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| data driven risk management: Data Analytics for Engineering and Construction Project Risk Management Ivan Damnjanovic, Kenneth Reinschmidt, 2019-05-23 This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution. |
| data driven risk management: Total Information Risk Management Alexander Borek, Ajith Kumar Parlikad, Jela Webb, Philip Woodall, 2013-08-30 How well does your organization manage the risks associated with information quality? Managing information risk is becoming a top priority on the organizational agenda. The increasing sophistication of IT capabilities along with the constantly changing dynamics of global competition are forcing businesses to make use of their information more effectively. Information is becoming a core resource and asset for all organizations; however, it also brings many potential risks to an organization, from strategic, operational, financial, compliance, and environmental to societal. If you continue to struggle to understand and measure how information and its quality affects your business, this book is for you. This reference is in direct response to the new challenges that all managers have to face. Our process helps your organization to understand the pain points regarding poor data and information quality so you can concentrate on problems that have a high impact on core business objectives. This book provides you with all the fundamental concepts, guidelines and tools to ensure core business information is identified, protected and used effectively, and written in a language that is clear and easy to understand for non-technical managers. - Shows how to manage information risk using a holistic approach by examining information from all sources - Offers varied perspectives of an author team that brings together academics, practitioners and researchers (both technical and managerial) to provide a comprehensive guide - Provides real-life case studies with practical insight into the management of information risk and offers a basis for broader discussion among managers and practitioners |
| data driven risk management: Data-Driven Security Jay Jacobs, Bob Rudis, 2014-02-24 Uncover hidden patterns of data and respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions. Everything in this book will have practical application for information security professionals. Helps IT and security professionals understand and use data, so they can thwart attacks and understand and visualize vulnerabilities in their networks Includes more than a dozen real-world examples and hands-on exercises that demonstrate how to analyze security data and intelligence and translate that information into visualizations that make plain how to prevent attacks Covers topics such as how to acquire and prepare security data, use simple statistical methods to detect malware, predict rogue behavior, correlate security events, and more Written by a team of well-known experts in the field of security and data analysis Lock down your networks, prevent hacks, and thwart malware by improving visibility into the environment, all through the power of data and Security Using Data Analysis, Visualization, and Dashboards. |
| data driven risk management: Water Security: Big Data-Driven Risk Identification, Assessment and Control of Emerging Contaminants Bin Liang, Shu-Hong Gao, Hongcheng Wang, 2024-06-12 Water Security: Big Data-Driven Risk Identification, Assessment and Control of Emerging Contaminants contains the latest information on big data-driven risk detection and analysis, risk assessment and environmental health effect, intelligent risk control technologies, and global control strategy of emerging contaminants. First, this book highlights advances and challenges throughout the detection of emerging chemical contaminants (e.g., antimicrobials, microplastics) by sensors or mass spectrometry, as well as emerging biological contaminant (e.g., ARGs, pathogens) by a combination of next- and third-generation sequencing technologies in aquatic environment. Second, it discusses in depth the ecological risk assessment and environmental health effects of emerging contaminants. Lastly, it presents the most up-to-date intelligent risk management technologies. This book shares instrumental global strategy and policy analysis on how to control emerging contaminants. Offering interdisciplinary and global perspectives from experts in environmental sciences and engineering, environmental microbiology and microbiome, environmental informatics and bioinformatics, intelligent systems, and knowledge engineering, this book provides an accessible and flexible resource for researchers and upper level students working in these fields. - Covers the detection, high-throughput analyses, and environmental behavior of the typical emerging chemical and biological contaminants - Focuses on chemical and biological big data driven aquatic ecological risk assessment models and techniques - Highlights the intelligent management and control technologies and policies for emerging contaminants in water environments |
| data driven risk management: 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 driven risk management: Data-driven Operational Risk Management Robert Scott Levine, 2008 This executive report demonstrates how to avoid severe losses through improved operational risk data collection. |
| data driven risk management: Security Risk Management for the Internet of Things John Soldatos, 2020-06-15 In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot. |
