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Data Ethics: Practical Strategies for Implementing Ethical Information Management and Governance
by Katherine O'Keefe Daragh O BrienData-gathering technology is more sophisticated than ever, as are the ethical standards for using this data. This second edition shows how to navigate this complex environment.Data Ethics provides a practical framework for the implementation of ethical principles into information management systems. It shows how to assess the types of ethical dilemmas organizations might face as they become more data-driven. This fully updated edition includes guidance on sustainability and environmental management and on how ethical frameworks can be standardized across cultures that have conflicting values. There is also discussion of data colonialism, the challenge of ethical trade-offs with ad-tech and analytics such as Covid-19 tracking systems and case studies on Smart Cities and Demings Principles.As the pace of developments in data-processing technology continues to increase, it is vital to capitalize on the opportunities this affords while ensuring that ethical standards and ideals are not compromised. Written by internationally regarded experts in the field, Data Ethics is the essential guide for students and practitioners to optimizing ethical data standards in organizations.
Data Flood: Helping the Navy Address the Rising Tide of Sensor Information
by Bradley Wilson Evan Saltzman Erin-Elizabeth Johnson Shane Tierney Isaac R. PorcheNavy analysts are struggling to keep pace with the growing flood of data collected by intelligence, surveillance, and reconnaissance sensors. This challenge is sure to intensify as the Navy continues to field new and additional sensors. The authors explore options for solving the Navy's "big data" challenge, considering changes across four dimensions: people, tools and technology, data and data architectures, and demand and demand management.
Data Governance Success: Growing and Sustaining Data Governance
by Rupa MahantiWhile good data is an enterprise asset, bad data is an enterprise liability. Data governance enables you to effectively and proactively manage data assets throughout the enterprise by providing guidance in the form of policies, standards, processes and rules and defining roles and responsibilities outlining who will do what, with respect to data. While implementing data governance is not rocket science, it is not a simple exercise. There is a lot confusion around what data governance is, and a lot of challenges in the implementation of data governance. Data governance is not a project or a one-off exercise but a journey that involves a significant amount of effort, time and investment and cultural change and a number of factors to take into consideration to achieve and sustain data governance success. Data Governance Success: Growing and Sustaining Data Governance is the third and final book in the Data Governance series and discusses the following:• Data governance perceptions and challenges • Key considerations when implementing data governance to achieve and sustain success• Strategy and data governance• Different data governance maturity frameworks• Data governance – people and process elements• Data governance metricsThis book shares the combined knowledge related to data and data governance that the author has gained over the years of working in different industrial and research programs and projects associated with data, processes, and technologies and unique perspectives of Thought Leaders and Data Experts through Interviews conducted. This book will be highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge to support and succeed in data governance implementations. This book is technology agnostic and contains a balance of concepts and examples and illustrations making it easy for the readers to understand and relate to their own specific data projects.
Data Governance and Compliance: Evolving to Our Current High Stakes Environment
by Rupa MahantiThis book sets the stage of the evolution of corporate governance, laws and regulations, other forms of governance, and the interaction between data governance and other corporate governance sub-disciplines. Given the continuously evolving and complex regulatory landscape and the growing number of laws and regulations, compliance is a widely discussed issue in the field of data. This book considers the cost of non-compliance bringing in examples from different industries of instances in which companies failed to comply with rules, regulations, and other legal obligations, and goes on to explain how data governance helps in avoiding such pitfalls.The first in a three-volume series on data governance, this book does not assume any prior or specialist knowledge in data governance and will be highly beneficial for IT, management and law students, academics, information management and business professionals, and researchers to enhance their knowledge and get guidance in managing their own data governance projects from a governance and compliance perspective.
Data Governance and Data Management: Contextualizing Data Governance Drivers, Technologies, and Tools
by Rupa MahantiThis book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives.
