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Data-Driven Decision Making in Entrepreneurship: Tools for Maximizing Human Capital
by Nikki Blacksmith Maureen E. McCuskerSince the beginning of the 21st century, there has been an explosion in startup organizations. Together, these organizations have been valued at over $3 trillion. In 2019, alone, nearly $300 billion of venture capital was invested globally (Global Startup Ecosystem Report 2020). Simultaneously, an explosion in high volume and high velocity of big data is rapidly changing how organizations function. Gone are the days where organizations can make decisions solely on intuition, logic, or experience. Some have gone as far as to say that data is the most valuable currency and resource available to businesses, and startups are no exception. However, startups and small businesses do differ from their larger counterparts and corporations in three distinct ways: 1) they tend to have fewer resources, time, and specialized training to devote to data analytics; 2) they are part of a unique entrepreneurial ecosystem with unique needs; 3) scholarship and academic research on human capital data analytics in startups is lacking. Existing entrepreneurship research focuses almost exclusively on macro-level aspects. There has been little to no integration of micro- and meso-level research (i.e., individual and team sciences), which is unfortunate given how organizational scientists have significantly advanced human capital data analytics. Unlike other books focused on data analytics and decision for organizations, this proposed book is purposefully designed to be more specifically aimed at addressing the unique idiosyncrasies of the science, research, and practice of startups. Each chapter highlights a specific organizational domain and discuss how a novel data analytic technique can help enhance decision-making, provides a tutorial of said regarding the data analytic technique, and lists references and resources for the respective data analytic technique. The volume will be grounded in sound theory and practice of organizational psychology, entrepreneurship and management and is divided into two parts: assessing and evaluating human capital performance and the use of data analytics to manage human capital.
Data-Driven Decision Making in Entrepreneurship: Tools for Maximizing Human Capital
Since the beginning of the 21st century, there has been an explosion in startup organizations. Together, these organizations have been valued at over $3 trillion. In 2019, alone, nearly $300 billion of venture capital was invested globally (Global Startup Ecosystem Report 2020). Simultaneously, an explosion in high volume and high velocity of big data is rapidly changing how organizations function. Gone are the days where organizations can make decisions solely on intuition, logic, or experience. Some have gone as far as to say that data is the most valuable currency and resource available to businesses, and startups are no exception. However, startups and small businesses do differ from their larger counterparts and corporations in three distinct ways: 1) they tend to have fewer resources, time, and specialized training to devote to data analytics; 2) they are part of a unique entrepreneurial ecosystem with unique needs; 3) scholarship and academic research on human capital data analytics in startups is lacking. Existing entrepreneurship research focuses almost exclusively on macro-level aspects. There has been little to no integration of micro- and meso-level research (i.e., individual and team sciences), which is unfortunate given how organizational scientists have significantly advanced human capital data analytics. Unlike other books focused on data analytics and decision for organizations, this proposed book is purposefully designed to be more specifically aimed at addressing the unique idiosyncrasies of the science, research, and practice of startups. Each chapter highlights a specific organizational domain and discuss how a novel data analytic technique can help enhance decision-making, provides a tutorial of said regarding the data analytic technique, and lists references and resources for the respective data analytic technique. The volume will be grounded in sound theory and practice of organizational psychology, entrepreneurship and management and is divided into two parts: assessing and evaluating human capital performance and the use of data analytics to manage human capital.
Data-Driven Decision-Making in Schools: Lessons From Trinidad
by J. Yamin-AliYamin-Ali shows how schools can undertake responsible decision-making through gathering and evaluating data, using as examples six fully developed case studies that shed light on common questions of school culture and student life, including student stress, subject selection, and the role of single-sex classes.
Data Driven Decision Making using Analytics (Computational Intelligence Techniques)
by Parul GandhiThis book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining. Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics. This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.
Data Driven Decision Making using Analytics (Computational Intelligence Techniques)
by Parul Gandhi Surbhi Bhatia Kapal DevThis book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining. Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics. This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.
Data Driven Decisions: Systems Engineering to Understand Corporate Value and Intangible Assets
by Joshua JahaniExpand your enterprise into new regions using systems engineering and data analysis In Data Driven Decisions: Systems Engineering to Understand Corporate Valuation and Intangible Assets, investment banker, systems engineer, and Cornell University lecturer Joshua Michael Jahani delivers an incisive and unique unveiling of how to use the tools of systems engineering to value your organization, its intangible assets, and how to gauge or prepare its readiness for an overseas or cross-border expansion. In the book, you’ll learn to implement a wide range of systems engineering tools, including context diagrams, decision matrices, Goal-Question-Metric analyses, and more. You’ll also discover the following: How to communicate corporate value measurements and their impact to owners, executives, and investors. Explorations of the relevant topics when considering an international expansion, including macroeconomics, joint ventures, market entry, corporate valuations, mergers and acquisitions, and company culture. A comprehensive framework and methodology for examining available global regions in your search for the perfect expansion target. A deep understanding of specific sectors in which intangible assets have a particular impact, including branded consumer products, ad-tech, and healthcare.A must-have resource for business owners, managers, executives, directors, and other corporate leaders, Data-Driven Decisions will also prove invaluable to consultants and other professionals who serve companies considering expansion or growth into new regions.
