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Making Enterprise Information Management (EIM) Work for Business: A Guide to Understanding Information as an Asset provides a comprehensive discussion of EIM. It endeavors to explain information asset management and place it into a pragmatic, focused, and relevant light. The book is organized into two parts. Part 1 provides the material required to sell, understand, and validate the EIM program. It explains concepts such as treating Information, