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| asset management data analytics: Asset Intelligence through Integration and Interoperability and Contemporary Vibration Engineering Technologies Joseph Mathew, C.W. Lim, Lin Ma, Don Sands, Michael E. Cholette, Pietro Borghesani, 2018-11-11 These proceedings include a collection of papers on a range of topics presented at the 12th World Congress on Engineering Asset Management (WCEAM) in Brisbane, 2 – 4 August 2017. Effective strategies are required for managing complex engineering assets such as built environments, infrastructure, plants, equipment, hardware systems and components. Following the release of the ISO 5500x set of standards in 2014, the 12th WCEAM addressed important issues covering all aspects of engineering asset management across various sectors including health. The topics discussed by the congress delegates are grouped into a number of tracks, including strategies for investment and divestment of assets, operations and maintenance of assets, assessment of assets’ health conditions, risk and vulnerability, technologies, and systems for management of assets, standards, education, training and certification. |
| asset management data analytics: Infonomics Douglas B. Laney, 2017-09-05 Many senior executives talk about information as one of their most important assets, but few behave as if it is. They report to the board on the health of their workforce, their financials, their customers, and their partnerships, but rarely the health of their information assets. Corporations typically exhibit greater discipline in tracking and accounting for their office furniture than their data. Infonomics is the theory, study, and discipline of asserting economic significance to information. It strives to apply both economic and asset management principles and practices to the valuation, handling, and deployment of information assets. This book specifically shows: CEOs and business leaders how to more fully wield information as a corporate asset CIOs how to improve the flow and accessibility of information CFOs how to help their organizations measure the actual and latent value in their information assets. More directly, this book is for the burgeoning force of chief data officers (CDOs) and other information and analytics leaders in their valiant struggle to help their organizations become more infosavvy. Author Douglas Laney has spent years researching and developing Infonomics and advising organizations on the infinite opportunities to monetize, manage, and measure information. This book delivers a set of new ideas, frameworks, evidence, and even approaches adapted from other disciplines on how to administer, wield, and understand the value of information. Infonomics can help organizations not only to better develop, sell, and market their offerings, but to transform their organizations altogether. Doug Laney masterfully weaves together a collection of great examples with a solid framework to guide readers on how to gain competitive advantage through what he labels the unruly asset – data. The framework is comprehensive, the advice practical and the success stories global and across industries and applications. Liz Rowe, Chief Data Officer, State of New Jersey A must read for anybody who wants to survive in a data centric world. Shaun Adams, Head of Data Science, Betterbathrooms.com Phenomenal! An absolute must read for data practitioners, business leaders and technology strategists. Doug's lucid style has a set a new standard in providing intelligible material in the field of information economics. His passion and knowledge on the subject exudes thru his literature and inspires individuals like me. Ruchi Rajasekhar, Principal Data Architect, MISO Energy I highly recommend Infonomics to all aspiring analytics leaders. Doug Laney’s work gives readers a deeper understanding of how and why information should be monetized and managed as an enterprise asset. Laney’s assertion that accounting should recognize information as a capital asset is quite convincing and one I agree with. Infonomics enjoyably echoes that sentiment! Matt Green, independent business analytics consultant, Atlanta area If you care about the digital economy, and you should, read this book. Tanya Shuckhart, Analyst Relations Lead, IRI Worldwide |
| asset management data analytics: DATA ANALYTICS Dr. Manish Kumar, |
| asset management data analytics: Data Analytics for Smart Infrastructure Bin Liang, Yang Wang, Hongda Tian, Fang Chen, Zhidong Li, Ting Guo, 2025-01-31 This book presents, for the first time, data analytics for smart infrastructures. The authors draw on over a decade's experience working with industry and demonstrating the capabilities of data analytics for infrastructure and asset management. The volume gives data-driven solutions to cover critical capabilities for infrastructure and asset management across three domains: 1) situation awareness 2) predictive analytics and 3) decision support. The reader will gain from various data analytic techniques including anomaly detection, performance evaluation, failure prediction, trend analysis, asset prioritization, smart sensing and real-time/online systems. These data analytic techniques are vital to solving problems in infrastructure and asset management. The reader will benefit from case studies drawn from critical infrastructures such as water management, structural health monitoring and rail networks. This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure. |
