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| data analysis life cycle: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-03-07 An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results. |
| data analysis life cycle: Big Data Fundamentals Thomas Erl, Wajid Khattak, Paul Buhler, 2015-12-29 “This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning |
| data analysis life cycle: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| data analysis life cycle: Guidebook for Managing Data from Emerging Technologies for Transportation Kelley Klaver Pecheux, Benjamin B. Pecheux, Gene Ledbetter, Chris Lambert (Systems consultant), 2020 With increased connectivity between vehicles, sensors, systems, shared-use transportation, and mobile devices, unexpected and unparalleled amounts of data are being added to the transportation domain at a rapid rate, and these data are too large, too varied in nature, and will change too quickly to be handled by the traditional database management systems of most transportation agencies. The TRB National Cooperative Highway Research Program's NCHRP Research Report 952: Guidebook for Managing Data from Emerging Technologies for Transportation provides guidance, tools, and a big data management framework, and it lays out a roadmap for transportation agencies on how they can begin to shift - technically, institutionally, and culturally - toward effectively managing data from emerging technologies. Modern, flexible, and scalable big data methods to manage these data need to be adopted by transportation agencies if the data are to be used to facilitate better decision-making. As many agencies are already forced to do more with less while meeting higher public expectations, continuing with traditional data management systems and practices will prove costly for agencies unable to shift. |
| data analysis life cycle: Analyzing and Interpreting Qualitative Research Charles Vanover, Paul Mihas, Johnny Saldana, 2021-04-08 Drawing on the expertise of major names in the field, this text provides comprehensive coverage of the key methods for analyzing, interpreting, and writing up qualitative research in a single volume. |
| data analysis life cycle: Life Cycle Inventory Analysis Andreas Ciroth, Rickard Arvidsson, 2022-09-01 Life Cycle Inventory (LCI) Analysis is the second phase in the Life Cycle Assessment (LCA) framework. Since the first attempts to formalize life cycle assessment in the early 1970, life cycle inventory analysis has been a central part. Chapter 1 “Introduction to Life Cycle Inventory Analysis“ discusses the history of inventory analysis from the 1970s through SETAC and the ISO standard. In Chapter 2 “Principles of Life Cycle Inventory Modeling”, the general principles of setting up an LCI model and LCI analysis are described by introducing the core LCI model and extensions that allow addressing reality better. Chapter 3 “Development of Unit Process Datasets” shows that developing unit processes of high quality and transparency is not a trivial task, but is crucial for high-quality LCA studies. Chapter 4 “Multi-functionality in Life Cycle Inventory Analysis: Approaches and Solutions” describes how multi-functional processes can be identified. In Chapter 5 “Data Quality in Life Cycle Inventories”, the quality of data gathered and used in LCI analysis is discussed. State-of-the-art indicators to assess data quality in LCA are described and the fitness for purpose concept is introduced. Chapter 6 “Life Cycle Inventory Data and Databases“ follows up on the topic of LCI data and provides a state-of-the-art description of LCI databases. It describes differences between foreground and background data, recommendations for starting a database, data exchange and quality assurance concepts for databases, as well as the scientific basis of LCI databases. Chapter 7 “Algorithms of Life Cycle Inventory Analysis“ provides the mathematical models underpinning the LCI. Since Heijungs and Suh (2002), this is the first time that this aspect of LCA has been fundamentally presented. In Chapter 8 “Inventory Indicators in Life Cycle Assessment”, the use of LCI data to create aggregated environmental and resource indicators is described. Such indicators include the cumulative energy demand and various water use indicators. Chapter 9 “The Link Between Life Cycle Inventory Analysis and Life Cycle Impact Assessment” uses four examples to discuss the link between LCI analysis and LCIA. A clear and relevant link between these phases is crucial. |
| data analysis life cycle: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients. |