Data Driven Decisions: Systems Engineering to Understand Corporate Value and Intangible Assets
by Joshua JahaniExpand your enterprise into new regions using systems engineering and data analysis In Data Driven Decisions: Systems Engineering to Understand Corporate Valuation and Intangible Assets, investment banker, systems engineer, and Cornell University lecturer Joshua Michael Jahani delivers an incisive and unique unveiling of how to use the tools of systems engineering to value your organization, its intangible assets, and how to gauge or prepare its readiness for an overseas or cross-border expansion. In the book, you’ll learn to implement a wide range of systems engineering tools, including context diagrams, decision matrices, Goal-Question-Metric analyses, and more. You’ll also discover the following: How to communicate corporate value measurements and their impact to owners, executives, and investors. Explorations of the relevant topics when considering an international expansion, including macroeconomics, joint ventures, market entry, corporate valuations, mergers and acquisitions, and company culture. A comprehensive framework and methodology for examining available global regions in your search for the perfect expansion target. A deep understanding of specific sectors in which intangible assets have a particular impact, including branded consumer products, ad-tech, and healthcare.A must-have resource for business owners, managers, executives, directors, and other corporate leaders, Data-Driven Decisions will also prove invaluable to consultants and other professionals who serve companies considering expansion or growth into new regions.
Data-Driven Design for Computer-Supported Collaborative Learning: Design Matters (Lecture Notes in Educational Technology)
by Lanqin ZhengThis book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design collaborative learning activities based on data-driven design approaches, while the second shares illustrative examples of computer-supported collaborative learning activities. In turn, the third and last section demonstrates how to evaluate design quality and the fidelity of enactment based on design-centered research.The book features several examples of innovative data-driven design approaches to optimizing collaborative learning activities; highlights innovative CSCL activities in authentic learning environments; demonstrates how learning analytics can be used to optimize CSCL design; and discusses the design-centered research approach to evaluating the alignment between design and enactment in CSCL. Given its scope, it will be of interest to a broad readership including researchers, educators, practitioners, and students in the field of collaborative learning, as well as the rapidly growing community of people who are interested in optimizing learning performance with CSCL.
Data Driven e-Science: Use Cases and Successful Applications of Distributed Computing Infrastructures (ISGC 2010)
by Simon C. Lin Eric YenISGC 2010, The International Symposium on Grid Computing was held at Academia Sinica, Taipei, Taiwan, March, 2010. The 2010 symposium brought together prestigious scientists and engineers worldwide to exchange ideas, present challenges/solutions and to discuss new topics in the field of Grid Computing. Data Driven e-Science: Use Cases and Successful Applications of Distributed Computing Infrastructures (ISGC 2010), an edited volume, introduces the latest achievements in grid technology for Biomedicine Life Sciences, Middleware, Security, Networking, Digital Library, Cloud Computing and more. This book provides Grid developers and end users with invaluable information for developing grid technology and applications. The last section of this book presents future development in the field of Grid Computing. This book is designed for a professional audience composed of grid users, developers and researchers working in the field of grid computing. Advanced-level students focused on computer science and engineering will also find this book valuable as a reference or secondary text book.
Data-Driven Engineering Design
by Ang Liu Yuchen Wang Xingzhi WangThis book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design.Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation.Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design.
Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science (Studies in Computational Intelligence #975)
by Yaochu Jin Handing Wang Chaoli SunIntended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture
by Syed Nisar Hussain BukhariIn the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies.Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture
In the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies.Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
Data-driven Generation of Policies (SpringerBriefs in Computer Science)
by Austin Parker Gerardo I. Simari Amy Sliva V.S. SubrahmanianThis Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.
Data-Driven HR: How to Use Analytics and Metrics to Drive Performance
by Bernard MarrTraditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-Driven HR is a practical guide which enables HR professionals to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, collect information in a transparent way that is in line with data protection requirements and turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-Driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR professionals looking to make a measurable difference in their organizations.
Data-Driven HR: How to Use Analytics and Metrics to Drive Performance
by Bernard MarrTraditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-Driven HR is a practical guide which enables HR professionals to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, collect information in a transparent way that is in line with data protection requirements and turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-Driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR professionals looking to make a measurable difference in their organizations.