| asset management data analytics: Leading Digital George Westerman, Didier Bonnet, Andrew McAfee, 2014-09-23 Become a Digital Master—No Matter What Business You’re In If you think the phrase “going digital” is only relevant for industries like tech, media, and entertainment—think again. In fact, mobile, analytics, social media, sensors, and cloud computing have already fundamentally changed the entire business landscape as we know it—including your industry. The problem is that most accounts of digital in business focus on Silicon Valley stars and tech start-ups. But what about the other 90-plus percent of the economy? In Leading Digital, authors George Westerman, Didier Bonnet, and Andrew McAfee highlight how large companies in traditional industries—from finance to manufacturing to pharmaceuticals—are using digital to gain strategic advantage. They illuminate the principles and practices that lead to successful digital transformation. Based on a study of more than four hundred global firms, including Asian Paints, Burberry, Caesars Entertainment, Codelco, Lloyds Banking Group, Nike, and Pernod Ricard, the book shows what it takes to become a Digital Master. It explains successful transformation in a clear, two-part framework: where to invest in digital capabilities, and how to lead the transformation. Within these parts, you’ll learn: • How to engage better with your customers • How to digitally enhance operations • How to create a digital vision • How to govern your digital activities The book also includes an extensive step-by-step transformation playbook for leaders to follow. Leading Digital is the must-have guide to help your organization survive and thrive in the new, digitally powered, global economy. |
| asset management data analytics: Asset Management: Tools And Issues Frank J Fabozzi, Francesco A Fabozzi, Marcos Lopez De Prado, Stoyan V Stoyanov, 2020-12-02 Long gone are the times when investors could make decisions based on intuition. Modern asset management draws on a wide-range of fields beyond financial theory: economics, financial accounting, econometrics/statistics, management science, operations research (optimization and Monte Carlo simulation), and more recently, data science (Big Data, machine learning, and artificial intelligence). The challenge in writing an institutional asset management book is that when tools from these different fields are applied in an investment strategy or an analytical framework for valuing securities, it is assumed that the reader is familiar with the fundamentals of these fields. Attempting to explain strategies and analytical concepts while also providing a primer on the tools from other fields is not the most effective way of describing the asset management process. Moreover, while an increasing number of investment models have been proposed in the asset management literature, there are challenges and issues in implementing these models. This book provides a description of the tools used in asset management as well as a more in-depth explanation of specialized topics and issues covered in the companion book, Fundamentals of Institutional Asset Management. The topics covered include the asset management business and its challenges, the basics of financial accounting, securitization technology, analytical tools (financial econometrics, Monte Carlo simulation, optimization models, and machine learning), alternative risk measures for asset allocation, securities finance, implementing quantitative research, quantitative equity strategies, transaction costs, multifactor models applied to equity and bond portfolio management, and backtesting methodologies. This pedagogic approach exposes the reader to the set of interdisciplinary tools that modern asset managers require in order to extract profits from data and processes. |
| asset management data analytics: Data Intensive Industrial Asset Management Farhad Balali, Jessie Nouri, Adel Nasiri, Tian Zhao, 2020-01-22 This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system. |
| asset management data analytics: Financial Statistics and Data Analytics Shuangzhe Li, Milind Sathye, 2021-03-02 Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three. |
| asset management data analytics: Applied Insurance Analytics Patricia L. Saporito, 2015 Data is the insurance industry's single greatest asset. Yet many insurers radically underutilize their data assets, and are failing to fully leverage modern analytics. This makes them vulnerable to traditional and non-traditional competitors alike. Today, insurers largely apply analytics in important but stovepiped operational areas like underwriting, claims, marketing and risk management. By and large, they lack an enterprise analytic strategy -- or, if they have one, it is merely an architectural blueprint, inadequately business-driven or strategically aligned. Now, writing specifically for insurance industry professionals and leaders, Patricia Saporito uncovers immense new opportunities for driving competitive advantage from analytics -- and shows how to overcome the obstacles that stand in your way. Drawing on 25+ years of insurance industry experience, Saporito introduces proven best practices for developing, maturing, and profiting from your analytic capabilities. This user-friendly handbook advocates an enterprise strategy approach to analytics, presenting a common framework you can quickly adapt based on your unique business model and current capabilities. Saporito reviews common analytic applications by functional area, offering specific case studies and examples, and helping you build upon the analytics you're already doing. She presents data governance models and models proven to help you organize and deliver trusted data far more effectively. Finally, she provides tools and frameworks for improving the analytic IQ of your entire enterprise, from IT developers to business users. |
