data governance assessment questions: Non-Invasive Data Governance Robert S. Seiner, 2014-09-01 Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: • Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. • Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. • Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. • A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. • Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve. |
data governance assessment questions: Data Governance Complete Self-Assessment Guide Gerardus Blokdyk, 2017-05-12 Does the Data Governance performance meet the customer's requirements? What type of data governance is right for your organization? What can happen without data governance? Has the Data Governance work been fairly and/or equitably divided and delegated among team members who are qualified and capable to perform the work? Has everyone contributed? Why have a data governance plan? Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role... In EVERY company, organization and department. Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' For more than twenty years, The Art of Service's Self-Assessments empower people who can do just that - whether their title is marketer, entrepreneur, manager, salesperson, consultant, business process manager, executive assistant, IT Manager, CIO etc... - they are the people who rule the future. They are people who watch the process as it happens, and ask the right questions to make the process work better. This book is for managers, advisors, consultants, specialists, professionals and anyone interested in Data Governance assessment. Featuring 582 new and updated case-based questions, divided into seven core areas of process design, this Self-Assessment will help you identify areas in which Data Governance improvements can be made. In using the questions you will be better able to: - diagnose Data Governance projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Data Governance and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Data Governance Index, you will develop a clear picture of which Data Governance areas need attention. Included with your purchase of the book is the Data Governance Self-Assessment downloadable resource, containing all questions and Self-Assessment areas of this book. This enables ease of (re-)use and enables you to import the questions in your preferred management tool. Access instructions can be found in the book. This Self-Assessment has been approved by The Art of Service as part of a lifelong learning and Self-Assessment program and as a component of maintenance of certification. Optional other Self-Assessments are available. For more information, visit http: //theartofservice.com |
data governance assessment questions: Data Governance Complete Self-assessment Guide Gerardus Blokdyk, 2017-04-22 Does the Data Governance performance meet the customer's requirements? What type of data governance is right for your organization? What can happen without data governance? Has the Data Governance work been fairly and/or equitably divided and delegated among team members who are qualified and capable to perform the work? Has everyone contributed? Why have a data governance plan? Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role... In EVERY company, organization and department. Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' For more than twenty years, The Art of Service's Self-Assessments empower people who can do just that - whether their title is marketer, entrepreneur, manager, salesperson, consultant, business process manager, executive assistant, IT Manager, CIO etc... - they are the people who rule the future. They are people who watch the process as it happens, and ask the right questions to make the process work better. This book is for managers, advisors, consultants, specialists, professionals and anyone interested in Data Governance assessment. Featuring 582 new and updated case-based questions, divided into seven core areas of process design, this Self-Assessment will help you identify areas in which Data Governance improvements can be made. In using the questions you will be better able to: - diagnose Data Governance projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Data Governance and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Data Governance Index, you will develop a clear picture of which Data Governance areas need attention. Included with your purchase of the book is the Data Governance Self-Assessment downloadable resource, containing all questions and Self-Assessment areas of this book. This enables ease of (re-)use and enables you to import the questions in your preferred management tool. Access instructions can be found in the book. This Self-Assessment has been approved by The Art of Service as part of a lifelong learning and Self-Assessment program and as a component of maintenance of certification. Optional other Self-Assessments are available. For more information, visit http://theartofservice.com |
data governance assessment questions: A Practitioner's Guide to Data Governance Uma Gupta, San Cannon, 2020-07-08 Data governance looks simple on paper, but in reality it is a complex issue facing organizations. In this practical guide, data experts Uma Gupta and San Cannon look to demystify data governance through pragmatic advice based on real-world experience and cutting-edge academic research. |
data governance assessment questions: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