Data-Driven Innovation: Why the Data-Driven Model Will Be Key to Future Success
by Michael Moesgaard Andersen Torben PedersenToday, innovation does not just occur in large and incumbent R&D organizations. Instead, it often emerges from the start-up community. In the new innovation economy, the key is to quickly find pieces of innovation, some of which may already be developed. Therefore, there is the need for more advanced means of searching and identifying innovation wherever it may occurs. We point to the importance of data-driven innovation based on digital platforms, as their footprints are growing rapidly and in sync with the shift from analogue to digital innovation workflows. This book offers companies insights on paths to business success and tools that will help them find the right route through the various options when it comes to the digital platforms where innovations may be discovered and from which value may be appropriated. The world hungers for growth and one of the most important vehicles for growth is innovation. In light of the new digital platforms from which data-driven innovation can be extracted, major parts of analogue workflows will be substituted with digital workflows. Data-driven innovation and digital innovation workflows are here to stay. Are you?
Data-Driven Innovation: Why the Data-Driven Model Will Be Key to Future Success
by Michael Moesgaard Andersen Torben PedersenToday, innovation does not just occur in large and incumbent R&D organizations. Instead, it often emerges from the start-up community. In the new innovation economy, the key is to quickly find pieces of innovation, some of which may already be developed. Therefore, there is the need for more advanced means of searching and identifying innovation wherever it may occurs. We point to the importance of data-driven innovation based on digital platforms, as their footprints are growing rapidly and in sync with the shift from analogue to digital innovation workflows. This book offers companies insights on paths to business success and tools that will help them find the right route through the various options when it comes to the digital platforms where innovations may be discovered and from which value may be appropriated. The world hungers for growth and one of the most important vehicles for growth is innovation. In light of the new digital platforms from which data-driven innovation can be extracted, major parts of analogue workflows will be substituted with digital workflows. Data-driven innovation and digital innovation workflows are here to stay. Are you?
Data-Driven Innovation for Intelligent Technology: Perspectives and Applications in ICT (Studies in Big Data #148)
by Hiram Ponce Jorge Brieva Octavio Lozada-Flores Lourdes Martínez-Villaseñor Ernesto Moya-AlborThis book focuses on new perspectives and applications of data-driven innovation technologies, applied artificial intelligence, applied machine learning and deep learning, data science, and topics related to transforming data into value.It includes theory and use cases to help readers understand the basics of data-driven innovation and to highlight the applicability of the technologies. It emphasizes how the data lifecycle is applied in current technologies in different business domains and industries, such as advanced materials, healthcare and medicine, resource optimization, control and automation, among others.This book is useful for anyone interested in data-driven innovation for smart technologies, as well as those curious in implementing cutting-edge technologies to solve impactful artificial intelligence, data science, and related information technology and communication problems.
Data-Driven Intelligence in Wireless Networks: Concepts, Solutions, and Applications
by Muhammad Khalil Afzal Muhammad Ateeq Sung Won KimThis book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence.This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks. The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.
Data-Driven Intelligence in Wireless Networks: Concepts, Solutions, and Applications
by Muhammad Khalil Afzal Muhammad Ateeq Sung Won KimThis book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence.This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks. The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.
Data-Driven Iterative Learning Control for Discrete-Time Systems (Intelligent Control and Learning Systems #2)
by Ronghu Chi Yu Hui Zhongsheng HouThis book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
Data-Driven Law: Data Analytics and the New Legal Services (Data Analytics Applications)
by Ed WaltersFor increasingly data-savvy clients, lawyers can no longer give "it depends" answers rooted in anecdata. Clients insist that their lawyers justify their reasoning, and with more than a limited set of war stories. The considered judgment of an experienced lawyer is unquestionably valuable. However, on balance, clients would rather have the considered judgment of an experienced lawyer informed by the most relevant information required to answer their questions. Data-Driven Law: Data Analytics and the New Legal Services helps legal professionals meet the challenges posed by a data-driven approach to delivering legal services. Its chapters are written by leading experts who cover such topics as: Mining legal data Computational law Uncovering bias through the use of Big Data Quantifying the quality of legal services Data mining and decision-making Contract analytics and contract standards In addition to providing clients with data-based insight, legal firms can track a matter with data from beginning to end, from the marketing spend through to the type of matter, hours spent, billed, and collected, including metrics on profitability and success. Firms can organize and collect documents after a matter and even automate them for reuse. Data on marketing related to a matter can be an amazing source of insight about which practice areas are most profitable. Data-driven decision-making requires firms to think differently about their workflow. Most firms warehouse their files, never to be seen again after the matter closes. Running a data-driven firm requires lawyers and their teams to treat information about the work as part of the service, and to collect, standardize, and analyze matter data from cradle to grave. More than anything, using data in a law practice requires a different mindset about the value of this information. This book helps legal professionals to develop this data-driven mindset.
Data-Driven Law: Data Analytics and the New Legal Services (Data Analytics Applications)
by Edward J. WaltersFor increasingly data-savvy clients, lawyers can no longer give "it depends" answers rooted in anecdata. Clients insist that their lawyers justify their reasoning, and with more than a limited set of war stories. The considered judgment of an experienced lawyer is unquestionably valuable. However, on balance, clients would rather have the considered judgment of an experienced lawyer informed by the most relevant information required to answer their questions. Data-Driven Law: Data Analytics and the New Legal Services helps legal professionals meet the challenges posed by a data-driven approach to delivering legal services. Its chapters are written by leading experts who cover such topics as: Mining legal data Computational law Uncovering bias through the use of Big Data Quantifying the quality of legal services Data mining and decision-making Contract analytics and contract standards In addition to providing clients with data-based insight, legal firms can track a matter with data from beginning to end, from the marketing spend through to the type of matter, hours spent, billed, and collected, including metrics on profitability and success. Firms can organize and collect documents after a matter and even automate them for reuse. Data on marketing related to a matter can be an amazing source of insight about which practice areas are most profitable. Data-driven decision-making requires firms to think differently about their workflow. Most firms warehouse their files, never to be seen again after the matter closes. Running a data-driven firm requires lawyers and their teams to treat information about the work as part of the service, and to collect, standardize, and analyze matter data from cradle to grave. More than anything, using data in a law practice requires a different mindset about the value of this information. This book helps legal professionals to develop this data-driven mindset.
Data-driven Marketing: Insights aus Wissenschaft und Praxis
by Silvia Boßow-Thies Christina Hofmann-Stölting Heike JochimsState-of-the-art Wissen zum Data-driven Marketing aus Forschung und PraxisFokussiert auf die entscheidenden Aspekte für ein erfolgreiches datengetriebenes MarketingAutoren sind Top-Experten aus der Praxis und der WissenschaftDieses Buch adressiert die entscheidenden Aspekte für ein erfolgreiches, datengetriebenes Marketing: Datenqualität, Datenanalyse, kreative, aber datenschutzkonforme und ethisch vertretbare Datennutzung. Die Herausgeberinnen haben dazu das aktuelle Know-how aus Wissenschaft und Praxis für die strategische und operative Marketingarbeit zusammengetragen. So ist ein wertvoller Impulsgeber und Leitfaden für Marketing-Professionals entstanden, die Ihre Marketingarbeit konsequent datenzentriert und kundenindividuell gestalten wollen. Dabei bleiben auch spezielle Aspekte wie eine visuelle Präsentation von Datenanalyse, der Einfluss der Tonalität einer Website auf die Werbewirksamkeit von Display Advertising und Prinzipien des digitalen Vertrauensaufbaus beim Einsatz von digitalen Kanälen nicht außen vor.Aus dem InhaltStrategischer Einsatz von Daten im Marketing Datenmanagement als Grundlage für MarketingentscheidungenSmarte Insights fürs Marketing (psychografisches Targeting, Programmatic Advertising, Uplift von Werbemaßnahmen, A/B-Testing)Data-driven Marketing in der realen Welt (Geointelligenz Im Outernet, digitale Komponenten bei Messen, Privacy Concerns in the Carsharing Economy)Datenschutz und die ethischen Grenzen der Datennutzung im Data-driven Marketing Mit Beiträgen von Prof. Dr. Silvia Boßow-Thies +++ Prof. Dr. Annette Corves +++ Prof. Dr. Nicole Fabisch +++ Prof. Dr. Lars-Gunnar Frahm +++ Dr. Björn Goerke +++ Prof. Dr. Goetz Greve +++ Prof. Dr. Susanne Hensel-Börner +++ Prof. Dr. Christina Hofmann-Stölting +++ Prof. Dr. Gregor Hopf +++ Luise Jacobs +++ Prof. Dr. Heike Jochims +++ Dr. Gwen Kaufmann +++ Carsten Köster +++ Terence Lutz +++ Prof. Dr. Doreén Pick +++ Dr. Dennis Proppe +++ Mareike Scheibe +++ Prof. Dr. Eva Schön +++ Prof. Dr. Manuel Stegemann +++ Prof. Dr. Thorsten Suwelack +++ Prof. Dr. Kai-Marcus Thäsler +++ Christian Westerkamp +++ Dr. Heike M. Wolters