| data analysis life cycle: Intelligent Computing and Innovation on Data Science Sheng-Lung Peng, Le Hoang Son, G. Suseendran, D. Balaganesh, 2020-05-14 This book covers both basic and high-level concepts relating to the intelligent computing paradigm and data sciences in the context of distributed computing, big data, data sciences, high-performance computing and Internet of Things. It is becoming increasingly important to develop adaptive, intelligent computing-centric, energy-aware, secure and privacy-aware systems in high-performance computing and IoT applications. In this context, the book serves as a useful guide for industry practitioners, and also offers beginners a comprehensive introduction to basic and advanced areas of intelligent computing. Further, it provides a platform for researchers, engineers, academics and industrial professionals around the globe to showcase their recent research concerning recent trends. Presenting novel ideas and stimulating interesting discussions, the book appeals to researchers and practitioners working in the field of information technology and computer science. |
| data analysis life cycle: INFORMS Analytics Body of Knowledge James J. Cochran, 2018-10-23 Standardizes the definition and framework of analytics #2 on Book Authority’s list of the Best New Analytics Books to Read in 2019 (January 2019) We all want to make a difference. We all want our work to enrich the world. As analytics professionals, we are fortunate - this is our time! We live in a world of pervasive data and ubiquitous, powerful computation. This convergence has inspired and accelerated the development of both analytic techniques and tools and this potential for analytics to have an impact has been a huge call to action for organizations, universities, and governments. This title from Institute for Operations Research and the Management Sciences (INFORMS) represents the perspectives of some of the most respected experts on analytics. Readers with various backgrounds in analytics – from novices to experienced professionals – will benefit from reading about and implementing the concepts and methods covered here. Peer reviewed chapters provide readers with in-depth insights and a better understanding of the dynamic field of analytics The INFORMS Analytics Body of Knowledge documents the core concepts and skills with which an analytics professional should be familiar; establishes a dynamic resource that will be used by practitioners to increase their understanding of analytics; and, presents instructors with a framework for developing academic courses and programs in analytics. |
| data analysis life cycle: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| data analysis life cycle: Life Cycle Assessment Michael Z. Hauschild, Ralph K. Rosenbaum, Stig Irving Olsen, 2017-09-01 This book is a uniquely pedagogical while still comprehensive state-of-the-art description of LCA-methodology and its broad range of applications. The five parts of the book conveniently provide: I) the history and context of Life Cycle Assessment (LCA) with its central role as quantitative and scientifically-based tool supporting society’s transitioning towards a sustainable economy; II) all there is to know about LCA methodology illustrated by a red-thread example which evolves as the reader advances; III) a wealth of information on a broad range of LCA applications with dedicated chapters on policy development, prospective LCA, life cycle management, waste, energy, construction and building, nanotechnology, agrifood, transport, and LCA-related concepts such as footprinting, ecolabelling,design for environment, and cradle to cradle. IV) A cookbook giving the reader recipes for all the concrete actions needed to perform an LCA. V) An appendix with an LCA report template, a full example LCA report serving as inspiration for students who write their first LCA report, and a more detailed overview of existing LCIA methods and their similarities and differences. |
| data analysis life cycle: Win with Advanced Business Analytics Jean-Paul Isson, Jesse Harriott, 2012-09-25 Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. Provides the essential concept and framework to implement business analytics Written clearly for a nontechnical audience Filled with case studies across a variety of industries Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition. |
| data analysis life cycle: Data Analytics and AI Jay Liebowitz, 2020-08-06 Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that artificial intelligence is included. We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data. |
| data analysis life cycle: Seismic Attributes as the Framework for Data Integration Throughout the Oilfield Life Cycle Kurt J. Marfurt, 2018-01-31 Useful attributes capture and quantify key components of the seismic amplitude and texture for subsequent integration with well log, microseismic, and production data through either interactive visualization or machine learning. Although both approaches can accelerate and facilitate the interpretation process, they can by no means replace the interpreter. Interpreter “grayware” includes the incorporation and validation of depositional, diagenetic, and tectonic deformation models, the integration of rock physics systematics, and the recognition of unanticipated opportunities and hazards. This book is written to accompany and complement the 2018 SEG Distinguished Instructor Short Course that provides a rapid overview of how 3D seismic attributes provide a framework for data integration over the life of the oil and gas field. Key concepts are illustrated by example, showing modern workflows based on interactive interpretation and display as well as those aided by machine learning. |