Data Governance and the Digital Economy in Asia: Harmonising Cross-Border Data Flows (Routledge Studies in the Modern World Economy)
by Paul Cheung Liu Jingting Ulrike SengstschmidData governance is the cornerstone of digital economy growth, particularly in Asia, where both the digital economy and the policy space are fast expanding. The chapters collected in this volume delve into how diverse and rapidly evolving data governance models of ASEAN countries and their Asian partners are shaping the regional digital economy integration, particularly through cross-border data flows.The book begins with an examination of the diffusion of data governance rules globally and their economic impacts on a macro level. It then delves into a regional analysis, emphasising the interplay between data governance and economic development. Key discussions include data policies in India, China, South Korea, and ASEAN countries, enriched with insights from industry leaders. The book evaluates the role of regional and international trade agreements in facilitating digital trade and explores the consequences of widely differing data governance models for the ASEAN regional economy, with a special focus on implications for ASEAN’s Digital Economy Framework Agreement.Written for scholars of digital economy, data governance, and digital trade, this book provides a thorough understanding of Asia’s data regulatory environment. Policymakers and industry professionals will also find the book’s insights into the intricacies of digital economy policies and their implications useful in navigating the future of digital economic integration and growth in the ASEAN region.
Data Governance for Managers: The Driver of Value Stream Optimization and a Pacemaker for Digital Transformation (Management for Professionals)
by Lars Michael BollwegProfessional data management is the foundation for the successful digital transformation of traditional companies. Unfortunately, many companies fail to implement data governance because they do not fully understand the complexity of the challenge (organizational structure, employee empowerment, change management, etc.) and therefore do not include all aspects in the planning and implementation of their data governance. This book explains the driving role that a responsive data organization can play in a company's digital transformation. Using proven process models, the book takes readers from the basics, through planning and implementation, to regular operations and measuring the success of data governance. All the important decision points are highlighted, and the advantages and disadvantages are discussed in order to identify digitization potential, implement it in the company, and develop customized data governance. The book will serve as a useful guide for interested newcomers as well as for experienced managers.
Data Governance für Manager: Datengetriebene Prozess- und Systemoptimierung als Taktgeber der digitalen Transformation
by Lars Michael BollwegDieses Fachbuch führt den Leser in fünf Buchteilen und mit der Hilfe praxiserprobter Vorgehensmodelle von den Grundlagen (Was ist Data Governance?), über die Planung (Welche Gestaltungsoptionen habe ich?) und Implementierung (Wie kann ich Data Governance im Unternehmen einführen?) bis zum Regelbetrieb (Wie kann ich Mehrwerte erzielen?) und der Erfolgsmessung einer Data Governance. Wie jedes Unternehmen ist auch jede Data Governance anders, deshalb werden alle wichtigen Entscheidungspunkte aufgezeigt, die Vor- und Nachteile diskutiert, um dem Leser, die Möglichkeit zu bieten, eine maßgeschneiderte Data Governance zu entwickeln.Ein professionelles Datenmanagement (Data Governance) ist die Grundlage für die erfolgreiche digitale Transformation traditioneller Unternehmen. Leider scheitern eine Vielzahl an Unternehmen an der Einführung einer Data Governance, weil sie die Komplexität der Herausforderung (Organisationsaufbau, Befähigung der Mitarbeiter, Change Management etc.) nicht vollständig überblicken und deshalb nicht alle Aspekte mit in die Planung und Umsetzung ihrer Data Governance miteinbeziehen. Hier setzt dieses Buch an: Es erläutert die treibende Rolle, die eine reaktionsfähige Datenorganisation innerhalb der digitalen Transformation eines Unternehmens einnehmen kann. Der Leser wird befähigt, Digitalisierungspotenziale aufzuzeigen und diese im Unternehmen in die Umsetzung zu überführen.Der InhaltGrundlagen Data GovernanceErfolgsfaktoren der ImplementierungEntwicklung eines reaktionsfähigen Operating Model Data Governance als Treiber der Wertstromoptimierung und Taktgeber der digitalen TransformationErfolgsmessung einer Data Governance
Data Governance: From the Fundamentals to Real Cases