| data driven risk management: Creating a Data-Driven Organization Carl Anderson, 2015-07-23 What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries ... Anderson explains the analytics value chain you need to adopt when building predictive business models--Publisher's description. |
| data driven risk management: Revisiting Supply Chain Risk George A. Zsidisin, Michael Henke, 2018-12-18 This book offers a bridge between our current understanding of supply chain risk in practice and theory, and the monumental shifts caused by the emergence of the fourth industrial revolution. Supply chain risk and its management have experienced significant attention in scholarship and practice over the past twenty years. Our understanding of supply chain risk and its many facets, such as uncertainty and vulnerability, has expanded beyond utilizing approaches such as deploying inventory to buffer the initial effects of disruptions. Even with our increased knowledge of supply chain risk, being in the era of lean supply chain practices, digitally managed global supply chains, and closely interconnected networks, firms are exposed as ever to supply chain uncertainties that can damage, or even destroy, their ability to compete in the marketplace. The book acknowledges the criticality of big data analytics in Supply Chain Risk Management (SCRM) processes and provides appropriate tools and approaches for creating robust SCRM processes. Revisiting Supply Chain Risk presents a state-of-the-art look at SCRM through current research and philosophical thought. It is divided into six sections that highlight established themes, as well as provide new insights to developing areas of inquiry and contexts on the topic. Section 1 examines the first step in managing supply chain risk, risk assessment. The chapters in Section 2 encompass resiliency in supply chains, while Section 3 looks at relational and behavioral perspectives from varying units of analysis including consortiums, teams and decision makers. Section 4 focuses on examining supply chain risk in the contexts of sustainability and innovation. Section 5 provides insight on emerging typologies and taxonomies for classifying supply chain risk. The book concludes with Section 6, featuring illustrative case studies as real-world examples in assessing and managing supply chain risk. |
| data driven risk management: The Data-Driven Project Manager Mario Vanhoucke, 2018-03-27 Discover solutions to common obstacles faced by project managers. Written as a business novel, the book is highly interactive, allowing readers to participate and consider options at each stage of a project. The book is based on years of experience, both through the author's research projects as well as his teaching lectures at business schools. The book tells the story of Emily Reed and her colleagues who are in charge of the management of a new tennis stadium project. The CEO of the company, Jacob Mitchell, is planning to install a new data-driven project management methodology as a decision support tool for all upcoming projects. He challenges Emily and her team to start a journey in exploring project data to fight against unexpected project obstacles. Data-driven project management is known in the academic literature as “dynamic scheduling” or “integrated project management and control.” It is a project management methodology to plan, monitor, and control projects in progress in order to deliver them on time and within budget to the client. Its main focus is on the integration of three crucial aspects, as follows: Baseline Scheduling: Plan the project activities to create a project timetable with time and budget restrictions. Determine start and finish times of each project activity within the activity network and resource constraints. Know the expected timing of the work to be done as well as an expected impact on the project’s time and budget objectives. Schedule Risk Analysis: Analyze the risk of the baseline schedule and its impact on the project’s time and budget. Use Monte Carlo simulations to assess the risk of the baseline schedule and to forecast the impact of time and budget deviations on the project objectives. Project Control: Measure and analyze the project’s performance data and take actions to bring the project on track. Monitor deviations from the expected project progress and control performance in order to facilitate the decision-making process in case corrective actions are needed to bring projects back on track. Both traditional Earned Value Management (EVM) and the novel Earned Schedule (ES) methods are used. What You'll Learn Implement a data-driven project management methodology (also known as dynamic scheduling) which allows project managers to plan, monitor, and control projects while delivering them on time and within budget Study different project management tools and techniques, such as PERT/CPM, schedule risk analysis (SRA), resource buffering, and earned value management (EVM) Understand the three aspects of dynamic scheduling: baseline scheduling, schedule risk analysis, and project control Who This Book Is For Project managers looking to learn data-driven project management (or dynamic scheduling) via a novel, demonstrating real-time simulations of how project managers can solve common project obstacles |
