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GOVERNANCE OF/THROUGH BIG DATA. Volume II by Giorgio Resta,Vincenzo Zeno-Zencovich Pdf
These two volumes collect twenty five articles and papers published within the “Governance of/through Data” research project financed by the Italian Ministry of Universities. The research project, which was promoted by Roma Tre University, as project lead, and saw the participation of professors and reseachers from Bocconi University in Milan; LUMSA University in Rome; Salento University in Lecce and Turin Polytechnic, cover multiple issues which are here presented in five sections: Algorithms and artificial intelligence; Antitrust, artificial intelligence and data; Big Data; Data governance; Data protection and privacy.
GOVERNANCE OF/THROUGH BIG DATA. Volume I by Giorgio Resta,Vincenzo Zeno-Zencovich Pdf
These two volumes collect twenty five articles and papers published within the “Governance of/through Data” research project financed by the Italian Ministry of Universities. The research project, which was promoted by Roma Tre University, as project lead, and saw the participation of professors and reseachers from Bocconi University in Milan; LUMSA University in Rome; Salento University in Lecce and Turin Polytechnic, cover multiple issues which are here presented in five sections: Algorithms and artificial intelligence; Antitrust, artificial intelligence and data; Big Data; Data governance; Data protection and privacy. DOI: 10.13134/979-12-5977-173-5
Author : Peter Ghavami Publisher : Walter de Gruyter GmbH & Co KG Page : 180 pages File Size : 54,5 Mb Release : 2020-11-09 Category : Business & Economics ISBN : 9783110664324
Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.
Data is the new Gold and Analytics is the machinery to mine, mold and mint it. Data analytics has become core to business and decision making. The rapid increase in data volume, velocity and variety, known as big data, offers both opportunities and challenges. While open source solutions to store big data, like Hadoop and NoSQL offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Organizations that are launching big data initiatives face significant challenges for managing this data effectively. In this book, the author has collected best practices from the world's leading organizations who have successfully implemented big data platforms. He offers the latest techniques and methods for managing big data effectively. The book offers numerous policies, strategies and recipes for managing big data. It addresses many issues that are prevalent with data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. Topics that cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and information technology leaders who are implementing big data platforms in their organizations.
Information Governance Principles and Practices for a Big Data Landscape by Chuck Ballard,Cindy Compert,Tom Jesionowski,Ivan Milman,Bill Plants,Barry Rosen,Harald Smith,IBM Redbooks Pdf
This IBM® Redbooks® publication describes how the IBM Big Data Platform provides the integrated capabilities that are required for the adoption of Information Governance in the big data landscape. As organizations embark on new use cases, such as Big Data Exploration, an enhanced 360 view of customers, or Data Warehouse modernization, and absorb ever growing volumes and variety of data with accelerating velocity, the principles and practices of Information Governance become ever more critical to ensure trust in data and help organizations overcome the inherent risks and achieve the wanted value. The introduction of big data changes the information landscape. Data arrives faster than humans can react to it, and issues can quickly escalate into significant events. The variety of data now poses new privacy and security risks. The high volume of information in all places makes it harder to find where these issues, risks, and even useful information to drive new value and revenue are. Information Governance provides an organization with a framework that can align their wanted outcomes with their strategic management principles, the people who can implement those principles, and the architecture and platform that are needed to support the big data use cases. The IBM Big Data Platform, coupled with a framework for Information Governance, provides an approach to build, manage, and gain significant value from the big data landscape.
Data Governance: The Definitive Guide by Evren Eryurek,Uri Gilad,Valliappa Lakshmanan,Anita Kibunguchy-Grant,Jessi Ashdown Pdf
As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness.
Big Data Governance and Perspectives in Knowledge Management by Strydom, Sheryl Kruger,Strydom, Moses Pdf
The world is witnessing the growth of a global movement facilitated by technology and social media. Fueled by information, this movement contains enormous potential to create more accountable, efficient, responsive, and effective governments and businesses, as well as spurring economic growth. Big Data Governance and Perspectives in Knowledge Management is a collection of innovative research on the methods and applications of applying robust processes around data, and aligning organizations and skillsets around those processes. Highlighting a range of topics including data analytics, prediction analysis, and software development, this book is ideally designed for academicians, researchers, information science professionals, software developers, computer engineers, graduate-level computer science students, policymakers, and managers seeking current research on the convergence of big data and information governance as two major trends in information management.
Written by a leading expert in the field, this guide focuses on the convergence of two major trends in information management--big data and information governance--by taking a strategic approach oriented around business cases and industry imperatives. With the advent of new technologies, enterprises are expanding and handling very large volumes of data; this book, nontechnical in nature and geared toward business audiences, encourages the practice of establishing appropriate governance over big data initiatives and addresses how to manage and govern big data, highlighting the relevant processes, procedures, and policies. It teaches readers to understand how big data fits within an overall information governance program; quantify the business value of big data; apply information governance concepts such as stewardship, metadata, and organization structures to big data; appreciate the wide-ranging business benefits for various industries and job functions; sell the value of big data governance to businesses; and establish step-by-step processes to implement big data governance.