| data analysis life cycle: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses |
| data analysis life cycle: Data Integration Life Cycle Management with SSIS Andy Leonard, 2017-11-17 Build a custom BimlExpress framework that generates dozens of SQL Server Integration Services (SSIS) packages in minutes. Use this framework to execute related SSIS packages in a single command. You will learn to configure SSIS catalog projects, manage catalog deployments, and monitor SSIS catalog execution and history. Data Integration Life Cycle Management with SSIS shows you how to bring DevOps benefits to SSIS integration projects. Practices in this book enable faster time to market, higher quality of code, and repeatable automation. Code will be created that is easier to support and maintain. The book teaches you how to more effectively manage SSIS in the enterprise environment by drawing on the art and science of modern DevOps practices. What You'll Learn Generate dozens of SSIS packages in minutes to speed your integration projects Reduce the execution of related groups of SSIS packages to a single command Successfully handle SSIS catalog deployments and their projects Monitor the execution and history of SSIS catalog projects Manage your enterprise data integration life cycle through automated tools and utilities Who This Book Is For Database professionals working with SQL Server Integration Services in enterprise environments. The book is especially useful to those readers following, or wishing to follow, DevOps practices in their use of SSIS. |
| data analysis life cycle: Reliability and Life-Cycle Analysis of Deteriorating Systems Mauricio Sánchez-Silva, Georgia-Ann Klutke, 2015-11-27 This book compiles and critically discusses modern engineering system degradation models and their impact on engineering decisions. In particular, the authors focus on modeling the uncertain nature of degradation considering both conceptual discussions and formal mathematical formulations. It also describes the basics concepts and the various modeling aspects of life-cycle analysis (LCA). It highlights the role of degradation in LCA and defines optimum design and operation parameters. Given the relationship between operational decisions and the performance of the system’s condition over time, maintenance models are also discussed. The concepts and models presented have applications in a large variety of engineering fields such as Civil, Environmental, Industrial, Electrical and Mechanical engineering. However, special emphasis is given to problems related to large infrastructure systems. The book is intended to be used both as a reference resource for researchers and practitioners and as an academic text for courses related to risk and reliability, infrastructure performance modeling and life-cycle assessment. |
| data analysis life cycle: The Computational Structure of Life Cycle Assessment R. Heijungs, Sangwon Suh, 2013-04-17 Life Cycle assessment (LCA) is a tool for environmental decision-support in relation to products from the cradle to the grave. Until now, more emphasis has been put on the inclusion quantitative models and databases and on the design of guidebooks for applying LCA than on the integrative aspect of combining these models and data. This is a remarkable thing, since LCA in practice deals with thousands of quantitative data items that have to be combined in the correct manner. For this, one needs mathematical rules and algorithmic principles for carrying out an LCA. This book presents the first coherent treatment of the mathematical and algorithmic aspects of LCA. These computational aspects are presented in matrix form, so that a concise and elegant formulation is achieved. This form, moreover, provides a platform for further extension of analysis using perturbation theory, structural theory and economic input-output analysis. |
| data analysis life cycle: SQL for Data Science Antonio Badia, 2020-11-09 This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing. The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. This book is for anyone interested in data science and/or databases. It just demands a bit of computer fluency, but no specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses. |