data governance assessment questions: Data Management Fundamentals (DMF) - CDMP exam preparation Paul Rakké, 1970-01-01 Besides this Data Management Fundamentals (DMF) CDMP exam preparation book, you are advised to obtain the publication the Data Management courseware based on CDMP Fundamentals - Revised edition (ISBN: 9789401811491) for your preparation for your Certified Data Management Professional (CDMP) certification. This CDMP certification based on the DAMA DMBok (Data Management Body of Knowledge) is a globally recognized credential that validates the knowledge and skills required in the field of data management.This exam preparation book is a well-balanced guide to help you pass the CDMP exam and earn your certification. All the knowledge areas as described in the related courseware and/or DAMA-DMBOK (2nd edition) of the well-known study book plus extra topics as described in the book too, will be treated with exam-like questions. The number of questions per topic can differ, depending on the weights as used in the formal exam composition. All the questions are newly defined questions by the author. Separately the correct answers and guiding explanations with references to the DAMA-DMBOK book are provided. Besides the set of questions per topic which consist of a set of 140 questions, also a set of 100 extra questions with the same weights per topic is provided to give you the opportunity to prepare yourself on the exam with this similar exam. So this 240 new questions provided in this book make your road to the CDMP certification complete. |
data governance assessment questions: Data Governance Handbook Wendy S. Batchelder, 2024-05-31 Build an actionable, business value driven case for data governance to obtain executive support and implement with excellence Key Features Develop a solid foundation in data governance and increase your confidence in data solutions Align data governance solutions with measurable business results and apply practical knowledge from real-world projects Learn from a three-time chief data officer who has worked in leading Fortune 500 companies Purchase of the print or Kindle book includes a free PDF eBook Book Description2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it’s their responsibility to navigate this complexity and ensure effective governance, empowering businesses with the right data, at the right time, and with the right controls. If you are a data professional, this book will equip you with valuable guidance to conquer data governance complexities with ease. Written by a three-time chief data officer in global Fortune 500 companies, the Data Governance Handbook is an exhaustive guide to understanding data governance, its key components, and how to successfully position solutions in a way that translates into tangible business outcomes. By the end, you’ll be able to successfully pitch and gain support for your data governance program, demonstrating tangible outcomes that resonate with key stakeholders. What you will learn Comprehend data governance from ideation to delivery and beyond Position data governance to obtain executive buy-in Launch a governance program at scale with a measurable impact Understand real-world use cases to drive swift and effective action Obtain support for data governance-led digital transformation Launch your data governance program with confidence Who this book is for Chief data officers, data governance leaders, data stewards, and engineers who want to understand the business value of their work, and IT professionals seeking further understanding of data management, will find this book useful. You need a basic understanding of working with data, business needs, and how to meet those needs with data solutions. Prior coding experience or skills in selling data solutions to executives are not required. |
data governance assessment questions: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
data governance assessment questions: Data Integrity and Data Governance R D McDowall, 2018-11-06 Data integrity is the hottest topic in the pharmaceutical industry. Global regulatory agencies have issued guidance, after guidance after guidance in the past few years, most of which does not offer practical advice on how to implement policies, procedures and processes to ensure integrity. These guidances state what but not how. Additionally, key stages of analysis that impact data integrity are omitted entirely. The aim of this book is to provide practical and detailed help on how to implement data integrity and data governance for regulated analytical laboratories working in or for the pharmaceutical industry. It provides clarification of the regulatory issues and trends, and gives practical methods for meeting regulatory requirements and guidance. Using a data integrity model as a basis, the principles of data integrity and data governance are expanded into practical steps for regulated laboratories to implement. The author uses case study examples to illustrate his points and provides instructions for applying the principles of data integrity and data governance to individual laboratory needs. This book is a useful reference for analytical chemists and scientists, management and senior management working in regulated laboratories requiring either an understanding about data integrity or help in implementing practical solutions. Consultants will also benefit from the practical guidance provided. |
data governance assessment questions: Data Governance John Ladley, 2019-11-08 Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition |
data governance assessment questions: Data Governance for Managers Lars Michael Bollweg, 2022-05-13 Professional 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 assessment questions: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