A Practical Guide to Analytics for Governments by Marie Lowman Pdf
« Analytics can make government work better—this book shows you how A Practical Guide to Analytics for Governments provides demonstrations of real-world analytics applications for legislators, policy-makers, and support staff at the federal, state, and local levels. Big data and analytics are transforming industries across the board, and government can reap many of those same benefits by applying analytics to processes and programs already in place. From healthcare delivery and child well-being, to crime and program fraud, analytics can—in fact, already does—transform the way government works. This book shows you how analytics can be implemented in your own milieu: What is the downstream impact of new legislation? How can we make programs more efficient? Is it possible to predict policy outcomes without analytics? How do I get started building analytics into my government organization? The answers are all here, with accessible explanations and useful advice from an expert in the field. Analytics allows you to mine your data to create a holistic picture of your constituents; this model helps you tailor programs, fine-tune legislation, and serve the populace more effectively. This book walks you through analytics as applied to government, and shows you how to reap Big data's benefits at whatever level necessary. Learn how analytics is already transforming government service delivery Delve into the digital healthcare revolution Use analytics to improve education, juvenile justice, and other child-focused areas Apply analytics to transportation, criminal justice, fraud, and much more Legislators and policy makers have plenty of great ideas—but how do they put those ideas into play? Analytics can play a crucial role in getting the job done well. A Practical Guide to Analytics for Governments provides advice, perspective, and real-world guidance for public servants everywhere » --
The Politics and Policies of Big Data by Ann Rudinow Sætnan,Ingrid Schneider,Nicola Green Pdf
Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden well-informed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance. An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance.
Digital China: Big Data and Government Managerial Decision by Qing Jiang Pdf
This book is the first practical case study on the application of big data in China's government management scenarios, which is important for comprehensively presenting the achievements of China's e-government and digital construction as well as deeply understanding the implementation of big data strategy in China. The author of this book is one of the earliest practitioners engaged in the study of big data applications, and has personally experienced the development, major events, application cases, and industry changes of big data in China. Cases in this book are all actual projects carried out. The author of this book explains the development history of big data she has personally experienced, presenting in an easy-to-understand way the basic concept and characteristics of big data and practical interpretation, which provides important reference for the practical work of government and enterprise managers. The application ideas of big data in management innovation are proposed, and scenarios are described and discussed in terms of accelerating research on sharing big data in government affairs, breaking barriers, realizing data flow information sharing, creating one-stop services, improving the corresponding policy system for sharing big data in government affairs, building public information platform for e-government, and strengthening network and information infrastructure. Especially for the government personnel in departments, this book will give them a better understanding of the charm and value of big data, intuitively understand the utilization and analysis of big data, carry out effective government management and make correct decisions, so as to improve the data literacy of organizations and individuals, form scientific support for their own government's decision-making and management, thus promote the continued construction of digital government, digital China, and digital economy era based on the application of big data.
Big Data and Analytics Applications in Government by Gregory Richards Pdf
Within this context, big data analytics (BDA) can be an important tool given that many analytic techniques within the big data world have been created specifically to deal with complexity and rapidly changing conditions. The important task for public sector organizations is to liberate analytics from narrow scientific silos and expand it across internally to reap maximum benefit across their portfolios of programs. This book highlights contextual factors important to better situating the use of BDA within government organizations and demonstrates the wide range of applications of different BDA techniques. It emphasizes the importance of leadership and organizational practices that can improve performance. It explains that BDA initiatives should not be bolted on but should be integrated into the organization’s performance management processes. Equally important, the book includes chapters that demonstrate the diversity of factors that need to be managed to launch and sustain BDA initiatives in public sector organizations.
The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy by John MacIntyre,Jinghua Zhao,Xiaomeng Ma Pdf
This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.
Building a data-driven organization (DDO) is an enterprise-wide initiative that may consume and lock up resources for the long term. Understandably, any organization considering such an initiative would insist on a roadmap and business case to be prepared and evaluated prior to approval. This book presents a step-by-step methodology in order to create a roadmap and business case, and provides a narration of the constraints and experiences of managers who have attempted the setting up of DDOs. The emphasis is on the big decisions – the key decisions that influence 90% of business outcomes – starting from decision first and reengineering the data to the decisions process-chain and data governance, so as to ensure the right data are available at the right time, every time. Investing in artificial intelligence and data-driven decision making are now being considered a survival necessity for organizations to stay competitive. While every enterprise aspires to become 100% data-driven and every Chief Information Officer (CIO) has a budget, Gartner estimates over 80% of all analytics projects fail to deliver intended value. Most CIOs think a data-driven organization is a distant dream, especially while they are still struggling to explain the value from analytics. They know a few isolated successes, or a one-time leveraging of big data for decision making does not make an organization data-driven. As of now, there is no precise definition for data-driven organization or what qualifies an organization to call itself data-driven. Given the hype in the market for big data, analytics and AI, every CIO has a budget for analytics, but very little clarity on where to begin or how to choose and prioritize the analytics projects. Most end up investing in a visualization platform like Tableau or QlikView, which in essence is an improved version of their BI dashboard that the organization had invested into not too long ago. The most important stakeholders, the decision-makers, are rarely kept in the loop while choosing analytics projects. This book provides a fail-safe methodology for assured success in deriving intended value from investments into analytics. It is a practitioners’ handbook for creating a step-by-step transformational roadmap prioritizing the big data for the big decisions, the 10% of decisions that influence 90% of business outcomes, and delivering material improvements in the quality of decisions, as well as measurable value from analytics investments. The acid test for a data-driven organization is when all the big decisions, especially top-level strategic decisions, are taken based on data and not on the collective gut feeling of the decision makers in the organization.
Handbook on the Politics and Governance of Big Data and Artificial Intelligence by Andrej Zwitter,Oskar J. Gstrein Pdf
Drawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance. This title contains one or more Open Access chapters.