| data analysis life cycle: Environmental Life Cycle Analysis David F. Ciambrone, 1997-08-11 The trend in industry and with the EPA is to prevent wastes before they are created instead of treating or disposing of them later. This book assists design/systems engineers and managers in designing or changing a product or set of processes in order to minimize the negative impact on the environment during its life cycle. It explains the overall concept of environmental life cycle analysis and breaks down each of the stages, providing a clear picture of the issues involved. Chapters 1 and 2 provide an introduction and overview of the environmental life cycle analysis process. Chapter 3 establishes the basis and methodologies required for analysis through description of the basic framework, definition of boundaries, use of checklists, data gathering processes, construction of models, and interpretation of results. Templates and special cases that may be encountered and how to handle them are addressed in Chapter 4. Chapters 5 through 9 go into detail about modeling, issues, and data collection for each stage of the product life cycle. The final chapter provides a summary of the various steps and offers ideas on how to present data and reports. |
| data analysis life cycle: Life Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision Robby Caspeele, Luc Taerwe, Dan M. Frangopol, 2018-10-15 This volume contains the papers presented at IALCCE2018, the Sixth International Symposium on Life-Cycle Civil Engineering (IALCCE2018), held in Ghent, Belgium, October 28-31, 2018. It consists of a book of extended abstracts and a USB device with full papers including the Fazlur R. Khan lecture, 8 keynote lectures, and 390 technical papers from all over the world. Contributions relate to design, inspection, assessment, maintenance or optimization in the framework of life-cycle analysis of civil engineering structures and infrastructure systems. Life-cycle aspects that are developed and discussed range from structural safety and durability to sustainability, serviceability, robustness and resilience. Applications relate to buildings, bridges and viaducts, highways and runways, tunnels and underground structures, off-shore and marine structures, dams and hydraulic structures, prefabricated design, infrastructure systems, etc. During the IALCCE2018 conference a particular focus is put on the cross-fertilization between different sub-areas of expertise and the development of an overall vision for life-cycle analysis in civil engineering. The aim of the editors is to provide a valuable source of cutting edge information for anyone interested in life-cycle analysis and assessment in civil engineering, including researchers, practising engineers, consultants, contractors, decision makers and representatives from local authorities. |
| data analysis life cycle: Digital Transformation of the Design, Construction and Management Processes of the Built Environment Bruno Daniotti, Marco Gianinetto, Stefano Della Torre, 2019-12-30 This open access book focuses on the development of methods, interoperable and integrated ICT tools, and survey techniques for optimal management of the building process. The construction sector is facing an increasing demand for major innovations in terms of digital dematerialization and technologies such as the Internet of Things, big data, advanced manufacturing, robotics, 3D printing, blockchain technologies and artificial intelligence. The demand for simplification and transparency in information management and for the rationalization and optimization of very fragmented and splintered processes is a key driver for digitization. The book describes the contribution of the ABC Department of the Polytechnic University of Milan (Politecnico di Milano) to R&D activities regarding methods and ICT tools for the interoperable management of the different phases of the building process, including design, construction, and management. Informative case studies complement the theoretical discussion. The book will be of interest to all stakeholders in the building process – owners, designers, constructors, and faculty managers – as well as the research sector. |
| data analysis life cycle: Annual Report United States. Federal Emergency Management Agency, 1983 |
| data analysis life cycle: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data analysis life cycle: Life Cycle Assessment Kathrina Simonen, 2014-04-16 Life Cycle Assessment addresses the dynamic and dialectic of building and ecology, presenting the key theories and techniques surrounding the use of life cycle assessment data and methods. Architects and construction professionals must assume greater responsibility in helping building owners to understand the implications of making material, manufacturing, and assemblage decisions and therefore design to accommodate more ecological building. Life Cycle Assessment is a guide for architects, engineers, and builders, presenting the principles and art of performing life cycle impact assessments of materials and whole buildings, including the need to define meaningful goals and objectives and critically evaluate analysis assumptions. As part of the PocketArchitecture Series, the book includes both fundamentals and advanced topics. The book is primarily focused on arming the design and construction professional with the tools necessary to make design decisions regarding life cycle, reuse, and sustainability. As such, the book is a practical text on the concepts and applications of life cycle techniques and environmental impact evaluation in architecture and is presented in language and depth appropriate for building industry professionals. |
