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Data Protection and Compliance: Second edition (G - Reference,information And Interdisciplinary Subjects Ser.)
by Ben Johnson Richard Hall Jamie Taylor Simon Davis Niall O'Brien Stewart Room Michelle Maher Adam Panagiotopoulos Shervin Nahid Tughan Thuraisingam James Drury-Smith Mark HendryLarge-scale data loss and data privacy compliance breaches continue to make headline news, highlighting the need for stringent data protection policies, especially when personal or commercially sensitive information is at stake. While regulations and legislation exist to address these issues, how organisations can best tailor their compliance approaches to their own operational circumstances has remained an open question. The focus of this book is on operationalising a truly risk-based approach to data protection and compliance, beyond just emphasis on regulatory frameworks and legalistic compliance.
Data Protection and Compliance: Second edition
by Stewart Room Michelle Maher Niall O'Brien Adam Panagiotopoulos Shervin Nahid Richard Hall Tughan Thuraisingam James Drury-Smith Simon Davis Mark Hendry Jamie Taylor Ben JohnsonLarge-scale data loss and data privacy compliance breaches continue to make headline news, highlighting the need for stringent data protection policies, especially when personal or commercially sensitive information is at stake. While regulations and legislation exist to address these issues, how organisations can best tailor their compliance approaches to their own operational circumstances has remained an open question. The focus of this book is on operationalising a truly risk-based approach to data protection and compliance, beyond just emphasis on regulatory frameworks and legalistic compliance.
Data Protection as a Corporate Social Responsibility
by Paolo Balboni Kate FrancisThis progressive book critically analyses the current state of data protection enforcement and proposes a new auditable framework of practical guidelines to contribute to a more sustainable data-driven future.In outlining the debates relating to current data protection structures, Paolo Balboni and Kate Elizabeth Francis argue that legislation alone cannot sufficiently protect individuals’ fundamental rights and freedoms, and instead consider the pressing need for a more ethical approach to data protection. They present the Maastricht University Data Protection as a Corporate Social Responsibility Framework (UM-DPCSR Framework), outlining not only its features, but also how it can fill the gap left by the inadequacies of a merely legal approach to data protection. Balboni and Francis persuasively call on organisations wishing to contribute positively to society through data processing to adopt this framework and to commit to doing good with data or, at the very least, to avoid harming individuals by processing their data.Data Protection as a Corporate Social Responsibility will be a beneficial read for scholars and students with particular interest in corporate law and governance, human rights, internet and technology law, and privacy. It will also appeal to legal professionals, cybersecurity professionals, and sustainability specialists alike.
Data Protection in a Post-Pandemic Society: Laws, Regulations, Best Practices and Recent Solutions
by Chaminda Hewage Yogachandran Rahulamathavan Deepthi RatnayakeThis book offers the latest research results and predictions in data protection with a special focus on post-pandemic society. This book also includes various case studies and applications on data protection. It includes the Internet of Things (IoT), smart cities, federated learning, Metaverse, cryptography and cybersecurity. Data protection has burst onto the computer security scene due to the increased interest in securing personal data. Data protection is a key aspect of information security where personal and business data need to be protected from unauthorized access and modification. The stolen personal information has been used for many purposes such as ransom, bullying and identity theft. Due to the wider usage of the Internet and social media applications, people make themselves vulnerable by sharing personal data. This book discusses the challenges associated with personal data protection prior, during and post COVID-19 pandemic. Some of these challenges are caused by the technological advancements (e.g. Artificial Intelligence (AI)/Machine Learning (ML) and ChatGPT). In order to preserve the privacy of the data involved, there are novel techniques such as zero knowledge proof, fully homomorphic encryption, multi-party computations are being deployed. The tension between data privacy and data utility drive innovation in this area where numerous start-ups around the world have started receiving funding from government agencies and venture capitalists. This fuels the adoption of privacy-preserving data computation techniques in real application and the field is rapidly evolving. Researchers and students studying/working in data protection and related security fields will find this book useful as a reference.
Data Protection in the Financial Services Industry
by Mandy WebsterPrivacy and data protection are now important issues for companies across the financial services industry. Financial records are amongst the most sensitive for many consumers and the regulator is keen to promote good data handling practices in an industry that is looking towards increased customer profiling, for both risk management and opportunity spotting. Mandy Webster's Data Protection in the Financial Services Industry explains how to manage privacy and data protection issues throughout the customer cycle; from making contact to seeking additional business from current customers. She also looks at the precise role of the Financial Services Authority and its response to compliance or non-compliance. Each of the Eight Principles of the Data Protection Act are reviewed and explained.
Data Protection in the Financial Services Industry
by Mandy WebsterPrivacy and data protection are now important issues for companies across the financial services industry. Financial records are amongst the most sensitive for many consumers and the regulator is keen to promote good data handling practices in an industry that is looking towards increased customer profiling, for both risk management and opportunity spotting. Mandy Webster's Data Protection in the Financial Services Industry explains how to manage privacy and data protection issues throughout the customer cycle; from making contact to seeking additional business from current customers. She also looks at the precise role of the Financial Services Authority and its response to compliance or non-compliance. Each of the Eight Principles of the Data Protection Act are reviewed and explained.
Data Protection Law: A Comparative Analysis of Asia-Pacific and European Approaches
by Robert Walters Leon Trakman Bruno ZellerThis book provides a comparison and practical guide for academics, students, and the business community of the current data protection laws in selected Asia Pacific countries (Australia, India, Indonesia, Japan Malaysia, Singapore, Thailand) and the European Union.The book shows how over the past three decades the range of economic, political, and social activities that have moved to the internet has increased significantly. This technological transformation has resulted in the collection of personal data, its use and storage across international boundaries at a rate that governments have been unable to keep pace. The book highlights challenges and potential solutions related to data protection issues arising from cross-border problems in which personal data is being considered as intellectual property, within transnational contracts and in anti-trust law. The book also discusses the emerging challenges in protecting personal data and promoting cyber security. The book provides a deeper understanding of the legal risks and frameworks associated with data protection law for local, regional and global academics, students, businesses, industries, legal profession and individuals.
Data Protection Officer (Bcs Guides To It Roles Ser.)
by Filip Johnssén Sofia EdvardsenThis book provides a practical guide to the DPO role, encompassing the key activities you’ll need to manage to succeed in the role. Coverage includes data protection fundamentals and processes, understanding risk and relevant standards, frameworks and tools, with DPO tips also embedded throughout the book and case studies included to support practice-based learning.
Data Protection Officer (Bcs Guides To It Roles Ser.)
by Filip Johnssén Sofia EdvardsenThis book provides a practical guide to the DPO role, encompassing the key activities you’ll need to manage to succeed in the role. Coverage includes data protection fundamentals and processes, understanding risk and relevant standards, frameworks and tools, with DPO tips also embedded throughout the book and case studies included to support practice-based learning.
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
by Carlo Batini Monica ScannapiecoPoor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone – researchers, students, or professionals – interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.
Data Quality: The Accuracy Dimension (The Morgan Kaufmann Series in Data Management Systems)
by Jack E. OlsonData Quality: The Accuracy Dimension is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it. Likewise, improving the accuracy of data in information systems is fast becoming a major goal as companies realize how much it affects their bottom line. Data profiling is a new technology that supports and enhances the accuracy of databases throughout major IT shops. Jack Olson explains data profiling and shows how it fits into the larger picture of data quality.* Provides an accessible, enjoyable introduction to the subject of data accuracy, peppered with real-world anecdotes. * Provides a framework for data profiling with a discussion of analytical tools appropriate for assessing data accuracy. * Is written by one of the original developers of data profiling technology. * Is a must-read for any data management staff, IT management staff, and CIOs of companies with data assets.
Data Quality: Empowering Businesses with Analytics and AI
by Prashanth SouthekalDiscover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you’ll learn techniques to define and assess data quality, discover how to ensure that your firm’s data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, including practical design patterns for remediating data quality Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the businessAn essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
Data Quality: Empowering Businesses with Analytics and AI
by Prashanth SouthekalDiscover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you’ll learn techniques to define and assess data quality, discover how to ensure that your firm’s data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, including practical design patterns for remediating data quality Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the businessAn essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
Data Quality (Advances in Database Systems #23)
by Richard Y. Wang Mostapha Ziad Yang W. LeeData Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.
Data Quality and its Impacts on Decision-Making: How Managers can benefit from Good Data (BestMasters)
by Christoph SamitschChristoph Samitsch investigates whether decision-making efficiency is being influenced by the quality of data and information. Results of the research provide evidence that defined data quality dimensions have an effect on decision-making performance as well as the time it takes to make a decision.
Data Quality for Decision Makers: A dialog between a board member and a DQ expert
by Guilherme MorbeyCurrently many companies are confronted with the decision how to deal with the new data quality requirements of the regulatory authorities. Future data quality statements for enterprise key figures and their origins are being demanded. Applying methods of a data quality management system can produce these statements best. Guilherme Morbey explains the introduction of such a system in the form of a dialogue.
Data Quality in Southeast Asia: Analysis of Official Statistics and Their Institutional Framework as a Basis for Capacity Building and Policy Making in the ASEAN
by Manuel StagarsThis book explores the reliability of official statistical data in the ASEAN (the Association of Southeast Asian Nations), and the benefits of a better vocabulary to discuss the quality of publicly available data to address the needs of all users. It introduces a rigorous method to disaggregate and rate data quality into principal factors containing a total of ten dimensions, which serves as the basis for a discussion on the opportunities and challenges for data quality, capacity building programs and data policy in Southeast Asia. Tools to standardize and monitor statistical capacity and data quality are presented, as well as methods and data sources to analyse data quality. The book analyses data quality in Indonesia, Malaysia, Singapore, the Philippines, Thailand, Vietnam, Brunei, Laos, Cambodia, and Myanmar, before concluding with thoughts on Open Data and the ASEAN Economic Community (AEC).