| asset management data analytics: Power and Gas Asset Management Miguel Moreira da Silva, 2020-01-01 This book offers meaningful insights into an impending challenge for the energy industry, namely the increasing role of asset management amongst the utilities’ core operations. In the aftermath of energy digitalization, power and gas companies will be able to seize asset productivity—through risk-based operation and maintenance—and better balance capital and operational expenditures. By addressing the asset management of both power and gas infrastructures, and by adopting a comprehensive approach—including regulation and business models, as well as a solid technology background—this book offers a unique perspective on the energy utilities’ transformation journey and the road to optimal decision-making for both asset portfolio expansion and replacement. The asset management end-to-end mission requires appropriate internal governance—depending on the business framework—and the development of decision aid models (for asset replacement and maintenance), supported on probabilistic risk and reliability indexes. This book advocates systematically digitalizing the power and gas assets, addressing both data governance and infrastructure, alongside real-time equipment condition monitoring. It also provides a meaningful methodology for designing data-centric asset management and predictive operation and maintenance, using artificial intelligence and engineering-based approaches. As such, it provides valuable strategy, methods and models—illustrated by case studies and proofs of concept—for a wide range of stakeholders, including utilities and industry professionals, regulators, policy-makers, researchers and students. |
| asset management data analytics: Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing Amit Kumar Tyagi, Shrikant Tiwari, Gulshan Soni, 2024-10-23 Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries. Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields. |
| asset management data analytics: Adaptive Asset Allocation Adam Butler, Michael Philbrick, Rodrigo Gordillo, 2016-02-02 Build an agile, responsive portfolio with a new approach to global asset allocation Adaptive Asset Allocation is a no-nonsense how-to guide for dynamic portfolio management. Written by the team behind Gestaltu.com, this book walks you through a uniquely objective and unbiased investment philosophy and provides clear guidelines for execution. From foundational concepts and timing to forecasting and portfolio optimization, this book shares insightful perspective on portfolio adaptation that can improve any investment strategy. Accessible explanations of both classical and contemporary research support the methodologies presented, bolstered by the authors' own capstone case study showing the direct impact of this approach on the individual investor. Financial advisors are competing in an increasingly commoditized environment, with the added burden of two substantial bear markets in the last 15 years. This book presents a framework that addresses the major challenges both advisors and investors face, emphasizing the importance of an agile, globally-diversified portfolio. Drill down to the most important concepts in wealth management Optimize portfolio performance with careful timing of savings and withdrawals Forecast returns 80% more accurately than assuming long-term averages Adopt an investment framework for stability, growth, and maximum income An optimized portfolio must be structured in a way that allows quick response to changes in asset class risks and relationships, and the flexibility to continually adapt to market changes. To execute such an ambitious strategy, it is essential to have a strong grasp of foundational wealth management concepts, a reliable system of forecasting, and a clear understanding of the merits of individual investment methods. Adaptive Asset Allocation provides critical background information alongside a streamlined framework for improving portfolio performance. |
| asset management data analytics: AI Technology in Wealth Management Mahnoosh Mirghaemi, |
| asset management data analytics: 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. |
| asset management data analytics: Big Data Analytics for Connected Vehicles and Smart Cities Bob McQueen, 2017-08-31 This practical new book presents the application of “big data” analytics to connected vehicles, smart cities, and transportation systems. This book enables transportation professionals to understand how data analytics can and will expand the design and engineering of connected vehicles and smart cities. Readers find extensive case studies and examples that provide a strong framework focusing on practical application of data sciences and analytic tools for actual projects in the field. Both federal and private sector investments have a strong interest in the connected vehicle and this book discusses the impact this has on transportation. This book defines urban analytics and modeling, incentives and governance, mobility networks, energy networks, and other attributes and elements that craft a smart city. Readers learn how smart cities impact the application of advanced technologies in urban areas. This book explains how recently passed transportation legislation for the US has a specific emphasis on the use of data for performance management. |
| asset management data analytics: Big Data Analytics Strategies for the Smart Grid Carol L. Stimmel, 2016-04-19 A comprehensive data analytics program is the only way utilities will be able to meet the challenges of modern grids with operational efficiency, while reconciling the demands of greenhouse gas legislation, and establishing a meaningful return on investment from smart grid deployments. This book addresses the requirements for applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid. |
| asset management data analytics: Data Analytics for Business Fenio Annansingh, Joseph Bon Sesay, 2022-04-20 Data analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of questions, and an instructor’s manual are also provided as online supplements. This will be a valuable text for undergraduate level courses in data analytics, data mining, business intelligence, and related areas. |
| asset management data analytics: Digital Built Asset Management Qiuchen Lu, Michael Pitt, 2024-10-03 This insightful book presents a comprehensive understanding of the new technologies impacting the digital era of built asset and facility management. Informative and accessible, it illustrates how the concepts, principles, strategies and applications of digital built asset management can be improved and implemented in real-life practice. |
| asset management data analytics: Investment Analytics In The Dawn Of Artificial Intelligence Bernard Lee, 2019-07-24 A class of highly mathematical algorithms works with three-dimensional (3D) data known as graphs. Our research challenge focuses on applying these algorithms to solve more complex problems with financial data, which tend to be in higher dimensions (easily over 100), based on probability distributions, with time subscripts and jumps. The 3D research analogy is to train a navigation algorithm when the way-finding coordinates and obstacles such as buildings change dynamically and are expressed in higher dimensions with jumps.Our short title 'ia≠ai' symbolizes how investment analytics is not a simplistic reapplication of artificial intelligence (AI) techniques proven in engineering. This book presents best-of-class sophisticated techniques available today to solve high dimensional problems with properties that go deeper than what is required to solve customary problems in engineering today.Dr Bernard Lee is the Founder and CEO of HedgeSPA, which stands for Sophisticated Predictive Analytics for Hedge Funds and Institutions. Previously, he was a managing director in the Portfolio Management Group of BlackRock in New York City as well as a finance professor who has taught and guest-lectured at a number of top universities globally.Related Link(s) |
| asset management data analytics: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| asset management data analytics: Building the Digital Enterprise Mark Skilton, 2016-04-29 The digital economy is at a tipping point. This practical book defines digital ecosystems, discusses digital design using converging technologies of social networking, mobility, big data and cloud computing, and provides a methods for linking digital technologies together to meet the challenges of building a digital enterprise in the new economy. |
| asset management data analytics: Quantitative Asset Management William Johnson, 2024-10-12 Quantitative Asset Management: Techniques for Optimizing Portfolio Returns is an authoritative guide that expertly bridges theory and practice, equipping readers with the essential tools and strategies to navigate the complex world of finance. This meticulously crafted book unveils the intricate frameworks and advanced methodologies at the core of quantitative asset management, empowering investors, analysts, and financial professionals to achieve superior portfolio performance. From the foundational principles of modern portfolio theory to the cutting-edge application of machine learning in finance, each chapter delivers a rich tapestry of insights that elevate the reader’s understanding and decision-making skills. This volume intricately explores a wide array of topics including risk management, algorithmic trading, behavioral finance, and the ethical considerations that underpin successful asset management. By weaving together practical examples and real-world applications, it ensures readers can apply the learned concepts effectively within their own financial contexts. Whether navigating the challenges of market dynamics or leveraging emerging technologies, this book stands as a vital resource, ensuring its readers are well-prepared to excel in the ever-evolving landscape of quantitative finance. |
| asset management data analytics: Python for Finance Yves J. Hilpisch, 2018-12-05 The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
| asset management data analytics: Data Analytics for Management, Banking and Finance Foued Saâdaoui, Yichuan Zhao, Hana Rabbouch, 2023-09-19 This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance. It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies. The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks |
| asset management data analytics: Advances in Risk-Informed Technologies Prabhakar V. Varde, Manoj Kumar, Mayank Agarwal, 2024-01-07 This book presents the latest research in the areas of development and application of risk-informed and risk-based technologies. The book discusses how advances in computational technologies, availability of accumulated experience and data on design, operations, maintenance and regulations, new insights in human factor modelling and development of new technologies, such as physics-of-failure modelling, prognostics and health management, have paved the way for implementation of risk and reliability tools and methods. The book will be useful for researchers, academicians, and engineers, particularly the field engineers, designers and regulators working on complex engineering systems. |
| asset management data analytics: Value Based and Intelligent Asset Management Adolfo Crespo Márquez, Marco Macchi, Ajith Kumar Parlikad, 2019-06-29 The fundamental motivation of this book is to contribute to the future advancement of Asset Management in the context of industrial plants and infrastructures. The book aims to foster a future perspective that takes advantage of value-based and intelligent asset management in order to make a step forward with respect to the evolution observed nowadays. Indeed, the current understanding of asset management is primarily supported by well-known standards. Nonetheless, asset management is still a young discipline and the knowledge developed by industry and academia is not set in stone yet. Furthermore, current trends in new organizational concepts and technologies lead to an evolutionary path in the field. Therefore, this book aims to discuss this evolutionary path, starting first of all from the consolidated theory, then moving forward to discuss: • The strategic understanding of value-based asset management in a company; • An operational definition of value, as a concept on the background of value-based asset management; • The identification of intelligent asset management, with the aim to frame a set of “tools” recommended to support the asset-related decision-making process over the asset lifecycle; • The emergence of new technologies such as cyber physical systems and digital twins, and the implications of this on asset management. |
| asset management data analytics: Quantitative Investment Analysis William Johnson, 2024-10-14 Quantitative Investment Analysis: Techniques for Active Portfolio Management offers a comprehensive exploration of the advanced methodologies used in the modern financial landscape to structure, analyze, and optimize investment portfolios. This engaging book demystifies the intersection of finance and quantitative analysis, making complex theories accessible to both novice investors and seasoned professionals. By delving into the intricacies of financial markets, readers are equipped with essential tools to evaluate diverse asset classes, develop robust trading strategies, and manage risk with precision. Through its detailed chapters, the book covers a spectrum of critical topics, from portfolio theory and equity valuation to the innovative application of machine learning and algorithmic trading. Each section provides actionable insights into optimizing returns, understanding market anomalies, and making informed decisions guided by a rigorous, quantitative framework. This text not only fosters a deep understanding of theoretical concepts but also presents real-world applications, empowering readers to navigate the challenges of contemporary financial markets with confidence and strategic acumen. Whether seeking to refine investment strategies or gain an edge in a competitive market, this book serves as an invaluable resource for mastering the art and science of quantitative investing. |
| asset management data analytics: IBM Software Defined Infrastructure for Big Data Analytics Workloads Dino Quintero, Daniel de Souza Casali, Marcelo Correia Lima, Istvan Gabor Szabo, Maciej Olejniczak, Tiago Rodrigues de Mello, Nilton Carlos dos Santos, IBM Redbooks, 2015-06-29 This IBM® Redbooks® publication documents how IBM Platform Computing, with its IBM Platform Symphony® MapReduce framework, IBM Spectrum Scale (based Upon IBM GPFSTM), IBM Platform LSF®, the Advanced Service Controller for Platform Symphony are work together as an infrastructure to manage not just Hadoop-related offerings, but many popular industry offeringsm such as Apach Spark, Storm, MongoDB, Cassandra, and so on. It describes the different ways to run Hadoop in a big data environment, and demonstrates how IBM Platform Computing solutions, such as Platform Symphony and Platform LSF with its MapReduce Accelerator, can help performance and agility to run Hadoop on distributed workload managers offered by IBM. This information is for technical professionals (consultants, technical support staff, IT architects, and IT specialists) who are responsible for delivering cost-effective cloud services and big data solutions on IBM Power SystemsTM to help uncover insights among client's data so they can optimize product development and business results. |
| asset management data analytics: Smart Metering Applications Nikolaos Efkarpidis, Martin Geidl, Holger Wache, Marco Peter, Marc Adam, 2022-10-03 This book presents a large number of smart metering applications from the points of view of different stakeholders. The applications are clustered with respect to three types of stakeholders: (a) end-customers, (b) energy service providers, and (c) authorities/research institutions or other organizations. The goal of the book is to examine the implementation potential for each application, considering the interests and benefits for the key stakeholders, main technical and regulatory requirements, as well as limitations and barriers. A business case for each application is created that can provide guidelines to the stakeholders involved in its realization. The book additionally investigates current business models for smart metering applications. A survey on the current techno-economic potential of such applications is conducted based on a questionnaire filled by various stakeholders. The book will be of interest to academic/research institutions, but also engineers in industry, authorities or other organizations. |
| asset management data analytics: Proceedings of the Future Technologies Conference (FTC) 2023, Volume 4 Kohei Arai, 2023-12-14 This book is a collection of thoroughly well-researched studies presented at the Eighth Future Technologies Conference. This annual conference aims to seek submissions from the wide arena of studies like Computing, Communication, Machine Vision, Artificial Intelligence, Ambient Intelligence, Security, and e-Learning. With an impressive 490 paper submissions, FTC emerged as a hybrid event of unparalleled success, where visionary minds explored groundbreaking solutions to the most pressing challenges across diverse fields. These groundbreaking findings open a window for vital conversation on information technologies in our community especially to foster future collaboration with one another. We hope that the readers find this book interesting and inspiring and render their enthusiastic support toward it. |
| asset management data analytics: Predictive Analytics for Marketers Barry Leventhal, 2018-02-03 Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics can be used to successfully achieve a range of business purposes. |
| asset management data analytics: Research Anthology on Big Data Analytics, Architectures, and Applications Management Association, Information Resources, 2021-09-24 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| asset management data analytics: Smarter Asset Management for the Integrated Mill Akram Bou-Ghannam, PhD, IBM Redbooks, 2016-03-18 IBM® Smarter Asset Management for Integrated Mill Operations helps metals companies optimize asset performance and maintenance in the large, unified facilities known as integrated mills. The solution described in this IBM Redbooks® Solution Guide gives metal-making companies direct visibility into asset usage and operational health. It helps executives and managers at all levels make decisions that are based on accurate, up-to-date reports on the running conditions and performance of their most critical assets. It also includes predictive analytics features that can help companies get ahead of the curve in terms of plant maintenance and turnarounds. By combining sensor-based condition monitoring with advanced analytics, the solution delivers actionable, data-driven insights to aid in daily operations, shutdown and turnaround management, and regulatory compliance . It helps reduce asset downtime by ensuring maintenance is performed exactly (and only) when needed, and improves overall productivity, which increases operational equipment efficiency (OEE). |
| asset management data analytics: Big Data Analytics Framework for Smart Grids Rajkumar Viral, Divya Asija, Surender Salkuti, 2023-12-22 The text comprehensively discusses smart grid operations and the use of big data analytics in overcoming the existing challenges. It covers smart power generation, transmission, and distribution, explains energy management systems, artificial intelligence, and machine learning–based computing. Presents a detailed state-of-the-art analysis of big data analytics and its uses in power grids Describes how the big data analytics framework has been used to display energy in two scenarios including a single house and a smart grid with thousands of smart meters Explores the role of the internet of things, artificial intelligence, and machine learning in smart grids Discusses edge analytics for integration of generation technologies, and decision-making approaches in detail Examines research limitations and presents recommendations for further research to incorporate big data analytics into power system design and operational frameworks The text presents a comprehensive study and assessment of the state-of-the-art research and development related to the unique needs of electrical utility grids, including operational technology, storage, processing, and communication systems. It further discusses important topics such as complex adaptive power system, self-healing power system, smart transmission, and distribution networks, and smart metering infrastructure. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology. |
| asset management data analytics: Digital Twins for Smart Cities and Villages Sailesh Iyer, Anand Nayyar, Anand Paul, Mohd Naved, 2024-11-01 Digital Twins for Smart Cities and Villages provides a holistic view of digital twin technology and how it can be deployed to develop smart cities and smart villages. Smart manufacturing, smart healthcare, smart education, smart agriculture, smart rural solutions, and related methodologies using digital twins are discussed, including challenges in deployment, their solutions and future roadmaps. This knowledge, enriched by a variety of case studies presented in the book, may empower readers with new capabilities for new research as well as new tasks and strategies for practical implementation and real-world problem solving.The book is thoughtfully structured, starting from the background of digital twin concepts and basic know-how to serve the needs of those new to the subject. It continues with implementation to facilitate and improve management in several urban contexts, infrastructures, and more. Global case study assessments further provide a deep characterization of the state-of-the-art in digital twin in urban and rural contexts. - Uniquely focuses on applications for smart cities and villages, including smart services for health, education, mobility, and agriculture - Provides use cases and practical deployment of research involved in the emerging uses of digital twins - Discusses all pertinent issues, challenges, and possible solutions instrumental in implementing digital twins smart solutions in this context - Edited and authored by a global team of experts in their given fields |
| asset management data analytics: Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops Ilias Maglogiannis, Lazaros Iliadis, John Macintyre, Paulo Cortez, 2022-06-16 This book constitutes the refereed proceedings of five International Workshops held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, virtually and in Hersonissos, Crete, Greece, in June 2022: the 11th Mining Humanistic Data Workshop (MHDW 2022); the 7th 5G-Putting Intelligence to the Network Edge Workshop (5G-PINE 2022); the 1st workshop on AI in Energy, Building and Micro-Grids (AIBMG 2022); the 1st Workshop/Special Session on Machine Learning and Big Data in Health Care (ML@HC 2022); and the 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics (AIBEI 2022). The 35 full papers presented at these workshops were carefully reviewed and selected from 74 submissions. |
| asset management data analytics: Managerial and Entrepreneurial Decision Making Matteo Cristofaro, Maria José Sousa, José Carlos Sánchez-García, 2021-06-10 Since the conceptualization of bounded rationality, management scholars started investigating how people—managers and entrepreneurs—really make decisions within (and for) organizations. The aim of this eBook is to deeply investigate trends that have flourished within this pivotal research area in conceptual and/or empirical terms, trying to provide new insights on how managers and entrepreneurs make decisions within and for organizations. In this vein, readers that approach this eBook will be taken by hand and accompanied to the discovery of how the mind of decision makers is at the basis of organizational developments or failures. In this regard, published contributions in this eBook underline how executives and entrepreneurs must be ecologically rational, thus be aware of the negative and positive effects that biases can have depending on the context and use them at their advantage. Managerial and entrepreneurial decision-making are phenomena that cannot be detached from the environment in which executives and entrepreneurs are embedded, claiming to establish new approaches to research that looks at decision-making as an individual/group/organization-environment dialectical and multi-level phenomenon. |
| asset management data analytics: Internet of Things and Big Data Analytics-Based Manufacturing Arun Kumar Rana, Sudeshna Chakraborty, Pallavi Goel, Sumit Kumar Rana, Ahmed A. Elngar, 2024-10-17 By enabling the conversion of traditional manufacturing systems into contemporary digitalized ones, Internet of Things (IoT) adoption in manufacturing creates huge economic prospects through reshaping industries. Modern businesses can more readily implement new data-driven strategies and deal with the pressure of international competition thanks to Industrial IoT. But as the use of IoT grows, the amount of created data rises, turning industrial data into Industrial Big Data. Internet of Things and Big Data Analytics-Based Manufacturing shows how Industrial Big Data can be produced as a result of IoT usage in manufacturing, considering sensing systems and mobile devices. Different IoT applications that have been developed are demonstrated and it is shown how genuine industrial data can be produced, leading to Industrial Big Data. This book is organized into four sections discussing IoT and technology, the future of Big Data, algorithms, and case studies demonstrating the use of IoT and Big Data in a variety of industries, including automation, industrial manufacturing, and healthcare. This reference title brings all related technologies into a single source so that researchers, undergraduate and postgraduate students, academicians, and those in the industry can easily understand the topic and further their knowledge. |
| asset management data analytics: Machine Intelligence and Data Science Applications Vaclav Skala, T. P. Singh, Tanupriya Choudhury, Ravi Tomar, Md. Abul Bashar, 2022-08-01 This book is a compilation of peer reviewed papers presented at International Conference on Machine Intelligence and Data Science Applications (MIDAS 2021), held in Comilla University, Cumilla, Bangladesh during 26 – 27 December 2021. The book covers applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, speech and gesture analysis, etc. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber physical system and smart agriculture, etc. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates. |
