Data Governance


Book Description

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




Information Quality and Governance for Business Intelligence


Book Description

Business intelligence initiatives have been dominating the technology priority list of many organizations. However, the lack of effective information quality and governance strategies and policies has been meeting these initiatives with some challenges. Information Quality and Governance for Business Intelligence presents the latest exchange of academic research on all aspects of practicing and managing information using a multidisciplinary approach that examines its quality for organizational growth. This book is an essential reference tool for researchers, practitioners, and university students specializing in business intelligence, information quality, and information systems.




The Data Governance Imperative


Book Description

This practical book covers both strategies and tactics around managing a data governance initiative to help make the most of your data.




Non-Invasive Data Governance


Book Description

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.




The Practitioner's Guide to Data Quality Improvement


Book Description

The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. - Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. - Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. - Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.




Master Data Management


Book Description

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




Enterprise Resource Planning and Business Intelligence Systems for Information Quality


Book Description

This book analyses the role of Enterprise Resource Planning (ERP) and Business Intelligence (BI) systems in improving information quality through an empirical analysis carried out in Italy. The study begins with a detailed examination of ERP features that highlights the advantages and disadvantages of ERP adoption. Critical success factors for ERP implementation and post-implementation are then discussed, along with the capabilities of ERP in driving the alignment between management accounting and financial accounting information.The study goes on to illustrate the features of BI systems and to summarize companies’ needs for BI. Critical success factors for BI implementation are then presented, along with the BI maturity model and lifecycle. The focus of the research entails a detailed empirical analysis in the Italian setting designed to investigate the role played by ERP and BI systems in reducing information overload/underload and improving information quality by influencing the features of information flow. The practical and theoretical implications of the study are discussed and future avenues of research are suggested. This book will be of value for all those who have an interest in the capacities of ERP and BI systems to enhance business information quality.




Applying Business Intelligence Initiatives in Healthcare and Organizational Settings


Book Description

Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings incorporates emerging concepts, methods, models, and relevant applications of business intelligence systems within problem contexts of healthcare and other organizational boundaries. Featuring coverage on a broad range of topics such as rise of embedded analytics, competitive advantage, and strategic capability, this book is ideally designed for business analysts, investors, corporate managers, and entrepreneurs seeking to advance their understanding and practice of business intelligence.




Data Science and Business Intelligence for Corporate Decision-Making


Book Description

About the Book: A comprehensive book plan on "Data Science and Business Intelligence for Corporate Decision-Making" with 15 chapters, each with several sections: Chapter 1: Introduction to Data Science and Business Intelligence Chapter 2: Foundations of Data Science Chapter 3: Business Intelligence Tools and Technologies Chapter 4: Data Visualization for Decision-Making Chapter 5: Machine Learning for Business Intelligence Chapter 6: Big Data Analytics Chapter 7: Data Ethics and Governance Chapter 8: Data-Driven Decision-Making Process Chapter 9: Business Intelligence in Marketing Chapter 10: Financial Analytics and Business Intelligence Chapter 11: Operational Excellence through Data Analytics Chapter 12: Human Resources and People Analytics Chapter 13: Case Studies in Data-Driven Decision-Making Chapter 14: Future Trends in Data Science and Business Intelligence Chapter 15: Implementing Data Science Strategies in Corporations Each chapter dives deep into the concepts, methods, and applications of data science and business intelligence, providing practical insights, real-world examples, and case studies for corporate decision-making processes.




Sustaining Competitive Advantage via Business Intelligence, Knowledge Management, and System Dynamics


Book Description

Volume 22 includes two main chapters in both Part A and B. It appears in two parts because all chapters offer great depth in coverage of core issues senior executives must address for long-term survival of the firm: business intelligence, knowledge management, and understanding of the systems dynamics of interfirm behavior.