| data driven risk management: Risk Management for Project Driven Organizations Andy Jordan, 2013-05-13 Organizations invest a lot of time, money, and energy into developing and utilizing risk management practices as part of their project management disciplines. Yet, when you move beyond the project to the program, portfolio, PMO and even organizational level, that same level of risk command and control rarely exists. With this in mind, well-known subject matter expert and author Andy Jordan starts where most leave off. He explores risk management in detail at the portfolio, program, and PMO levels. Using an engaging and easy-to-read writing style, Mr. Jordan takes readers from concepts to a process model, and then to the application of that customizable model in the user’s unique environment, helping dramatically improve their risk command and control at the organizational level. He also provides a detailed discussion of some of the challenges involved in this process. Risk Management for Project Driven Organizations is designed to aid strategic C-level decision makers and those involved in the project, program, portfolio, and PMO levels of an organization. J. Ross Publishing offers an add-on for a nominal fee -- Downloadable tools and templates for easy customization and implementation. |
| data driven risk management: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data driven risk management: Model-Driven Risk Analysis Mass Soldal Lund, Bjørnar Solhaug, Ketil Stølen, 2010-10-20 The term “risk” is known from many fields, and we are used to references to contractual risk, economic risk, operational risk, legal risk, security risk, and so forth. We conduct risk analysis, using either offensive or defensive approaches to identify and assess risk. Offensive approaches are concerned with balancing potential gain against risk of investment loss, while defensive approaches are concerned with protecting assets that already exist. In this book, Lund, Solhaug and Stølen focus on defensive risk analysis, and more explicitly on a particular approach called CORAS. CORAS is a model-driven method for defensive risk analysis featuring a tool-supported modelling language specially designed to model risks. Their book serves as an introduction to risk analysis in general, including the central concepts and notions in risk analysis and their relations. The authors’ aim is to support risk analysts in conducting structured and stepwise risk analysis. To this end, the book is divided into three main parts. Part I of the book introduces and demonstrates the central concepts and notation used in CORAS, and is largely example-driven. Part II gives a thorough description of the CORAS method and modelling language. After having completed this part of the book, the reader should know enough to use the method in practice. Finally, Part III addresses issues that require special attention and treatment, but still are often encountered in real-life risk analysis and for which CORAS offers helpful advice and assistance. This part also includes a short presentation of the CORAS tool support. The main target groups of the book are IT practitioners and students at graduate or undergraduate level. They will appreciate a concise introduction into the emerging field of risk analysis, supported by a sound methodology, and completed with numerous examples and detailed guidelines. |
| data driven risk management: The Handbook of Maritime Economics and Business Costas Grammenos, 2013-07-04 This book is the founding title in the Grammenos Library. The diversity of the subjects covered is unique and the results of research developed over many years are not only comprehensive, but also have important implications on real life issues in maritime business. The new edition covers a vast number of topics, including: • Shipping Economics and Maritime Nexus • International Seaborne Trade • Economics of Shipping Market and Shipping Cycles • Economics of Shipping Sectors • Issues in Liner Shipping • Economics of Maritime Safety and Seafaring Labour Market • National and International Shipping Policies • Aspects of Shipping Management and Operations• Shipping Investment and Finance • Port Economics and Management • Aspects of International Logistics |
| data driven risk management: Risk Analytics: From Concept To Deployment Edward Hon Khay Ng, 2021-10-04 This book is written to empower risk professionals to turn analytics and models into deployable solutions with minimal IT intervention. Corporations, especially financial institutions, must show evidence of having quantified credit, market and operational risks. They have databases but automating the process to translate data into risk parameters remains a desire.Modelling is done using software with output codes not readily processed by databases. With increasing acceptance of open-source languages, database vendors have seen the value of integrating modelling capabilities into their products. Nevertheless, deploying solutions to automate processes remains a challenge. While not comprehensive in dealing with all facets of risks, the author aims to develop risk professionals who will be able to do just that. |
| data driven risk management: Credit Risk Management Tony Van Gestel, Bart Baesens, 2009 This first of three volumes on credit risk management, providing a thorough introduction to financial risk management and modelling. |
| data driven risk management: Data-Driven Organization Design Rupert Morrison, 2021-10-03 SHORTLISTED: CMI Management Book of the Year 2017 - Management Futures Category Understand how to drive business performance with your organizational data and analytics in the second edition of Data-Driven Organization Design. Using data and analytics is a key opportunity for businesses to transform performance and achieve success. With a data-driven approach, all the elements of the organizational system can be connected to design an environment in which people can excel and attain competitive advantage. Data-Driven Organization Design provides a practical framework for HR and organization design practitioners to build a baseline of data, set objectives, carry out fixed and dynamic process design, map competencies, and right-size the organization. It shows how to collect the right data, present it meaningfully and ask the most relevant questions of it to help complex, fluid organizations constantly evolve and meet moving objectives. This updated second edition contains new material on organizational planning and analysis, role design and job architecture, position management lifecycle and delta reporting. Alongside this, new case studies and examples will show how these approaches have been applied in practice. Whether planning a long-term transformation, a large redesign or an individual small project, Data-Driven Organization Design will demonstrate how to make the most of your organizational data and analytics to drive business performance. |
| data driven risk management: Winning With Risk Management Russell Walker, 2013-04-04 This book develops the notion that companies can succeed on the basis of risk management, much as companies compete on efficiency, costs, labor, location, and other dimensions. The reality of risk and how it impacts companies is that it is much more definite, often catastrophic and looks more like a shock. This is striking, as a difference between firms on risk different than a marginal difference in operating efficiencies, for example. Competing on Risk Management requires a discipline, a commitment to using information and recognizing shocks and then acting upon those to redistribute assets. This book will examine how leading firms that compete on risk have done this and showcase best practices and impacts to the capital structure of firms and their organizational formation. |
| data driven risk management: Simplifying Risk Management Patrick Roberts, 2022-04-25 Recent decades have seen much greater attention paid to risk management at an organizational level, as evidenced by the proliferation of legislation, regulation, international standards and good practice guidance. The recent experience of Covid-19 has only served to heighten this attention. Growing interest in the discipline has been accompanied by significant growth in the risk management profession; but practitioners are not well served with suitable books to guide them in their work or challenge them in their professional development. This book attempts to place the practice of risk management within organizations into a broader context, looking as much at why we try to manage risk as how we try to manage risk. In doing so, it challenges two significant trends in the practice of risk management: • The treatment of risk management primarily as a compliance issue within an overall corporate governance narrative; and • The very widespread use of qualitative risk assessment tools (“heat maps” etc.) which have absolutely no proven effectiveness. Taken together, these trends have resulted in much attention being devoted to developing formalized systems for identifying and analyzing risks; but there is little evidence that this is driving practical, cost-effective efforts to actually manage risk. There appears to be a preoccupation with the risks themselves, rather than a focus on the positive actions that can (and should) be taken to benefit stakeholders. This book outlines a simple, quantitative approach to risk management which refocuses attention on treating risks; and presents choices about risk treatment as normal business decisions. |
| data driven risk management: 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 driven risk management: 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 driven risk management: Enterprise Security Risk Management Brian Allen, Esq., CISSP, CISM, CPP, CFE, Rachelle Loyear CISM, MBCP, 2017-11-29 As a security professional, have you found that you and others in your company do not always define “security” the same way? Perhaps security interests and business interests have become misaligned. Brian Allen and Rachelle Loyear offer a new approach: Enterprise Security Risk Management (ESRM). By viewing security through a risk management lens, ESRM can help make you and your security program successful. In their long-awaited book, based on years of practical experience and research, Brian Allen and Rachelle Loyear show you step-by-step how Enterprise Security Risk Management (ESRM) applies fundamental risk principles to manage all security risks. Whether the risks are informational, cyber, physical security, asset management, or business continuity, all are included in the holistic, all-encompassing ESRM approach which will move you from task-based to risk-based security. How is ESRM familiar? As a security professional, you may already practice some of the components of ESRM. Many of the concepts – such as risk identification, risk transfer and acceptance, crisis management, and incident response – will be well known to you. How is ESRM new? While many of the principles are familiar, the authors have identified few organizations that apply them in the comprehensive, holistic way that ESRM represents – and even fewer that communicate these principles effectively to key decision-makers. How is ESRM practical? ESRM offers you a straightforward, realistic, actionable approach to deal effectively with all the distinct types of security risks facing you as a security practitioner. ESRM is performed in a life cycle of risk management including: Asset assessment and prioritization. Risk assessment and prioritization. Risk treatment (mitigation). Continuous improvement. Throughout Enterprise Security Risk Management: Concepts and Applications, the authors give you the tools and materials that will help you advance you in the security field, no matter if you are a student, a newcomer, or a seasoned professional. Included are realistic case studies, questions to help you assess your own security program, thought-provoking discussion questions, useful figures and tables, and references for your further reading. By redefining how everyone thinks about the role of security in the enterprise, your security organization can focus on working in partnership with business leaders and other key stakeholders to identify and mitigate security risks. As you begin to use ESRM, following the instructions in this book, you will experience greater personal and professional satisfaction as a security professional – and you’ll become a recognized and trusted partner in the business-critical effort of protecting your enterprise and all its assets. |
| data driven risk management: Data-Driven HR Bernard Marr, 2018-04-03 FINALIST: Business Book Awards 2019 - HR and Management Category Traditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-Driven HR is a practical guide which enables HR professionals to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, collect information in a transparent way that is in line with data protection requirements and turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-Driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR professionals looking to make a measurable difference in their organizations. |
| data driven risk management: Risk Analytics Eduardo Rodríguez Taborda, 2023 Organizations and institutions are looking for implementing data-driven decision-making processes, business process improvement and methods for advancing faster in innovation. This book is about implementing the analytics process to develop a risk management practice that creates competitive advantages under uncertainty-- |
| data driven risk management: The Failure of Risk Management Douglas W. Hubbard, 2009-04-27 An essential guide to the calibrated risk analysis approach The Failure of Risk Management takes a close look at misused and misapplied basic analysis methods and shows how some of the most popular risk management methods are no better than astrology! Using examples from the 2008 credit crisis, natural disasters, outsourcing to China, engineering disasters, and more, Hubbard reveals critical flaws in risk management methods–and shows how all of these problems can be fixed. The solutions involve combinations of scientifically proven and frequently used methods from nuclear power, exploratory oil, and other areas of business and government. Finally, Hubbard explains how new forms of collaboration across all industries and government can improve risk management in every field. Douglas W. Hubbard (Glen Ellyn, IL) is the inventor of Applied Information Economics (AIE) and the author of Wiley's How to Measure Anything: Finding the Value of Intangibles in Business (978-0-470-11012-6), the #1 bestseller in business math on Amazon. He has applied innovative risk assessment and risk management methods in government and corporations since 1994. Doug Hubbard, a recognized expert among experts in the field of risk management, covers the entire spectrum of risk management in this invaluable guide. There are specific value-added take aways in each chapter that are sure to enrich all readers including IT, business management, students, and academics alike —Peter Julian, former chief-information officer of the New York Metro Transit Authority. President of Alliance Group consulting In his trademark style, Doug asks the tough questions on risk management. A must-read not only for analysts, but also for the executive who is making critical business decisions. —Jim Franklin, VP Enterprise Performance Management and General Manager, Crystal Ball Global Business Unit, Oracle Corporation. |
| data driven risk management: Big Data Driven Supply Chain Management Nada R. Sanders, 2014-05-07 Master a complete, five-step roadmap for leveraging Big Data and analytics to gain unprecedented competitive advantage from your supply chain. Using Big Data, pioneers such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery over their supply chains. They are achieving greater visibility into inventory levels, order fulfillment rates, material and product delivery… using predictive data analytics to match supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains. You'll learn best practices for segmenting and analyzing customers, defining competitive priorities for each segment, aligning functions behind strategy, dissolving organizational boundaries to sense demand and make better decisions, and choose the right metrics to support all of this. Using these techniques, you can overcome the widespread obstacles to making the most of Big Data in your supply chain — and earn big profits from the data you're already generating. For all executives, managers, and analysts interested in using Big Data technologies to improve supply chain performance. |
| data driven risk management: 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 driven risk management: Building a Digital Analytics Organization Judah Phillips, 2013-07-25 Drive maximum business value from digital analytics, web analytics, site analytics, and business intelligence! In Building a Digital Analytics Organization, pioneering expert Judah Phillips thoroughly explains digital analytics to business practitioners, and presents best practices for using it to reduce costs and increase profitable revenue throughout the business. Phillips covers everything from making the business case through defining and executing strategy, and shows how to successfully integrate analytical processes, technology, and people in all aspects of operations. This unbiased and product-independent guide is replete with examples, many based on the author’s own extensive experience. Coverage includes: key concepts; focusing initiatives and strategy on business value, not technology; building an effective analytics organization; choosing the right tools (and understanding their limitations); creating processes and managing data; analyzing paid, owned, and earned digital media; performing competitive and qualitative analyses; optimizing and testing sites; implementing integrated multichannel digital analytics; targeting consumers; automating marketing processes; and preparing for the revolutionary “analytical economy.” For all business practitioners interested in analytics and business intelligence in all areas of the organization. |
| data driven risk management: Proceedings of the 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023) Zhikai Wang, Qiujing Wu, Songsong Liu, Guoliang Wang, Jia Li, 2024-02-10 This is an open access book. 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023) will be held from Oct. 27 to 29 in Xiamen, China. It dedicates to create a platform for academic communications between specialists and scholars in the fields of Public Service, Economic Management and Sustainable Development. PESD 2023 is the Public Service, Economic Management and Sustainable Development conference aimed at presenting current research being carried out. Economic development provides the basic material basis for public services, and public services create a good social foundation for economic development. At the same time, social and economic aspects need to jointly promote sustainable development. The idea of the conference is for the scientists, scholars, engineers, and students from Universities all around the world and the industry to present ongoing research activities, and hence to foster research relations between the Universities and the industry. This conference provides opportunities for the delegates to exchange new ideas and application experiences face to face, establish business or research relations, and find global partners for future collaboration. |
| data driven risk management: The Future of Risk Management Howard Kunreuther, Robert J. Meyer, Erwann O. Michel-Kerjan, 2019-07-26 Whether man-made or naturally occurring, large-scale disasters can cause fatalities and injuries, devastate property and communities, savage the environment, impose significant financial burdens on individuals and firms, and test political leadership. Moreover, global challenges such as climate change and terrorism reveal the interdependent and interconnected nature of our current moment: what occurs in one nation or geographical region is likely to have effects across the globe. Our information age creates new and more integrated forms of communication that incur risks that are difficult to evaluate, let alone anticipate. All of this makes clear that innovative approaches to assessing and managing risk are urgently required. When catastrophic risk management was in its inception thirty years ago, scientists and engineers would provide estimates of the probability of specific types of accidents and their potential consequences. Economists would then propose risk management policies based on those experts' estimates with little thought as to how this data would be used by interested parties. Today, however, the disciplines of finance, geography, history, insurance, marketing, political science, sociology, and the decision sciences combine scientific knowledge on risk assessment with a better appreciation for the importance of improving individual and collective decision-making processes. The essays in this volume highlight past research, recent discoveries, and open questions written by leading thinkers in risk management and behavioral sciences. The Future of Risk Management provides scholars, businesses, civil servants, and the concerned public tools for making more informed decisions and developing long-term strategies for reducing future losses from potentially catastrophic events. Contributors: Mona Ahmadiani, Joshua D. Baker, W. J. Wouter Botzen, Cary Coglianese, Gregory Colson, Jeffrey Czajkowski, Nate Dieckmann, Robin Dillon, Baruch Fischhoff, Jeffrey A. Friedman, Robin Gregory, Robert W. Klein, Carolyn Kousky, Howard Kunreuther, Craig E. Landry, Barbara Mellers, Robert J. Meyer, Erwann Michel-Kerjan, Robert Muir-Wood, Mark Pauly, Lisa Robinson, Adam Rose, Paul J. H. Schoemaker, Paul Slovic, Phil Tetlock, Daniel Västfjäll, W. Kip Viscusi, Elke U. Weber, Richard Zeckhauser. |
| data driven risk management: Risk Management Exam Review , Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| data driven risk management: Data-driven Organization Design Rupert Morrison, 2015-10-03 SHORTLISTED: CMI Management Book of the Year 2017 - Management Futures Category Data is changing the nature of competition. Making sense of it is tough; taking advantage of it is even tougher. There is a clear business opportunity for organizations to use data and analytics to transform business performance. Data-driven Organization Design provides a practical framework for HR and organization design practitioners to build a baseline of data, set objectives, carry out fixed and dynamic process design, map competencies, and right-size the organization so everyone performs to their potential and organizations have a hope of getting and sustaining a competitive edge. Data-driven Organization Design shows how to collect the right data on organizations, present it meaningfully and ask the right questions of it to help complex, fluid organizations constantly evolve and meet moving objectives. Through the use of case studies, practical tips, and sample exercises, it explains in detail how to use data and analytics to connect all the elements of the system so you can design an environment for people to perform, an organization which has the right people, in the right place, doing the right things, at the right time. Whether you are looking to implement a long-term transformation, large redesign, or a one-off small scale project, Data-driven Organization Design will guide you through making the most of organizational data and analytics to drive business performance. |
| data driven risk management: COBIT 5 for Risk ISACA, 2013-09-25 Information is a key resource for all enterprises. From the time information is created to the moment it is destroyed, technology plays a significant role in containing, distributing and analysing information. Technology is increasingly advanced and has become pervasive in enterprises and the social, public and business environments. |
| data driven risk management: Resilient Urban Futures Zoé A. Hamstead, David M. Iwaniec, Timon McPhearson, Marta Berbés-Blázquez, Elizabeth M. Cook, Tischa A. Muñoz-Erickson, 2021-04-06 This open access book addresses the way in which urban and urbanizing regions profoundly impact and are impacted by climate change. The editors and authors show why cities must wage simultaneous battles to curb global climate change trends while adapting and transforming to address local climate impacts. This book addresses how cities develop anticipatory and long-range planning capacities for more resilient futures, earnest collaboration across disciplines, and radical reconfigurations of the power regimes that have institutionalized the disenfranchisement of minority groups. Although planning processes consider visions for the future, the editors highlight a more ambitious long-term positive visioning approach that accounts for unpredictability, system dynamics and equity in decision-making. This volume brings the science of urban transformation together with practices of professionals who govern and manage our social, ecological and technological systems to design processes by which cities may achieve resilient urban futures in the face of climate change. |
| data driven risk management: Sport Analytics Gil Fried, Ceyda Mumcu, 2016-11-10 The increasing availability of data has transformed the way sports are played, promoted and managed. This is the first textbook to explain how the big data revolution is having a profound influence across the sport industry, demonstrating how sport managers and business professionals can use analytical techniques to improve their professional practice. While other sports analytics books have focused on player performance data, this book shows how analytics can be applied to every functional area of sport business, from marketing and event management to finance and legal services. Drawing on research that spans the entire sport industry, it explains how data is influencing the most important decisions, from ticket sales and human resources to risk management and facility operations. Each chapter contains real world examples, industry profiles and extended case studies which are complimented by a companion website full of useful learning resources. Sport Analytics: A data-driven approach to sport business and management is an essential text for all sport management students and an invaluable reference for any sport management professional involved in operational research. |
| data driven risk management: Cyber Strategy Carol A. Siegel, Mark Sweeney, 2020-03-23 Cyber Strategy: Risk-Driven Security and Resiliency provides a process and roadmap for any company to develop its unified Cybersecurity and Cyber Resiliency strategies. It demonstrates a methodology for companies to combine their disassociated efforts into one corporate plan with buy-in from senior management that will efficiently utilize resources, target high risk threats, and evaluate risk assessment methodologies and the efficacy of resultant risk mitigations. The book discusses all the steps required from conception of the plan from preplanning (mission/vision, principles, strategic objectives, new initiatives derivation), project management directives, cyber threat and vulnerability analysis, cyber risk and controls assessment to reporting and measurement techniques for plan success and overall strategic plan performance. In addition, a methodology is presented to aid in new initiative selection for the following year by identifying all relevant inputs. Tools utilized include: Key Risk Indicators (KRI) and Key Performance Indicators (KPI) National Institute of Standards and Technology (NIST) Cyber Security Framework (CSF) Target State Maturity interval mapping per initiative Comparisons of current and target state business goals and critical success factors A quantitative NIST-based risk assessment of initiative technology components Responsible, Accountable, Consulted, Informed (RACI) diagrams for Cyber Steering Committee tasks and Governance Boards’ approval processes Swimlanes, timelines, data flow diagrams (inputs, resources, outputs), progress report templates, and Gantt charts for project management The last chapter provides downloadable checklists, tables, data flow diagrams, figures, and assessment tools to help develop your company’s cybersecurity and cyber resiliency strategic plan. |
| data driven risk management: Revolutionizing Project Management Avery Harrison, 2023-11-24 Revolutionizing Project Management: Harnessing the Power of Artificial Intelligence is a cutting-edge guide designed to help project managers, professionals, and enthusiasts navigate the rapidly evolving landscape of AI-driven project management. As the world becomes increasingly interconnected and technology continues to advance at an unprecedented pace, artificial intelligence has emerged as a game-changing force capable of transforming project management practices and empowering professionals to achieve greater success. In this comprehensive book, you'll discover the potential of AI to revolutionize project management across various domains, from intelligent project scoping and resource allocation to AI-powered risk analysis and mitigation. We start by laying a solid foundation, introducing you to the concepts of artificial intelligence, its history, and its relevance to project management. As we progress through the chapters, we'll uncover the myriad ways AI can optimize project management processes, enhance team communication, and drive better decision-making within project teams. Key topics covered in the book include: The fundamental concepts of artificial intelligence, its history, and its relevance to project management. The evolution of project management, traditional vs. modern approaches, and the role of technology in shaping the discipline. The intersection of AI and project management, exploring opportunities, challenges, and the impact of AI on project management functions. AI-driven techniques for intelligent project scoping, optimal resource allocation, and risk analysis and mitigation. The critical role AI plays in enhancing team communication, collaboration, and conflict resolution, including AI-assisted meeting scheduling and management, natural language processing, and sentiment analysis. The ethical considerations surrounding AI in project management, including data privacy, security, and the potential implications of AI-driven decision-making on human employment. Practical guidance on how to integrate AI into your project management practices, identifying the essential steps for successful AI implementation and offering real-life case studies and examples to illustrate the transformative power of AI in project management. With its in-depth exploration of AI's transformative potential and practical guidance on integrating AI into project management practices, Revolutionizing Project Management: Harnessing the Power of Artificial Intelligence is an indispensable resource for project managers and professionals seeking to stay ahead of the curve in an increasingly competitive and technologically advanced world. Whether you are a seasoned project manager or just beginning your journey in the world of project management, this book aims to equip you with the knowledge and insights necessary to harness the full potential of AI and revolutionize the way you manage projects. Don't miss the opportunity to explore how AI can elevate your project management skills and deliver exceptional results in this fast-paced, technology-driven era. Order your copy today and embark on this exciting journey into the future of project management. |
| data driven risk management: Data-Driven Innovation Big Data for Growth and Well-Being OECD, 2015-10-06 This report improves the evidence base on the role of Data Driven Innovation for promoting growth and well-being, and provide policy guidance on how to maximise the benefits of DDI and mitigate the associated economic and societal risks. |
| data driven risk management: Cyber-Risk Management Atle Refsdal, Bjørnar Solhaug, Ketil Stølen, 2015-10-01 This book provides a brief and general introduction to cybersecurity and cyber-risk assessment. Not limited to a specific approach or technique, its focus is highly pragmatic and is based on established international standards (including ISO 31000) as well as industrial best practices. It explains how cyber-risk assessment should be conducted, which techniques should be used when, what the typical challenges and problems are, and how they should be addressed. The content is divided into three parts. First, part I provides a conceptual introduction to the topic of risk management in general and to cybersecurity and cyber-risk management in particular. Next, part II presents the main stages of cyber-risk assessment from context establishment to risk treatment and acceptance, each illustrated by a running example. Finally, part III details four important challenges and how to reasonably deal with them in practice: risk measurement, risk scales, uncertainty, and low-frequency risks with high consequence. The target audience is mainly practitioners and students who are interested in the fundamentals and basic principles and techniques of security risk assessment, as well as lecturers seeking teaching material. The book provides an overview of the cyber-risk assessment process, the tasks involved, and how to complete them in practice. |
| data driven risk management: Data-Driven, Information-Enabled Regulatory Delivery OECD, 2021-09-25 Industries and businesses are becoming increasingly digital, and the COVID-19 pandemic has further accelerated this trend. This report maps out several efforts undertaken jointly by the OECD and Italian regulators to develop and use artificial intelligence and machine learning tools in regulatory inspections and enforcement. |
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 …
Data and Digital Outputs Managemen…
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Re…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward …
Open Data Policy and Principles - Belmont …
The data policy includes the following principles: Data should be: Discoverable through catalogues and …
Belmont Forum Adopts Open Data Principle…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A …
Belmont Forum Data Accessibility State…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data …