| asset management data analytics: Data Analytics for Corporate Debt Markets Robert Kricheff, 2014 Written for portfolio managers, traders, analysts, marketers, investment bankers, and other financial practitioners, this book introduces the key data analytics tools, methods, and applications currently used in the corporate debt market. Robert Kricheff shows how data analytics can improve and accelerate the process of proper investment selection, and guides market participants in focusing their credit work. Kricheff demonstrates how to use analytics to position yourself for the future; to assess how your current portfolio or trading desk is presently positioned relative to the marketplace; and to pinpoint which part of your holdings impacted past performance. He outlines how analytics can be used to compare markets, develop investment themes, and select debt issues that fit (or do not fit) those themes. He also demonstrates how investors seek to analyze short term supply and demand, and covers some special parts of the market that utilize analytics. Coverage includes: Why corporate debt analysis is different, and how data analytics can help The essential terminology and tools of data mining and analytics The markets and the players Indexes and index construction Analytics from macro market data to credit selection Analyzing market technicals Special Vehicles: Liquid Bond Indexes, Credit Default Swaps and Indexes, and ETFs Collateralized Loan Obligations (CLOs) Tools for portfolio analysis The future of data analytics in corporate debt markets |
Asset Recovery Services | Dell USA
Transparency is essential for an asset lifecycle strategy that supports your sustainability goals. In alignment with ISO 14040/44 guidelines, our dynamic and personalized Environmental Impact …
Using Dell Command Configure to Set The Asset Tag Information …
Jun 9, 2025 · Check the BIOS to ensure that the Asset Tag is correct. Using CCTK Tool (CLI) NOTE: Dell Client Configuration Toolkit is a packaged software offering that provides scripted …
Dell Asset Tag Utility, A01 | Driver Details | Dell US
Jun 30, 2004 · The Asset Tag Tool provides the ability to read and display the FRU fields Asset Tag, Service Tag, and PPID. It also provides the capability to update the Asset Tag field. This …
New 7020 Small form factor and Tower spec sheet - Dell
May 29, 2024 · https://www.delltechnologies.com/asset/en-us/products/desktops-and-all-in-ones/technical-support/optiplex-sff-spec-sheet-7020.pdf.external gen ID: 7020 Intel 14th gen
Dell Asset Utility | Driver Details | Dell US
May 30, 2013 · Dell Asset Utility Installed This file was automatically installed as part of a recent update. If you are experiencing any issues, you can manually download and reinstall.
Service Tag change? - Dell
Feb 15, 2009 · The Asset Tag Utility allows asset tag and service tag numbers to be entered into the system's NVRAM where they can be viewed by the System Setup screens. The utility is …
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Get support for your Dell product with free diagnostic tests, drivers, downloads, how-to articles, videos, FAQs and community forums.
How to Find Warranty Status and Information for Your Dell Product
3 days ago · Warranty and Ownership Transfer - You may request a warranty or ownership transfer if you have recently purchased or received a used Dell product, the Dell product is …
Drivers & Downloads | Dell US
Having an issue with your display, audio, or touchpad? Whether you're working on an Alienware, Inspiron, Latitude, or other Dell product, driver updates keep your device running at top …
Dell APEX PC as a Service
Dell APEX PC as a Service (PCaaS) is a complete IT solution that simplifies PC lifecycle management by combining hardware, software, lifecycle services & financing.
Asset Recovery Services | Dell USA
Transparency is essential for an asset lifecycle strategy that supports your sustainability goals. In alignment with ISO 14040/44 guidelines, our dynamic and personalized Environmental Impact …
Using Dell Command Configure to Set The Asset Tag Information …
Jun 9, 2025 · Check the BIOS to ensure that the Asset Tag is correct. Using CCTK Tool (CLI) NOTE: Dell Client Configuration Toolkit is a packaged software offering that provides scripted …
Dell Asset Tag Utility, A01 | Driver Details | Dell US
Jun 30, 2004 · The Asset Tag Tool provides the ability to read and display the FRU fields Asset Tag, Service Tag, and PPID. It also provides the capability to update the Asset Tag field. This …
New 7020 Small form factor and Tower spec sheet - Dell
May 29, 2024 · https://www.delltechnologies.com/asset/en-us/products/desktops-and-all-in-ones/technical-support/optiplex-sff-spec-sheet-7020.pdf.external gen ID: 7020 Intel 14th gen
Dell Asset Utility | Driver Details | Dell US
May 30, 2013 · Dell Asset Utility Installed This file was automatically installed as part of a recent update. If you are experiencing any issues, you can manually download and reinstall.
Service Tag change? - Dell
Feb 15, 2009 · The Asset Tag Utility allows asset tag and service tag numbers to be entered into the system's NVRAM where they can be viewed by the System Setup screens. The utility is …
Support | Dell US
Get support for your Dell product with free diagnostic tests, drivers, downloads, how-to articles, videos, FAQs and community forums.
How to Find Warranty Status and Information for Your Dell Product
3 days ago · Warranty and Ownership Transfer - You may request a warranty or ownership transfer if you have recently purchased or received a used Dell product, the Dell product is …
Drivers & Downloads | Dell US
Having an issue with your display, audio, or touchpad? Whether you're working on an Alienware, Inspiron, Latitude, or other Dell product, driver updates keep your device running at top …
Dell APEX PC as a Service
Dell APEX PC as a Service (PCaaS) is a complete IT solution that simplifies PC lifecycle management by combining hardware, software, lifecycle services & financing.