by Mario Piattini Ismael CaballeroThis book presents a set of models, methods, and techniques that allow the successful implementation of data governance (DG) in an organization and reports real experiences of data governance in different public and private sectors. To this end, this book is composed of two parts. Part I on “Data Governance Fundamentals” begins with an introduction to the concept of data governance that stresses that DG is not primarily focused on databases, clouds, or other technologies, but that the DG framework must be understood by business users, systems personnel, and the systems themselves alike. Next, chapter 2 addresses crucial topics for DG, such as the evolution of data management in organizations, data strategy and policies, and defensive and offensive approaches to data strategy. Chapter 3 then details the central role that human resources play in DG, analysing the key responsibilities of the different DG-related roles and boards, while chapter 4 discusses the most common barriers to DG in practice. Chapter 5 summarizes the paradigm shifts in DG from control to value creation. Subsequently chapter 6 explores the needs, characteristics and key functionalities of DG tools, before this part ends with a chapter on maturity models for data governance. Part II on “Data Governance Applied” consists of five chapters which review the situation of DG in different sectors and industries. Details about DG in the banking sector, public administration, insurance companies, healthcare and telecommunications each are presented in one chapter. The book is aimed at academics, researchers and practitioners (especially CIOs, Data Governors, or Data Stewards) involved in DG. It can also serve as a reference for courses on data governance in information systems.
Data Governance: The Definitive Guide
by Valliappa Lakshmanan Evren Eryurek Uri Gilad Anita Kibunguchy-Grant Jessi AshdownAs you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets compliance requirements. Data governance incorporates the ways people, processes, and technology work together to ensure data is trustworthy and can be used effectively. This practical guide shows you how to effectively implement and scale data governance throughout your organization.Chief information, data, and security officers and their teams will learn strategy and tooling to support democratizing data and unlocking its value while enforcing security, privacy, and other governance standards. Through good data governance, you can inspire customer trust, enable your organization to identify business efficiencies, generate more competitive offerings, and improve customer experience. This book shows you how.You'll learn:Data governance strategies addressing people, processes, and toolsBenefits and challenges of a cloud-based data governance approachHow data governance is conducted from ingest to preparation and useHow to handle the ongoing improvement of data qualityChallenges and techniques in governing streaming dataData protection for authentication, security, backup, and monitoringHow to build a data culture in your organization
Data Infrastructure Management: Insights and Strategies
by Greg SchulzThis book looks at various application and data demand drivers, along with data infrastructure options from legacy on premise, public cloud, hybrid, software-defined data center (SDDC), software data infrastructure (SDI), container as well as serverless along with infrastructure as a Service (IaaS), IT as a Service (ITaaS) along with related technology, trends, tools, techniques and strategies. Filled with example scenarios, tips and strategy considerations, the book covers frequently asked questions and answers to aid strategy as well as decision-making.
Data Integration in the Life Sciences: 12th International Conference, DILS 2017, Luxembourg, Luxembourg, November 14-15, 2017, Proceedings (Lecture Notes in Computer Science #10649)
by Marcos Da Silveira Cédric Pruski Reinhard SchneiderThis book constitutes the proceedings of the 12th International Conference on Data Integration in the Life Sciences, DILS 2017, held in Luxembourg, in November 2017. The 5 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 16 submissions. They cover topics such as: life science data modelling; analysing, indexing, and querying life sciences datasets; annotating, matching, and sharing life sciences datasets; privacy and provenance of life sciences datasets.
Data Is Everybody's Business: The Fundamentals of Data Monetization (Management on the Cutting Edge)
by Cynthia M. Beath Barbara H. Wixom Leslie OwensA clear, engaging, evidence-based guide to monetizing data, for everyone from employee to board member.Most organizations view data monetization—converting data into money—too narrowly: as merely selling data sets. But data monetization is a core business activity for both commercial and noncommercial organizations, and, within organizations, it&’s critical to have wide-ranging support for this pursuit. In Data Is Everybody&’s Business, the authors offer a clear and engaging way for people across the entire organization to understand data monetization and make it happen. The authors identify three viable ways to convert data into money—improving work with data, wrapping products with data, and selling information offerings—and explain when to pursue each and how to succeed. Key features of the book:• Grounded in twenty-eight years of academic research, including nine years of research at the MIT Sloan Center for Information Systems Research (MIT CISR)• Definitions of key terms, self-reflection questions, appealing graphics, and easy-to-use frameworks• Rich with detailed case studies• Supplemented by free MIT CISR website resources (cisr.mit.edu)Ideal for organizations engaged in data literacy training, data-driven transformation, or digital transformation, Data Is Everybody&’s Business is the essential guide for helping everybody in the organization—not just the data specialists—understand, get excited about, and participate in data monetization.
