance proceeds systematically through such topics and issues as the following: required documentation, policies, and procedures; risk assessment tools and analysis frameworks; children’s data; employee and health data; international transfers post-Schrems II; data subject rights including the right of access; data retention and erasure; tracking and surveillance; and effects of technologies such as artificial intelligence, biometrics, and machine learning. With its practical examples derived from the author’s experience in building GDPR-compliant software, as well as its analysis of case law and enforcement priorities, this incomparable guide enables company data protection officers and compliance staff to advise on key issues with full awareness of the legal and reputational risks and how to mitigate them. It is also sure to be of immeasurable value to concerned regulators and policymakers at all government levels. “…it's going to be the go to resource for practitioners.” Tom Gilligan, Data Protection Consultant, September 2021 I purchased this book recently and I’m very glad I did. It’s the textbook I have been waiting for. As someone relatively new to data protection, I was finding it very difficult to find books on the practical side of data protection. This book is very clearly laid out with practical examples and case law given for each topic, which is immensely helpful. I would recommend it to any data protection practitioners. Jennifer Breslin, LLM CIPP/E, AIPP Member |
| data management risk examples: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 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 |
| data management risk examples: Wiley CPA Exam Review Study Guide 2023 , 2023 The Wiley CPA Study Guides four-volume set, fully updated for the 2022 CPA exam, reviews all four parts of the exam and provides the detailed information candidates need to master or reinforce tough topic areas. Content is organized into Bite-Sized Lessons that map perfectly to the Wiley CPA online course. The books are designed to supplement the online course but may also be used as a stand-alone study tool. |
| data management risk examples: Risk Analysis and Management: Engineering Resilience Ivo Häring, 2016-02-19 The book introduces basic risk concepts and then goes on to discuss risk management and analysis processes and steps. The main emphasis is on methods that fulfill the requirements of one or several risk management steps. The focus is on risk analysis methods including statistical-empirical analyses, probabilistic and parametrized models, engineering approaches and simulative methods, e.g. for fragment and blast propagation or hazard density computation. Risk management is essential for improving all resilience management steps: preparation, prevention, protection, response and recovery. The methods investigate types of event and scenario, as well as frequency, exposure, avoidance, hazard propagation, damage and risks of events. Further methods are presented for context assessment, risk visualization, communication, comparison and assessment as well as selecting mitigation measures. The processes and methods are demonstrated using detailed results and overviews of security research projects, in particular in the applications domains transport, aviation, airport security, explosive threats and urban security and safety. Topics include: sufficient control of emerging and novel hazards and risks, occupational safety, identification of minimum (functional) safety requirements, engineering methods for countering malevolent or terrorist events, security research challenges, interdisciplinary approaches to risk control and management, risk-based change and improvement management, and support of rational decision-making. The book addresses advanced bachelor students, master and doctoral students as well as scientists, researchers and developers in academia, industry, small and medium enterprises working in the emerging field of security and safety engineering. |
| data management risk examples: Privacy Program Management, Third Edition Russell Densmore, 2021-12 |
| data management risk examples: Data Management in Large-Scale Education Research Crystal Lewis, 2024-07-09 Research data management is becoming more complicated. Researchers are collecting more data, using more complex technologies, all the while increasing the visibility of our work with the push for data sharing and open science practices. Ad hoc data management practices may have worked for us in the past, but now others need to understand our processes as well, requiring researchers to be more thoughtful in planning their data management routines. This book is for anyone involved in a research study involving original data collection. While the book focuses on quantitative data, typically collected from human participants, many of the practices covered can apply to other types of data as well. The book contains foundational context, instructions, and practical examples to help researchers in the field of education begin to understand how to create data management workflows for large-scale, typically federally funded, research studies. The book starts by describing the research life cycle and how data management fits within this larger picture. The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase. Finally, considerations on whether the reader should implement, and how to integrate those practices into a workflow, are discussed. Key Features: Provides a holistic approach to the research life cycle, showing how project management and data management processes work in parallel and collaboratively Can be read in its entirety, or referenced as needed throughout the life cycle Includes relatable examples specific to education research Includes a discussion on how to organize and document data in preparation for data sharing requirements Contains links to example documents as well as templates to help readers implement practices |