data governance assessment questions: Data Governance Dimitrios Sargiotis, |
data governance assessment questions: Governance for Drought Resilience Hans Bressers, Nanny Bressers, Corinne Larrue, 2016-05-11 This book presents the findings of a team of scientists and practitioners who have been working on the project “Benefits of Governance in Drought Adaptation” (in short: the DROP project), which is included in the European Union’s INTERREG IVB NWE programme. The DROP governance team developed a Governance Assessment Tool (GAT), which allows the governance setting of a given region for planning and realizing drought adaptation measures to be assessed. Based on this assessment, recommendations can be developed for regional water authorities concerning how to operate most effectively towards increased drought resilience in this context. The GAT has been applied to six regions in Northwest Europe: Twente and Salland in the Netherlands, Eifel-Ruhr in Germany, Brittany in France, Somerset in the United Kingdom, and Flanders in Belgium. These regions are subject to drought aspects related to nature, agriculture and freshwater. This book will aid regional water authorities and other relevant stakeholders interested in governance assessment, whether that context is about water, more specifically about drought or flooding events, or other environmental issues. Further, the GAT can and has also been applied more broadly to a range of governance contexts for water management and beyond. |
data governance assessment questions: Data Strategy in Colleges and Universities Kristina Powers, 2019-10-16 This valuable resource helps institutional leaders understand and implement a data strategy at their college or university that maximizes benefits to all creators and users of data. Exploring key considerations necessary for coordination of fragmented resources and the development of an effective, cohesive data strategy, this book brings together professionals from different higher education experiences and perspectives, including academic, administration, institutional research, information technology, and student affairs. Focusing on critical elements of data strategy and governance, each chapter in Data Strategy in Colleges and Universities helps higher education leaders address a frustrating problem with much-needed solutions for fostering a collaborative, data-driven strategy. |
data governance assessment questions: The "Orange" Model of Data Management Irina Steenbeek, 2019-10-21 *This book is a brief overview of the model and has only 24 pages.*Almost every data management professional, at some point in their career, has come across the following crucial questions:1. Which industry reference model should I use for the implementation of data managementfunctions?2. What are the key data management capabilities that are feasible and applicable to my company?3. How do I measure the maturity of the data management functions and compare that withthose of my peers in the industry4. What are the critical, logical steps in the implementation of data management?The Orange (meta)model of data management provides a collection of techniques and templates for the practical set up of data management through the design and implementation of the data and information value chain, enabled by a set of data management capabilities.This book is a toolkit for advanced data management professionals and consultants thatare involved in the data management function implementation.This book works together with the earlier published The Data Management Toolkit. The Orange model assists in specifying the feasible scope of data management capabilities, that fits company's business goals and resources. The Data Management Toolkit is a practical implementation guide of the chosen data management capabilities. |
data governance assessment questions: Platform Ecosystems Amrit Tiwana, 2013-11-12 Platform Ecosystems is a hands-on guide that offers a complete roadmap for designing and orchestrating vibrant software platform ecosystems. Unlike software products that are managed, the evolution of ecosystems and their myriad participants must be orchestrated through a thoughtful alignment of architecture and governance. Whether you are an IT professional or a general manager, you will benefit from this book because platform strategy here lies at the intersection of software architecture and business strategy. It offers actionable tools to develop your own platform strategy, backed by original research, tangible metrics, rich data, and cases. You will learn how architectural choices create organically-evolvable, vibrant ecosystems. You will also learn to apply state-of-the-art research in software engineering, strategy, and evolutionary biology to leverage ecosystem dynamics unique to platforms. Read this book to learn how to: Evolve software products and services into vibrant platform ecosystems Orchestrate platform architecture and governance to sustain competitive advantage Govern platform evolution using a powerful 3-dimensional framework If you’re ready to transform platform strategy from newspaper gossip and business school theory to real-world competitive advantage, start right here! Understand how architecture and strategy are inseparably intertwined in platform ecosystems Architect future-proof platforms and apps and amplify these choices through governance Evolve platforms, apps, and entire ecosystems into vibrant successes and spot platform opportunities in almost any—not just IT—industry |