| data analysis life cycle: Environmental Life Cycle Assessment Olivier Jolliet, Myriam Saade-Sbeih, Shanna Shaked, Alexandre Jolliet, Pierre Crettaz, 2015-11-18 Environmental Life Cycle Assessment is a pivotal guide to identifying environmental problems and reducing related impacts for companies and organizations in need of life cycle assessment (LCA). LCA, a unique sustainability tool, provides a framework that addresses a growing demand for practical technological solutions. Detailing each phase of the LCA methodology, this textbook covers the historical development of LCA, presents the general principles and characteristics of LCA, and outlines the corresponding standards for good practice determined by the International Organization for Standardization. It also explains how to identify the critical aspects of an LCA, provides detailed examples of LCA analysis and applications, and includes illustrated problems and solutions with concrete examples from water management, electronics, packaging, automotive, and other industries. In addition, readers will learn how to: Use consistent criteria to realize and evaluate an LCA independently of individual interests Understand the LCA methodology and become familiar with existing databases and methods based on the latest results of international research Analyze and critique a completed LCA Apply LCA methodology to simple case studies Geared toward graduate and undergraduate students studying environmental science and industrial ecology, as well as practicing environmental engineers, and sustainability professionals who want to teach themselves LCA good practices, Environmental Life Cycle Assessment demonstrates how to conduct environmental assessments for products throughout their life cycles. It presents existing methods and recent developments in the growing field of LCA and systematically covers goal and system definition, life cycle inventory, life cycle impact assessment, and interpretation. |
| data analysis life cycle: Lean Analytics Alistair Croll, Benjamin Yoskovitz, 2024-02-23 Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you'll know it's time to move forward Apply Lean Analytics principles to large enterprises and established products |
| data analysis life cycle: Guide to Intelligent Data Science Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo, 2020-08-06 Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject. |
| data analysis life cycle: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data analysis life cycle: Understanding the Predictive Analytics Lifecycle Alberto Cordoba, 2014-08-18 A high-level, informal look at the different stages of the predictive analytics cycle Understanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects. Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes: An overview of all relevant phases: design, prepare, explore, model, communicate, and measure Coverage of the stages of the predictive analytics cycle across different industries and countries A chapter dedicated to each of the phases of the development of a predictive initiative A comprehensive overview of the entire analytic process lifecycle If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide. |
| data analysis life cycle: Database Life Cycle Open University. Relational Databases: Theory and Practice Course Team, 2007-04 This block is concerned with the database lifecycle, which describes the stages a database goes through, from the time the need for a database is established until it is withdrawn from use. This block applies the practice developed in Block 3 to systematically develop, implement and maintain a database design that supports the information requirements of an enterprise. It presents a simple framework for database development and maintenance.This is a very practical block and will require you to write and execute SQL statements for which you will need access to a computer installed with the course software (order code M359/CDR01) and database cards Scenarios and Hospital conceptual data model (order code M359/DBCARDS) |
| data analysis life cycle: Data Integrity and Data Governance R. D. McDowall, 2018-11-09 This book provides practical and detailed advice on how to implement data governance and data integrity for regulated analytical laboratories working in the pharmaceutical and allied industries. |