| data management risk examples: Managing Risk in Information Systems Darril Gibson, 2014-07-17 This second edition provides a comprehensive overview of the SSCP Risk, Response, and Recovery Domain in addition to providing a thorough overview of risk management and its implications on IT infrastructures and compliance. Written by industry experts, and using a wealth of examples and exercises, this book incorporates hands-on activities to walk the reader through the fundamentals of risk management, strategies and approaches for mitigating risk, and the anatomy of how to create a plan that reduces risk. It provides a modern and comprehensive view of information security policies and frameworks; examines the technical knowledge and software skills required for policy implementation; explores the creation of an effective IT security policy framework; discusses the latest governance, regulatory mandates, business drives, legal considerations, and much more. -- |
| data management risk examples: Big Data Security Shibakali Gupta, Indradip Banerjee, Siddhartha Bhattacharyya, 2019-10-08 After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology. |
| data management risk examples: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| data management risk examples: Fundamentals of Bank Risk Management Benjamin Lee, 2020-03-10 Banking today has become unduly complex because new forms of risk such as technological, compliance and reputational risks are evolving and growing. They amplify the fundamental risks inherent in any bank – those of credit, market, operational and liquidity. While established concepts and principles of risk management flourish, new prescribed practices such as those of the Basel Committee on Banking Supervision continually unfold over the years. All in all, the discipline can appear complicated to many. Fortunately, there is universal consensus as to what constitutes sound risk management applicable to banks everywhere. Bank regulators and banks themselves are urging that staff, at all levels, should be aware of, and have a working knowledge of, risk management. This book brings together, in a comprehensive package, the essential elements of bank risk management, current practices and contemporary topics such as Basel IV and cyber-attack risk. It offers international cases and examples that are useful to remember. The book concludes with an epilogue on the future of risk management and an 11-page glossary. It will benefit anyone who seeks an overview and basic understanding of risk management in banking. Knowledge gained from this book will also help to give the reader insights into overall bank management. SAMPLE REVIEWS: “This book is very timely as it deals with critical areas of risk with clear explanations and international examples. I strongly recommend it as the basis for training banking executives at all levels and for students interested in risk management.” HASSAN JAFRANI Chief Risk Officer, Asia Pacific IFC, World Bank Group “This is an enjoyable and refreshing read on banks’ risk management. The fundamentals of banking and the definitions and concepts associated with bank risk management are presented in a structured and easy-to-follow format. MARK MCKENZIE Senior Financial Sector Specialist, The South East Asian Central Banks’ Research and Training Centre “... a useful reference tool for bankers everywhere. This is a book that I highly recommend to practitioners and students alike.” DR. MD. AKHTARUZZAMAN Peter Faber Business School, Australian Catholic University A very meaningful endeavour to explain the basics of risk management principles and practices in banking institutions. Written by a senior ex-banker, it provides insightful perspectives using language that is easy to understand. CHOO YEE KWAN Independent Non-Executive Director, HSBC Bank |
| data management risk examples: Space Data Management Agostino Cortesi, |
| data management risk examples: Secure Data Management Willem Jonker, Milan Petković, 2014-05-14 This book constitutes the refereed proceedings of the 10th VLDB Workshop on Secure Data Management held in Trento, Italy, on August 30, 2013. The 15 revised full papers and one keynote paper presented were carefully reviewed and selected from various submissions. The papers are organized in technical papers and 10 vision papers which address key challenges in secure data management and indicate interesting research questions. |
| data management risk examples: Operational Risk Management Philippa X. Girling, 2013-10-14 A best practices guide to all of the elements of an effective operational risk framework While many organizations know how important operational risks are, they still continue to struggle with the best ways to identify and manage them. Organizations of all sizes and in all industries need best practices for identifying and managing key operational risks, if they intend on exceling in today's dynamic environment. Operational Risk Management fills this need by providing both the new and experienced operational risk professional with all of the tools and best practices needed to implement a successful operational risk framework. It also provides real-life examples of successful methods and tools you can use while facing the cultural challenges that are prevalent in this field. Contains informative post-mortems on some of the most notorious operational risk events of our time Explores the future of operational risk in the current regulatory environment Written by a recognized global expert on operational risk An effective operational risk framework is essential for today's organizations. This book will put you in a better position to develop one and use it to identify, assess, control, and mitigate any potential risks of this nature. |
| data management risk examples: Management Decision-Making, Big Data and Analytics Simone Gressel, David J. Pauleen, Nazim Taskin, 2020-10-12 Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels. |
| data management risk examples: Data Protection David G. Hill, 2016-04-19 Failure to appreciate the full dimensions of data protection can lead to poor data protection management, costly resource allocation issues, and exposure to unnecessary risks. Data Protection: Governance, Risk Management, and Compliance explains how to gain a handle on the vital aspects of data protection.The author begins by building the foundatio |
| data management risk examples: Event- and Data-Centric Enterprise Risk-Adjusted Return Management Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil, 2022-01-06 Take a holistic view of enterprise risk-adjusted return management in banking. This book recommends that a bank transform its siloed operating model into an agile enterprise model. It offers an event-driven, process-based, data-centric approach to help banks plan and implement an enterprise risk-adjusted return model (ERRM), keeping the focus on business events, processes, and a loosely coupled enterprise service architecture. Most banks suffer from a lack of good quality data for risk-adjusted return management. This book provides an enterprise data management methodology that improves data quality by defining and using data ontology and taxonomy. It extends the data narrative with an explanation of the characteristics of risk data, the usage of machine learning, and provides an enterprise knowledge management methodology for risk-return optimization. The book provides numerous examples for process automation, data analytics, event management, knowledge management, and improvements to risk quantification. The book provides guidance on the underlying knowledge areas of banking, enterprise risk management, enterprise architecture, technology, event management, processes, and data science. The first part of the book explains the current state of banking architecture and its limitations. After defining a target model, it explains an approach to determine the gap and the second part of the book guides banks on how to implement the enterprise risk-adjusted return model. What You Will Learn Know what causes siloed architecture, and its impact Implement an enterprise risk-adjusted return model (ERRM) Choose enterprise architecture and technology Define a reference enterprise architecture Understand enterprise data management methodology Define and use an enterprise data ontology and taxonomy Create a multi-dimensional enterprise risk data model Understand the relevance of event-driven architecture from business generation and risk management perspectives Implement advanced analytics and knowledge management capabilities Who This Book Is For The global banking community, including: senior management of a bank, such as the Chief Risk Officer, Head of Treasury/Corporate Banking/Retail Banking, Chief Data Officer, and Chief Technology Officer. It is also relevant for banking software vendors, banking consultants, auditors, risk management consultants, banking supervisors, and government finance professionals. |
| data management risk examples: Data Governance and Data Management Rupa Mahanti, 2021-09-08 This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives. |
| data management risk examples: Computers at Risk National Research Council, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Commission on Physical Sciences, Mathematics, and Applications, System Security Study Committee, 1990-02-01 Computers at Risk presents a comprehensive agenda for developing nationwide policies and practices for computer security. Specific recommendations are provided for industry and for government agencies engaged in computer security activities. The volume also outlines problems and opportunities in computer security research, recommends ways to improve the research infrastructure, and suggests topics for investigators. The book explores the diversity of the field, the need to engineer countermeasures based on speculation of what experts think computer attackers may do next, why the technology community has failed to respond to the need for enhanced security systems, how innovators could be encouraged to bring more options to the marketplace, and balancing the importance of security against the right of privacy. |
| data management risk examples: Data Governance and Compliance Rupa Mahanti, 2021-04-27 This book sets the stage of the evolution of corporate governance, laws and regulations, other forms of governance, and the interaction between data governance and other corporate governance sub-disciplines. Given the continuously evolving and complex regulatory landscape and the growing number of laws and regulations, compliance is a widely discussed issue in the field of data. This book considers the cost of non-compliance bringing in examples from different industries of instances in which companies failed to comply with rules, regulations, and other legal obligations, and goes on to explain how data governance helps in avoiding such pitfalls. The first in a three-volume series on data governance, this book does not assume any prior or specialist knowledge in data governance and will be highly beneficial for IT, management and law students, academics, information management and business professionals, and researchers to enhance their knowledge and get guidance in managing their own data governance projects from a governance and compliance perspective. |
| data management risk examples: Disrupting Finance Theo Lynn, John G. Mooney, Pierangelo Rosati, Mark Cummins, 2018-12-06 This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry. |
| data management risk examples: Clinical Analytics and Data Management for the DNP Martha L. Sylvia, PhD, MBA, RN, Mary F. Terhaar, PhD, RN, ANEF, FAAN, 2023-01-18 Praise for the first edition: DNP students may struggle with data management, since their projects are not research but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects. Score: 98, 5 Stars -- Doody's Medical Reviews This unique text and reference—the only book to address the full spectrum of clinical data management for the DNP student—instills a fundamental understanding of how clinical data is gathered, used, and analyzed, and how to incorporate this data into a quality DNP project. The new third edition is updated to reflect changes in national health policy such as quality measurements, bundled payments for specialty care, and Advances to the Affordable Care Act (ACA) and evolving programs through the Centers for Medicare and Medicaid Services (CMS). The third edition reflects the revision of 2021 AACN Essentials and provides data sets and other examples in Excel and SPSS format, along with several new chapters. This resource takes the DNP student step-by-step through the complete process of data management, from planning through presentation, clinical applications of data management that are discipline-specific, and customization of statistical techniques to address clinical data management goals. Chapters are brimming with descriptions, resources, and exemplars that are helpful to both faculty and students. Topics spotlight requisite competencies for DNP clinicians and leaders such as phases of clinical data management, statistics and analytics, assessment of clinical and economic outcomes, value-based care, quality improvement, benchmarking, and data visualization. A progressive case study highlights multiple techniques and methods throughout the text. New to the Third Edition: New Chapter: Using EMR Data for the DNP Project New chapter solidifies link between EBP and Analytics for the DNP project New chapter highlights use of workflow mapping to transition between current and future state, while simultaneously visualizing process measures needed to ensure success of the DNP project Includes more examples to provide practical application exercises for students Key Features: Disseminates robust strategies for using available data from everyday practice to support trustworthy evaluation of outcomes Uses multiple tools to meet data management objectives [SPSS, Excel®, Tableau] Presents case studies to illustrate multiple techniques and methods throughout chapters Includes specific examples of the application and utility of these techniques using software that is familiar to graduate nursing students Offers real world examples of completed DNP projects Provides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan |
| data management risk examples: Site Reliability Engineering Niall Richard Murphy, Betsy Beyer, Chris Jones, Jennifer Petoff, 2016-03-23 The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You’ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient—lessons directly applicable to your organization. This book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices—Understand the theory and practice of an SRE’s day-to-day work: building and operating large distributed computing systems Management—Explore Google's best practices for training, communication, and meetings that your organization can use |
| data management risk examples: Cybersecurity Risk Management Cynthia Brumfield, 2021-12-09 Cybersecurity Risk Management In Cybersecurity Risk Management: Mastering the Fundamentals Using the NIST Cybersecurity Framework, veteran technology analyst Cynthia Brumfield, with contributions from cybersecurity expert Brian Haugli, delivers a straightforward and up-to-date exploration of the fundamentals of cybersecurity risk planning and management. The book offers readers easy-to-understand overviews of cybersecurity risk management principles, user, and network infrastructure planning, as well as the tools and techniques for detecting cyberattacks. The book also provides a roadmap to the development of a continuity of operations plan in the event of a cyberattack. With incisive insights into the Framework for Improving Cybersecurity of Critical Infrastructure produced by the United States National Institute of Standards and Technology (NIST), Cybersecurity Risk Management presents the gold standard in practical guidance for the implementation of risk management best practices. Filled with clear and easy-to-follow advice, this book also offers readers: A concise introduction to the principles of cybersecurity risk management and the steps necessary to manage digital risk to systems, assets, data, and capabilities A valuable exploration of modern tools that can improve an organization’s network infrastructure protection A practical discussion of the challenges involved in detecting and responding to a cyberattack and the importance of continuous security monitoring A helpful examination of the recovery from cybersecurity incidents Perfect for undergraduate and graduate students studying cybersecurity, Cybersecurity Risk Management is also an ideal resource for IT professionals working in private sector and government organizations worldwide who are considering implementing, or who may be required to implement, the NIST Framework at their organization. |
| data management risk examples: Target-setting Methods and Data Management to Support Performance-based Resource Allocation by Transportation Agencies National Cooperative Highway Research Program, 2010 TRB's National Cooperative Highway Research Program (NCHRP) Report 666: Target Setting Methods and Data Management to Support Performance-Based Resource Allocation by Transportation Agencies - Volume I: Research Report, and Volume II: Guide for Target-Setting and Data Management provides a framework and specific guidance for setting performance targets and for ensuring that appropriate data are available to support performance-based decision-making. Volume III to this report was published separately in an electronic-only format as NCHRP Web-Only Document 154. Volume III includes case studies of organizations investigated in the research used to develop NCHRP Report 666. |
| data management risk examples: OECD SME and Entrepreneurship Outlook 2019 OECD, 2019-05-20 The new OECD SME and Entrepreneurship Outlook presents the latest trends in performance of small and medium-sized enterprises (SMEs) and provides a comprehensive overview of business conditions and policy frameworks for SMEs and entrepreneurs. This year’s edition provides comparative evidence on business dynamism, productivity growth, wage gaps and export trends by firm size across OECD countries and emerging economies. |
| data management risk examples: A Primer in Financial Data Management Martijn Groot, 2017-05-10 A Primer in Financial Data Management describes concepts and methods, considering financial data management, not as a technological challenge, but as a key asset that underpins effective business management. This broad survey of data management in financial services discusses the data and process needs from the business user, client and regulatory perspectives. Its non-technical descriptions and insights can be used by readers with diverse interests across the financial services industry. The need has never been greater for skills, systems, and methodologies to manage information in financial markets. The volume of data, the diversity of sources, and the power of the tools to process it massively increased. Demands from business, customers, and regulators on transparency, safety, and above all, timely availability of high quality information for decision-making and reporting have grown in tandem, making this book a must read for those working in, or interested in, financial management. - Focuses on ways information management can fuel financial institutions' processes, including regulatory reporting, trade lifecycle management, and customer interaction - Covers recent regulatory and technological developments and their implications for optimal financial information management - Views data management from a supply chain perspective and discusses challenges and opportunities, including big data technologies and regulatory scrutiny |
| data management risk examples: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| data management risk examples: Urban and Regional Data Management Alenka Krek, Massimo Rumor, Sisi Zlatanova, Elfriede M. Fendel, 2009-06-02 Natural and human activities change the environment we are living in and consequently impact the quality of life. Analysing these dynamics leads to a better understanding of urban change and facilitates urban development. Research related to the management of urban data has a long tradition. Through the years a variety of challenging research questions has been investigated related to the collection, storage, use and visualisation of the data representing the urban phenomena in a computer-based environment. The Urban Data Management Symposium (UDMS) focuses on these issues since 1971. UDMS aims at providing a forum to discuss urban planning processes, exchange ideas, share information on available technology and demonstrate and promote successful information systems in local government. The focus is on urban, regional and rural issues. The UDMS 2009 annual addresses the following themes: 3D modelling, Spatial Data Infrastructures and databases, Risk and Disaster management, Environmental planning, analysis and e-government and Traffic and road monitoring. The book will be a useful source of information for urban data-related professionals, such as scholars, GIS engineers, geomatic professionals, photogrammetrists, land surveyors, mapping specialists, urban planners and researchers, as well as for postgraduate students and lecturers. |
| data management risk examples: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data management risk examples: Urban Water Security: Managing Risks Blanca Jimenez Cisneros, Joan B. Rose, 2009-03-24 Understanding the impacts of urbanization on the urban water cycle and managing the associated health risks demand adequate strategies and measures. Health risks associated with urban water systems and services include the microbiological and chemical contamination of urban waters and outbreak of water-borne diseases, mainly due to poor water and s |
| data management risk examples: "Federal Geospatial Data Management" and H.R. 2489, "AmericaView Geospatial Imagery Mapping Program Act" United States. Congress. House. Committee on Natural Resources. Subcommittee on Energy and Mineral Resources, 2009 |
| data management risk examples: Brink's Modern Internal Auditing Robert R. Moeller, 2016-01-05 The complete guide to internal auditing for the modern world Brink's Modern Internal Auditing: A Common Body of Knowledge, Eighth Edition covers the fundamental information that you need to make your role as internal auditor effective, efficient, and accurate. Originally written by one of the founders of internal auditing, Vic Brink and now fully updated and revised by internal controls and IT specialist, Robert Moeller, this new edition reflects the latest industry changes and legal revisions. This comprehensive resource has long been—and will continue to be—a critical reference for both new and seasoned internal auditors alike. Through the information provided in this inclusive text, you explore how to maximize your impact on your company by creating higher standards of professional conduct and greater protection against inefficiency, misconduct, illegal activity, and fraud. A key feature of this book is a detailed description of an internal audit Common Body of Knowledge (CBOK), key governance; risk and compliance topics that all internal auditors need to know and understand. There are informative discussions on how to plan and perform internal audits including the information technology (IT) security and control issues that impact all enterprises today. Modern internal auditing is presented as a standard-setting branch of business that elevates professional conduct and protects entities against fraud, misconduct, illegal activity, inefficiency, and other issues that could detract from success. Contribute to your company's productivity and responsible resource allocation through targeted auditing practices Ensure that internal control procedures are in place, are working, and are leveraged as needed to support your company's performance Access fully-updated information regarding the latest changes in the internal audit industry Rely upon a trusted reference for insight into key topics regarding the internal audit field Brink's Modern Internal Auditing: A Common Body of Knowledge, Eighth Editionpresents the comprehensive collection of information that internal auditors rely on to remain effective in their role. |
| data management risk examples: CMMI-ACQ Brian Gallagher, Mike Phillips, Karen Richter, Sandra Shrum, 2008-12-24 CMMI-ACQ® (Capability Maturity Model® Integration for Acquisition) describes best practices for the successful acquisition of products and services. Providing a practical framework for improving acquisition processes, CMMI-ACQ addresses the growing trend in business and government for organizations to purchase or outsource required products and services as an alternative to in-house development or resource allocation. Modeled after CMMI®, Second Edition, which documented CMMI for Development, this book is the definitive reference for the current release of CMMI for Acquisition (version 1.2). In addition to the entire CMMI-ACQ model, the book includes tips, hints, cross-references, and other author notes to help you understand, apply, and find more information about the content of the acquisition process areas. The authors also have added two chapters to illustrate the application of CMMI-ACQ in industry (a case study from General Motors) and government. Whether you are new to CMMI models or are already familiar with one or more of them, you will find this book an essential resource for managing your acquisition processes and improving your overall performance. The book is divided into three parts. Part One introduces CMMI-ACQ in the broad context of CMMI models, including essential concepts and useful background. It then describes and shows the relationships among all the components of the CMMI-ACQ process areas, and explains paths to the adoption and use of the model for process improvement and benchmarking. Finally, two separate chapters describe special acquisition needs in a government environment and real experiences with CMMI-ACQ from industry. Part Two first describes generic goals and generic practices, and then, in twenty-two sections, details each of the CMMI-ACQ process areas, including specific goals, specific practices, and examples. These process areas are organized alphabetically by process area acronym to facilitate quick reference. Part Three provides several useful references, including sources for further information about CMMI and CMMI-ACQ, acronym definitions, a glossary of terms, and an index. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