data governance assessment questions: Global Digital Data Governance Carolina Aguerre, Malcolm Campbell-Verduyn, Jan Aart Scholte, 2024-01-30 This book provides a nuanced exploration of contemporary digital data governance, highlighting the importance of cooperation across sectors and disciplines in order to adapt to a rapidly evolving technological landscape. Most of the theory around global digital data governance remains scattered and focused on specific actors, norms, processes, or disciplinary approaches. This book argues for a polycentric approach, allowing readers to consider the issue across multiple disciplines and scales. Polycentrism, this book argues, provides a set of lenses that tie together the variety of actors, issues, and processes intertwined in digital data governance at subnational, national, regional, and global levels. Firstly, this approach uncovers the complex array of power centers and connections in digital data governance. Secondly, polycentric perspectives bridge disciplinary divides, challenging assumptions and drawing together a growing range of insights about the complexities of digital data governance. Bringing together a wide range of case studies, this book draws out key insights and policy recommendations for how digital data governance occurs and how it might occur differently. Written by an international and interdisciplinary team, this book will be of interest to students and scholars in the field of development studies, political science, international relations, global studies, science and technology studies, sociology, and media and communication studies. |
data governance assessment questions: Data-Driven Decision-Making for Business Claus Grand Bang, 2024-08-22 Research shows that companies that employ data-driven decision-making are more productive, have a higher market value, and deliver higher returns for their shareholders. In this book, the reader will discover the history, theory, and practice of data-driven decision-making, learning how organizations and individual managers alike can utilize its methods to avoid cognitive biases and improve confidence in their decisions. It argues that value does not come from data, but from acting on data. Throughout the book, the reader will examine how to convert data to value through data-driven decision-making, as well as how to create a strong foundation for such decision-making within organizations. Covering topics such as strategy, culture, analysis, and ethics, the text uses a collection of diverse and up-to-date case studies to convey insights which can be developed into future action. Simultaneously, the text works to bridge the gap between data specialists and businesspeople. Clear learning outcomes and chapter summaries ensure that key points are highlighted, enabling lecturers to easily align the text to their curriculums. Data-Driven Decision-Making for Business provides important reading for undergraduate and postgraduate students of business and data analytics programs, as well as wider MBA classes. Chapters can also be used on a standalone basis, turning the book into a key reference work for students graduating into practitioners. The book is supported by online resources, including PowerPoint slides for each chapter. |
data governance assessment questions: Indigenous Data Sovereignty and Policy Maggie Walter, Tahu Kukutai, Stephanie Russo Carroll, Desi Rodriguez-Lonebear, 2020-10-29 This book examines how Indigenous Peoples around the world are demanding greater data sovereignty, and challenging the ways in which governments have historically used Indigenous data to develop policies and programs. In the digital age, governments are increasingly dependent on data and data analytics to inform their policies and decision-making. However, Indigenous Peoples have often been the unwilling targets of policy interventions and have had little say over the collection, use and application of data about them, their lands and cultures. At the heart of Indigenous Peoples’ demands for change are the enduring aspirations of self-determination over their institutions, resources, knowledge and information systems. With contributors from Australia, Aotearoa New Zealand, North and South America and Europe, this book offers a rich account of the potential for Indigenous data sovereignty to support human flourishing and to protect against the ever-growing threats of data-related risks and harms. The Open Access version of this book, available at https://www.taylorfrancis.com/books/e/9780429273957, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license |
data governance assessment questions: Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis Osman, Ibrahim H., 2013-08-31 Organizations can use the valuable tool of data envelopment analysis (DEA) to make informed decisions on developing successful strategies, setting specific goals, and identifying underperforming activities to improve the output or outcome of performance measurement. The Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis highlights the advantages of using DEA as a tool to improve business performance and identify sources of inefficiency in public and private organizations. These recently developed theories and applications of DEA will be useful for policymakers, managers, and practitioners in the areas of sustainable development of our society including environment, agriculture, finance, and higher education sectors. |
data governance assessment questions: Data Governance and Strategies Mr.Desidi Narsimha Reddy, 2024-09-05 Mr.Desidi Narsimha Reddy, Data Consultant (Data Governance, Data Analytics: Enterprise Performance Management, AI & ML), Soniks consulting LLC, 101 E Park Blvd Suite 600, Plano, TX 75074, United States. |