| data analysis life cycle: Female Empowerment A. K. Nigam, Ravindra Srivastava, A. C. Kulshreshtha, Kuldeep Kumar, 2020-04-09 Throughout their lifecycle, from before birth until death, women are subjected to various forms of gender discrimination. In India, and the wider global context, there is widespread prevalence of discrimination, from dietary intake (both in quality and content), schooling, and clothing, to health and marriage. As this volume shows, overcoming each of these disparities leads to the empowerment of women. It focuses on disparities against women emanating under the framework of mortality, natality, basic facilities, special opportunities, professional work, and the household, highlighting possible ways of combating these prejudices. |
| data analysis life cycle: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data analysis life cycle: Life Cycle Reliability Engineering Guang Yang, 2007-02-02 As the Lead Reliability Engineer for Ford Motor Company, Guangbin Yang is involved with all aspects of the design and production of complex automotive systems. Focusing on real-world problems and solutions, Life Cycle Reliability Engineering covers the gamut of the techniques used for reliability assurance throughout a product's life cycle. Yang pulls real-world examples from his work and other industries to explain the methods of robust design (designing reliability into a product or system ahead of time), statistical and real product testing, software testing, and ultimately verification and warranting of the final product's reliability |
| data analysis life cycle: Steps to Facilitate Principal-Investigator-Led Earth Science Missions National Research Council, Division on Engineering and Physical Sciences, Space Studies Board, Committee on Earth Studies, 2004-04-21 Principal-investigator (PI) Earth science missions are small, focused science projects involving relatively small spacecraft. The selected PI is responsible for the scientific and programmatic success of the entire project. A particular objective of PI-led missions has been to help develop university-based research capacity. Such missions, however, pose significant challenges that are beyond the capabilities of most universities to manage. To help NASA's Office of Earth Science determine how best to address these, the NRC carried out an assessment of key issues relevant to the success of university-based PI-led Earth observation missions. This report presents the result of that study. In particular, the report provides an analysis of opportunities to enhance such missions and recommendations about whether and, if so, how they should be used to build university-based research capabilities. |
| data analysis life cycle: Life Cycle Management Guido Sonnemann, Manuele Margni, 2015-07-16 This book provides insight into the Life Cycle Management (LCM) concept and the progress in its implementation. LCM is a management concept applied in industrial and service sectors to improve products and services, while enhancing the overall sustainability performance of business and its value chains. In this regard, LCM is an opportunity to differentiate through sustainability performance on the market place, working with all departments of a company such as research and development, procurement and marketing, and to enhance the collaboration with stakeholders along a company’s value chain. LCM is used beyond short-term business success and aims at long-term achievements by minimizing environmental and socio-economic burden, while maximizing economic and social value. |
| data analysis life cycle: Life Cycle Assessment (LCA) Walter Klöpffer, Birgit Grahl, 2014-04-21 This first hands-on guide to ISO-compliant Life Cycle Assessment (LCA) makes this powerful tool immediately accessible to both professionals and students. Following a general introduction on the philosophy and purpose of LCA, the reader is taken through all the stages of a complete LCA analysis, with each step exemplified by real-life data from a major LCA project on beverage packaging. Measures as carbon and water footprint, based on the most recent international standards and definitions, are addressed. Written by two pioneers of LCA, this practical volume is targeted at first-time LCA users but equally makes a much-valued reference for more experienced practitioners. From the content: * Goal and Scope Definition * Life Cycle Inventory Analysis * Life Cycle Impact Assessment * Interpretation, Reporting and Critical Review * From LCA to Sustainability Assessment and more. |
| data analysis life cycle: Business Intelligence Roadmap Larissa Terpeluk Moss, S. Atre, 2003 This software will enable the user to learn about business intelligence roadmap. |
| data analysis life cycle: Big Data Analytics with R and Hadoop Vignesh Prajapati, 2013 Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R. |
The Data Life Cycle
Oct 4, 2019 · To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user.
Data Management Life Cycle Final report - Texas A&M …
Researchers developed the data management life cycle to organize data, characterize its nature and value over time, and identify policy implications of cross-cutting data management issues.