Data Quality Management in the Data Age: Excellence in Data Quality for Enhanced Digital Economic Growth (SpringerBriefs in Service Science)
by Haiyan YuThis book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality. High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services. Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets.
Data Quality Management with Semantic Technologies
by Christian FürberChristian Fürber investigates the useful application of semantic technologies for the area of data quality management. Based on a literature analysis of typical data quality problems and typical activities of data quality management processes, he develops the Semantic Data Quality Management framework as the major contribution of this thesis. The SDQM framework consists of three components that are evaluated in two different use cases. Moreover, this thesis compares the framework to conventional data quality software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work.
Data Science: Konzepte, Erfahrungen, Fallstudien und Praxis
by Detlev Frick Andreas Gadatsch Jens Kaufmann Birgit Lankes Christoph Quix Andreas Schmidt Uwe SchmitzData Science ist in vielen Organisationen angekommen und oft alltägliche Praxis. Dennoch stehen viele Verantwortliche vor der Herausforderung, sich erstmalig mit konkreten Fragestellungen zu beschäftigen oder laufende Projekte weiterzuentwickeln. Die Spannbreite der Methoden, Werkzeuge und Anwendungsmöglichkeiten ist sehr groß und entwickelt sich kontinuierlich weiter. Die Vielzahl an Publikationen zu Data Science ist spezialisiert und behandelt fokussiert Einzelaspekte. Das vorliegende Werk gibt den Leserinnen und Lesern eine umfassende Orientierung zum Status Quo aus der wissenschaftlichen Perspektive und zahlreiche vertiefende Darstellungen praxisrelevanter Aspekte. Die Inhalte bauen auf den wissenschaftlichen CAS-Zertifikatskursen zu Big Data und Data Science der Hochschule Niederrhein in Kooperation mit der Hochschule Bonn-Rhein-Sieg und der FH Dortmund auf. Sie berücksichtigen wissenschaftliche Grundlagen und Vertiefungen, aber auch konkrete Erfahrungen aus Data Science Projekten. Das Buch greift praxisrelevante Fragen auf wissenschaftlichem Niveau aus Sicht der Rollen eines „Data Strategist“, „Data Architect“ und „Data Analyst“ auf und bindet erprobte Praxiserfahrungen u. a. von Seminarteilnehmern mit ein. Das Buch gibt für Interessierte einen Einblick in die aktuell relevante Vielfalt der Aspekte zu Data Science bzw. Big Data und liefert Hinweise für die praxisnahe Umsetzung.
Data Science: Innovative Developments in Data Analysis and Clustering (Studies in Classification, Data Analysis, and Knowledge Organization)
by Francesco Palumbo Angela Montanari Maurizio VichiThis edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015.
Data Science: Create Teams That Ask the Right Questions and Deliver Real Value
by Doug RoseLearn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business.Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story.Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts:You should mine your own company for talent. You can’t change your organization by hiring a few data science superheroes.You should form small, agile-like data teams that focus on delivering valuable insights early and often.You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry.What Your Will Learn:Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization’s dataUnderstand key data science terms and concepts Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering valueUse sprints and storytelling to help your team stay on track and adapt to new knowledgeWho This Book Is ForData science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists.
Data Science: A First Introduction (Chapman & Hall/CRC Data Science Series)
by Tiffany Timbers Trevor Campbell Melissa LeeData Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.
Data Science: A First Introduction (Chapman & Hall/CRC Data Science Series)
by Tiffany Timbers Trevor Campbell Melissa LeeData Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.
Data Science – Analytics and Applications: Proceedings of the 1st International Data Science Conference – iDSC2017
by Peter Haber Thomas Lampoltshammer Manfred MayrThe iDSC Proceedings reports on state-of-the-art results in Data Science research, development and business. Topics and content of the IDSC2017 proceedings are• Reasoning and Predictive Analytics• Data Analytics in Community Networks• Data Analytics through Sentiment Analysis• User/Customer-centric Data Analytics• Data Analytics in Industrial Application ScenariosAdvances in technology and changes in the business and social environment have led to an increasing flood of data, fueling both the need and the desire to generate value from these assets. The emerging field of Data Science is poised to deliver theoretical and practical solutions to the pressing issues of data-driven applications.The 1st International Data Science Conference (iDSC2017 / http://www.idsc.at) organized by Salzburg University of Applied Sciences in cooperation with Information Professionals GmbH, established a new key Data Science event, by providing a forum for the international exchange of Data Science technologies and applications.