data governance assessment questions: Ethical Data and Information Management Katherine O'Keefe, Daragh O Brien, 2018-05-03 Information and how we manage, process and govern it is becoming increasingly important as organizations ride the wave of the big data revolution. Ethical Data and Information Management offers a practical guide for people in organizations who are tasked with implementing information management projects. It sets out, in a clear and structured way, the fundamentals of ethics, and provides practical and pragmatic methods for organizations to embed ethical principles and practices into their management and governance of information. Written by global experts in the field, Ethical Data and Information Management is an important book addressing a topic high on the information management agenda. Key coverage includes how to build ethical checks and balances into data governance decision making; using quality management methods to assess and evaluate the ethical nature of processing during design; change methods to communicate ethical values; how to avoid common problems that affect ethical action; and how to make the business case for ethical behaviours. |
data governance assessment questions: Evaluation of the FAO-EU forest law enforcement, governance and trade programme – Phase III Food and Agriculture Organization of the United Nations, 2022-04-11 The FAO-EU forest law enforcement, governance and trade (FLEGT) programme seeks to reduce and eventually eliminate illegal logging. With the support of its donors, the European Union, the Swedish International Development Cooperation Agency (SIDA) and the Foreign, Commonwealth and Development Office (FCDO), the FAO-EU FLEGT Programme funds projects created by governments, civil society and private sector organizations in Latin America, Africa and Asia to improve forest governance and promote trade in legal timber products on domestic and international markets. The Programme works in support of the European Commission’s Action Plan on FLEGT to promote the legal production and consumption of timber. The evaluation looked at the third phase of the programme, which remained a significant contribution to the goals of the FLEGT Action Plan. The increased capacity of service providers (particularly beginner non-governmental organizations and civil society organizations) and micro, small and medium-sized enterprise associations was considered the most significant change generated by the programme. The promotion of South-South cooperation proved to be an important aspect of capacity enhancement. Thanks to increased capacities, but also multi-stakeholder platforms and improved policy and regulative tools, a positive incipient impact on more inclusive forest governance has been achieved. More information and independent forest monitoring provided an important contribution to improved enabling conditions for legal timber trade and on the information of timber legality, even though the actual market impact is still limited. Recommendations to FAO and its project partners and stakeholders include actions to take away institutional, fiscal, technical and political barriers to scale up results, and actions to strengthen the sustainability of results, gender equity and social inclusion, knowledge management as well as monitoring and evaluation. |
data governance assessment questions: Big Data, Algorithms and Food Safety Salvatore Sapienza, 2022-10-20 This book identifies the principles that should be applied when processing Big Data in the context of food safety risk assessments. Food safety is a critical goal in the protection of individuals’ right to health and the flourishing of the food and feed market. Big Data is fostering new applications capable of enhancing the accuracy of food safety risk assessments. An extraordinary amount of information is analysed to detect the existence or predict the likelihood of future risks, also by means of machine learning algorithms. Big Data and novel analysis techniques are topics of growing interest for food safety agencies, including the European Food Safety Authority (EFSA). This wealth of information brings with it both opportunities and risks concerning the extraction of meaningful inferences from data. However, conflicting interests and tensions among the parties involved are hindering efforts to find shared methods for steering the processing of Big Data in a sound, transparent and trustworthy way. While consumers call for more transparency, food business operators tend to be reluctant to share informational assets. This has resulted in a considerable lack of trust in the EU food safety system. A recent legislative reform, supported by new legal cases, aims to restore confidence in the risk analysis system by reshaping the meaning of data ownership in this domain. While this regulatory approach is being established, breakthrough analytics techniques are encouraging thinking about the next steps in managing food safety data in the age of machine learning. The book focuses on two core topics – data ownership and data governance – by evaluating how the regulatory framework addresses the challenges raised by Big Data and its analysis in an applied, significant, and overlooked domain. To do so, it adopts an interdisciplinary approach that considers both the technological advances and the policy tools adopted in the European Union, while also assuming an ethical perspective when exploring potential solutions. The conclusion puts forward a proposal: an ethical blueprint for identifying the principles – Security, Accountability, Fairness, Explainability, Transparency and Privacy – to be observed when processing Big Data for food safety purposes, including by means of machine learning. Possible implementations are then discussed, also in connection with two recent legislative proposals, namely the Data Governance Act and the Artificial Intelligence Act. |