Data lifecycle and data management planning - UK Data Service
DATA CREATED FROM RESEARCH ARE VALUABLE RESOURCES THAT CAN BE USED AND RE-USED FOR FUTURE SCIENTIFIC AND EDUCATIONAL PURPOSES. SHARING …
Big Data Life Cycle - School of Engineering & Applied Science
Big data lifecycle differs from traditional lifecycle due to velocity and volume. It is a repetitive set of steps that you need to take to complete and deliver a project to your client. Different big data …
Research Data Management 101: The Lifecycle of a Dataset
Metadata, or “data about data” explains your dataset and allows you to document important information for: • Finding the data later • Understanding what the data is later • Sharing the …
Big Data Analytics: Life cycle and its Tools - JETIR
In big data analytics the data can be driven by the life cycle which includes stages. And for examining the big data, various tools were developed to gain insights of what actually is going …
The Data Life Cycle - PubPub
Data science is the study of extracting value from data. “Value” is subject to the interpretation by the end user and “extracting” represents the work done in all phases of the data life cycle (see …
The Data Life Cycle - FCSM
Nov 10, 2022 · How my own research has been affected/evolved … • Data Quality (Multi- source Inference/Learning) • Data Privacy (Multi -phase Inference/Learning) • Data Granularity (Multi …
Fundamentals of Data Analytics and Lifecycle - Springer
Data Analytics evolution with big data analytics, SQL analytics, and business analytics is explained. Furthermore, the chapter outlines the future of data analytics by leveraging its …
Data Analysis Life Cycle - stats.communityfunded.com
Data Management Life Cycle Phases The stages of the data management life cycle—collect, process, store and secure, use, share and communicate, archive, reuse/repurpose, and …
Data Life Cycle And The Data Analysis Process
democratization of data and governance principles Through good data governance you can inspire customer trust enable your organization to extract more value from data and generate …
Data Lifecycle and Analytics in the AWS Cloud - Amazon Web …
• How do you collect and analyze high-velocity data across a variety of data types – structured, unstructured, and semistructured? • How do you scale up IT resources to run thousands of …
What we Learned About the Data Science Life Cycle: Best …
Characteristics of Data Analysis Phases •From the beginning it was clear that the phases interact •Central is the understanding of these phases as an iterative process •They are passed …
Exploratory Data Analysis Life cycle-An Overview - IJCRT
data analysis life cycle. Keywords: Data Analysis, Data Science, Analytics, Statistical analyses I. INTRODUCTION In exploratory data analysis process the very first thing to be understood is …
Data Preparation in the Analytical Life Cycle - SAS
There are two main phases in the analytical life cycle (Figure 2): discovery and deployment. First, we ask a question. The discovery process is driven by asking business questions and defining …
Data lifecycles analysis: towards intelligent cycle
In this paper, we study the available lifecycles of data in the literature that we consider relevant. As a result of this study, we classify these cycles following two types: first analysis oriented …
Data Modeling and Data Analytics Lifecycle - IJARSCT
The Data Analytics Lifecycle is designed specifically for big data problems and data science projects. The iterative depiction of lifecycle is intended to more closely portray a real project, in …
Data Life Cycle And The Data Analysis Process - new.frcog.org
learning Data Governance: The Definitive Guide Evren Eryurek,Uri Gilad,Valliappa Lakshmanan,Anita Kibunguchy-Grant,Jessi Ashdown,2021-03-08 As your company moves …
Best practices in the real-world data life cycle - PLOS
The illustrated life cycle is a series of necessary or recommended steps that produce RWD usable for analysis, from raw data generated by clinical encounters or operational workflows. Insights …
Managing the Analytics Life Cycle for Decisions at Scale
At SAS, we’ve developed an iterative analytics life cycle to guide you step-by-step through the process of going from data to decision. We begin by realizing that there are two sides to the …
Life Cycle Greenhouse Gas Emissions from Electricity …
Title: Life Cycle Greenhouse Gas Emissions from Electricity Generation: Update Author: Scott Nicholson and Garvin Heath Subject: As clean energy increasingly becomes part of the …
The Data Life Cycle
Oct 4, 2019 · To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user.
Data Management Life Cycle Final report - Texas A&M …
Researchers developed the data management life cycle to organize data, characterize its nature and value over time, and identify policy implications of cross-cutting data management issues.
Data lifecycle and data management planning - UK Data …
DATA CREATED FROM RESEARCH ARE VALUABLE RESOURCES THAT CAN BE USED AND RE-USED FOR FUTURE SCIENTIFIC AND EDUCATIONAL PURPOSES. SHARING …
Big Data Life Cycle - School of Engineering & Applied Science
Big data lifecycle differs from traditional lifecycle due to velocity and volume. It is a repetitive set of steps that you need to take to complete and deliver a project to your client. Different big data …
Research Data Management 101: The Lifecycle of a Dataset
Metadata, or “data about data” explains your dataset and allows you to document important information for: • Finding the data later • Understanding what the data is later • Sharing the …
Big Data Analytics: Life cycle and its Tools - JETIR
In big data analytics the data can be driven by the life cycle which includes stages. And for examining the big data, various tools were developed to gain insights of what actually is going …
The Data Life Cycle - PubPub
Data science is the study of extracting value from data. “Value” is subject to the interpretation by the end user and “extracting” represents the work done in all phases of the data life cycle (see …
The Data Life Cycle - FCSM
Nov 10, 2022 · How my own research has been affected/evolved … • Data Quality (Multi- source Inference/Learning) • Data Privacy (Multi -phase Inference/Learning) • Data Granularity (Multi …
Fundamentals of Data Analytics and Lifecycle - Springer
Data Analytics evolution with big data analytics, SQL analytics, and business analytics is explained. Furthermore, the chapter outlines the future of data analytics by leveraging its …
Data Analysis Life Cycle - stats.communityfunded.com
Data Management Life Cycle Phases The stages of the data management life cycle—collect, process, store and secure, use, share and communicate, archive, reuse/repurpose, and …
Data Life Cycle And The Data Analysis Process
democratization of data and governance principles Through good data governance you can inspire customer trust enable your organization to extract more value from data and generate …
Data Lifecycle and Analytics in the AWS Cloud - Amazon …
• How do you collect and analyze high-velocity data across a variety of data types – structured, unstructured, and semistructured? • How do you scale up IT resources to run thousands of …
What we Learned About the Data Science Life Cycle: Best …
Characteristics of Data Analysis Phases •From the beginning it was clear that the phases interact •Central is the understanding of these phases as an iterative process •They are passed …
Exploratory Data Analysis Life cycle-An Overview - IJCRT
data analysis life cycle. Keywords: Data Analysis, Data Science, Analytics, Statistical analyses I. INTRODUCTION In exploratory data analysis process the very first thing to be understood is …
Data Preparation in the Analytical Life Cycle - SAS
There are two main phases in the analytical life cycle (Figure 2): discovery and deployment. First, we ask a question. The discovery process is driven by asking business questions and defining …
Data lifecycles analysis: towards intelligent cycle
In this paper, we study the available lifecycles of data in the literature that we consider relevant. As a result of this study, we classify these cycles following two types: first analysis oriented …
Data Modeling and Data Analytics Lifecycle - IJARSCT
The Data Analytics Lifecycle is designed specifically for big data problems and data science projects. The iterative depiction of lifecycle is intended to more closely portray a real project, in …
Data Life Cycle And The Data Analysis Process - new.frcog.org
learning Data Governance: The Definitive Guide Evren Eryurek,Uri Gilad,Valliappa Lakshmanan,Anita Kibunguchy-Grant,Jessi Ashdown,2021-03-08 As your company moves …
Best practices in the real-world data life cycle - PLOS
The illustrated life cycle is a series of necessary or recommended steps that produce RWD usable for analysis, from raw data generated by clinical encounters or operational workflows. Insights …
Managing the Analytics Life Cycle for Decisions at Scale
At SAS, we’ve developed an iterative analytics life cycle to guide you step-by-step through the process of going from data to decision. We begin by realizing that there are two sides to the …
Life Cycle Greenhouse Gas Emissions from Electricity …
Title: Life Cycle Greenhouse Gas Emissions from Electricity Generation: Update Author: Scott Nicholson and Garvin Heath Subject: As clean energy increasingly becomes part of the …