Data Lake Development with Big Data
by Pradeep Pasupuleti Beulah Salome PurraExplore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book * Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture * Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability * Packed with industry best practices and use-case scenarios to get you up-and-running Who This Book Is For This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management, information lifecycle management, data governance, data product design, data engineering, and systems architecture. Also required is experience of Big Data technologies such as Hadoop, Spark, Splunk, and Storm. What You Will Learn * Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake * Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios * Find out the key considerations to be taken into account while building each tier of the Data Lake * Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes * Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies * Enable data discovery on the Data Lake to allow users to discover the data * Discover how data is packaged and provisioned for consumption * Comprehend the importance of including data governance disciplines while building a Data Lake In Detail A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. It eliminates the need for up-front modeling and rigid data structures by allowing schema-less writes. Data Lakes make it possible to ask complex far-reaching questions to find out hidden data patterns and relationships. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications such as Spark, Storm, Hive, and so on, to create an environment in which data from different sources can be meaningfully brought together and analyzed. Data Lakes can be viewed as having three capabilities--intake, management, and consumption. This book will take readers through each of these processes of developing a Data Lake and guide them (using best practices) in developing these capabilities. It will also explore often ignored, yet crucial considerations while building Data Lakes, with the focus on how to architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data. You will be able to utilize Data Lakes for efficient and easy data processing and analytics. Style and approach Data Lake Development with Big Data provides architectural approaches to building a Data Lake. It follows a use case-based approach where practical implementation scenarios of each key component are explained. It also helps you understand how these use cases are implemented in a Data Lake. The chapters are organized in a way that mimics the sequential data flow evidenced in a Data Lake.
Data Leadership for Everyone: How You Can Harness the True Power of Data at Work
by Anthony AlgminA revolutionary approach to bringing data and business togetherData is lazy. It sits in files or databases, minding its own business but not accomplishing very much. Data is like someone in their mid-twenties, living with their parents, who won't get off the couch and make something of their life. Data is also the closest thing we have to truth in our organizations—but most business leaders today struggle using data to make an impact on what really matters: the success of their businesses. Data Leadership for Everyone is a game-changing book for anyone who believes in the power of data and is ready to create revolutionary change in their organization. Whether you're a C-suite executive, a manager, or an individual contributor, this book will propel your career by unlocking the mystery of how raw data transforms into real outcomes. In this book, data leadership advocate and transformation coach Anthony J. Algmin reveals his five-step Data Leadership Framework, breaking down the complexity of data systems and empowering you to:Access and prepare data for useRefine data to maximize its potentialUse data to find new insightsImpact business success with data valueGovern and scale data-driven outcomes Data is the key to the future success of all businesses, and anyone not making the most of data will lose, while those who can use data to drive business value will win. It's not enough to learn about data—business success requires a special leadership approach to connect data to the people, processes, and technologies unique to your organization. With over 150 specific takeaways, Data Leadership for Everyone is a must-have business leadership book to help you become a better data leader for the twenty-first century and beyond.
Data Management Technologies and Applications: Third International Conference, DATA 2014, Vienna, Austria, August 29-31, 2014, Revised Selected papers (Communications in Computer and Information Science #178)
by Andreas Holzinger Markus Helfert Orlando Belo Chiara FrancalanciThis book constitutes the thoroughly refereed proceedings of the Fourth International Conference on Data Technologies and Applications, DATA 2015, held in Colmar, France, in July 2015. The 9 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: databases, data warehousing, data mining, data management, data security, knowledge and information systems and technologies; advanced application of data.
Data Management and Analysis: Case Studies in Education, Healthcare and Beyond (Studies in Big Data #65)
by Reda Alhajj Mohammad Moshirpour Behrouz FarData management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
Data Management: Der Weg zum datengetriebenen Unternehmen
by Klaus-Dieter GronwaldDieses Lehrbuch betrachtet Data Management als interdisziplinäres Konzept mit Fokus auf den Zielen datengetriebener Unternehmen. Im Zentrum steht die interaktive Entwicklung eines Unternehmensdatenmodells für ein virtuelles Unternehmen mit Unterstützung eines online Learning Games unter Einbeziehung der Aufgaben, Ziele und Grundsätze des Data Managements, typischer Data-Management-Komponenten und Frameworks wie Datenmodellierung und Design, Metadaten Management, Data Architecture, und Data Governance, und verknüpft diese mit datengetriebenen Anwendungen wie Business Warehousing, Big Data, In-Memory Data Management, und Machine Learning im Data Management Kontext.Das Buch dient als Lehrbuch für Studierende der Informatik, der Wirtschaft und der Wirtschaftsinformatik an Universitäten, Hochschulen und Fachschulen und zur industriellen Aus- und Weiterbildung.
Data Migration Management for SAP S/4HANA: A Practical Guide
by Aleksei ArziaevEnhance your data transfer and storage skills with this comprehensive step-by-step guide to managing data migration for new on-premises SAP S/4HANA implementations. This book is tailored towards small to large projects, with a focus on the managerial aspects of the data migration process rather than the technical details. You’ll follow a project-led approach, enriched with a practical case study, and a comprehensive methodology for data migration planning and documentation. Athen traverse through a detailed plan on managing and documenting data migration throughout the project lifecycle. This book utilizes the general SAP Activate methodology for on-premises solutions as its foundational framework, enhancing it with specific strategies for data migration. Structured in alignment with the project phases of the SAP Activate methodology, Data Migration Management for SAP S/4HANA methodically covers planning, organizing, and controlling the data migration process. It serves as an essential guide for professionals tasked with implementing SAP S/4HANA in their business, ensuring a thorough understanding of each data migration phase on the project. What You'll Learn Significantly decrease the time needed for both the preparation and execution of data migration activities. Foster clear transparency in data migration processes for all stakeholders, including the customer and the project team. Facilitate a seamless and timely data migration process. Establish a benchmark for data migration management in future projects. Address and remedy any deficiencies in the SAP Activate methodology pertaining to data migration. Who This Book Is For SAP projects and data migration workstreams leads, already well-versed in SAP Activate methodology and possessing moderate experience in project and workstream management, who are seeking to enhance their skills in professionally managing data migration in implementation projects.
Data Mining Methods and Applications
by Kenneth D. Lawrence Ronald K. Klimberg Stephan KudybaWith today's information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them. Gain a Competitive Advantage Employ data mining in research and forecasting Build models with data management
Data Mining Mobile Devices
by Jesus MenaWith today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire.Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertainin
Data Mining and Analytics in Healthcare Management: Applications and Tools (International Series in Operations Research & Management Science #341)
by David L. Olson Özgür M. ArazThis book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.
Data Mining and Business Analytics with R
by Johannes LedolterCollecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:* A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools* Illustrations of how to use the outlined concepts in real-world situations* Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials* Numerous exercises to help readers with computing skills and deepen their understanding of the materialData Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
Data Mining and Exploration: From Traditional Statistics to Modern Data Science
by Chong Ho Alex YuThis book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.
Data Mining and Market Intelligence for Optimal Marketing Returns
by Domingo Tavella Susan ChiuThe authors present a practical and highly informative perspective on the elements that are crucial to the success of a marketing campaign. Unlike books that are either too theoretical to be of practical use to practitioners, or too soft to serve as solid and measurable implementation guidelines, this book focuses on the integration of established quantitative techniques into real life case studies that are immediately relevant to marketing practitioners.