data governance assessment questions: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
data governance assessment questions: EJISE Volume 15 Issue 1 , |
data governance assessment questions: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
data governance assessment questions: Project Health Assessment Paul S. Royer, PMP, 2014-10-24 Project managers, sponsors, team members, and involved stakeholders know when things aren’t going well. A frequent first indication is a missing or errant process. Project Health Assessment presents an innovative approach for assessing project processes through a set of ten critical success factors based on PMI’s PMBOK® Guide knowledge areas. The findings from such assessments can help project managers reduce project risk, improve stakeholder satisfaction, and increase the likelihood of project success, as demonstrated by 30+ assessments done over 15 years of putting this approach into practice. Project Health Assessment breaks down each PMBOK® Guide knowledge area into its process steps, inputs, and outputs and then creates critical success factor questions that evaluate its effectiveness and potential risk. These questions can be used by project managers to establish sufficient project processes or by external entities to evaluate a project and assess its overall risk The book illustrates critical success factor points through numerous case studies, including a step-by-step example of how to conduct a project health assessment from engagement acquisition through startup, initial assessment, and periodic follow-up assessments. The book provides several downloadable document, spreadsheet, and scheduling templates that practitioners can customize and use in their projects. Using these tools, you can avoid or minimize the cost of failed projects to your organization. |
data governance assessment questions: Digital Supply Chain Leadership David Kurz, Murugan Anandarajan, 2021-02-25 Strong leadership is necessary to drive the transformational change required to build and apply digital capabilities across organizations. Digital transformation in the supply chain is a leadership problem first and foremost. This book draws out some of the key digital business strategies supply chain leaders must become familiar with as they take on the responsibilities of leading transformations within their firms. The central rationale of the book is to establish a clear business case for the performance shifts and opportunities of the Digital Supply Chain. The benefits of a digital supply chain for firms can be summarized as uniquely reducing the amount of trade-off between costs and customer satisfaction. The challenges, complexity, and management involved in transforming to a digital supply chain have slowed many firms in their implementation. The key to unlocking this value and advantage is a new, robust, and digitally aware supply chain leadership mindset. It will provide readers with a practical Digital Supply Chain Leadership Road Map that will accelerate actions in technology, analytics, talent and business models. The road map to digital transformation will step the reader through these critical dimensions and illustrate how they can support their own organizational transformation by developing greater levels of maturity. This book will be most valued by supply chain leaders in medium to large scale organizations, as well as consultants and academics interested in digital business and supply chain transformation. The book will also be valuable for students studying digital transformation, supply chain, and operations. |
data governance assessment questions: The Routledge Handbook of Human Research Ethics and Integrity in Australia Bruce M. Smyth, Michael A. Martin, Mandy Downing, 2024-10-23 The Routledge Handbook of Human Research Ethics and Integrity in Australia highlights why it is important to look at the subject of human research ethics and integrity within the Australian context, and what the Australian perspective can offer to all researchers in the social sciences and humanities globally. Australia has one of the world’s most rigorous ethics governance frameworks. This edited collection comprises 35 chapters, compiled with the aim of presenting human research ethics and integrity in a way that can be readily understood and applied by undergraduate and postgraduate students, early career and seasoned researchers, Human Research Ethics Committee members, and those who work in the administration of human research ethics. Chapters that focus on research ethics with Aboriginal and Torres Strait Islander people are likely to be of great interest to an international audience interested in Indigenous research ethics more broadly. This collection will act as a prism through which ethical ‘first principles’ can be seen afresh from the vista of contemporary Australian research ethics frameworks. The issues raised in this collection are likely to resonate beyond the Australian context and will speak to researchers and educators in a variety of settings who find themselves grappling with thorny ethical issues ranging from the rapid evolution of data security and privacy concerns to research about cultural heritage and ethical approaches to Indigenous cultural and intellectual property. |
data governance assessment questions: Data Governance Success Rupa Mahanti, 2021-12-13 While 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 metrics This 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 assessment questions: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
data governance assessment questions: Cloud Computing – CLOUD 2023 Min Luo, |
data governance assessment questions: PISA, Power, and Policy Heinz-Dieter Meyer, Aaron Benavot, 2013-05-13 Over the past ten years the PISA assessment has risen to strategic prominence in the international education policy discourse. Sponsored, organized and administered by the Organization for Economic Cooperation and Development (OECD), PISA seems well on its way to being institutionalized as the main engine in the global accountability regime. The goal of this book is to problematize this development and PISA as an institution-building force in global education. It scrutinizes the role of PISA in the emerging regime of global educational governance and questions the presumption that the quality of a nation’s school system can be evaluated through a standardized assessment that is insensitive to the world’s vast cultural and institutional diversity. The book raises the question of whether PISA’s dominance in the global educational discourse runs the risk of engendering an unprecedented process of worldwide educational standardization for the sake of hitching schools more tightly to the bandwagon of economic efficiency, while sacrificing their role to prepare students for independent thinking and civic participation. |
data governance assessment questions: Designing Data Governance from the Ground Up Lauren Maffeo, 2023-01-18 Businesses own more data than ever before, but it's of no value if you don't know how to use it. Data governance manages the people, processes, and strategy needed for deploying data projects to production. But doing it well is far from easy: Less than one fourth of business leaders say their organizations are data driven. In Designing Data Governance from the Ground Up, you'll build a cross-functional strategy to create roadmaps and stewardship for data-focused projects, embed data governance into your engineering practice, and put processes in place to monitor data after deployment. In the last decade, the amount of data people produced grew 3,000 percent. Most organizations lack the strategy to clean, collect, organize, and automate data for production-ready projects. Without effective data governance, most businesses will keep failing to gain value from the mountain of data that's available to them. There's a plethora of content intended to help DataOps and DevOps teams reach production, but 90 percent of projects trained with big data fail to reach production because they lack governance. This book shares six steps you can take to build a data governance strategy from scratch. You'll find a data framework, pull together a team of data stewards, build a data governance team, define your roadmap, weave data governance into your development process, and monitor your data in production Whether you're a chief data officer or individual contributor, this book will show you how to manage up, get the buy-in you need to build data governance, find the right colleagues to co-create data governance, and keep them engaged for the long haul. |
data governance assessment questions: Information Security Management Handbook, Volume 7 Richard O'Hanley, James S. Tiller, 2013-08-29 Updated annually, the Information Security Management Handbook, Sixth Edition, Volume 7 is the most comprehensive and up-to-date reference available on information security and assurance. Bringing together the knowledge, skills, techniques, and tools required of IT security professionals, it facilitates the up-to-date understanding required to stay |
data governance assessment questions: Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3 Kohei Arai, |
data governance assessment questions: Big Data Management Peter Ghavami, 2020-11-09 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 governance assessment questions: Data Management courseware based on CDMP Fundamentals Alliance BV And More Group BV, 1970-01-01 Besides the courseware publication (ISBN: 9789401811491), you are advised to obtain the DAMA DMBOK publication (ISBN: 9781634622349). Optionally, you can use the publication Data management: a gentle introduction (ISBN: 9789401805506) as inspiration for examples and quotes about the field of data management. This material is intended to prepare participants for the CDMP exam by DAMA International. The courseware can only be ordered by partners and is based on the current version of the DAMA DMBOK. The material will be updated when new versions of DMBOK are published. DAMA DMBOK is the industry reference for data management. It is published by DAMA International and is currently in its second version. The DMBOK is developed by professionals and can be seen as a collection of best practices. The domain of data management is divided into functional areas which are discussed in terms of definitions (what is it), goals (what are we trying to achieve), steps (what are typical activities), inputs/outputs, and participating roles. Developing and sustaining an effective data management function is far from an easy task. The DMBOK framework is adopted by many organizations as the foundation for their data management function: standardized language and good practices speed up the learning process. After the training, you have an overview of the field of data management, its terminology, and current best practices. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …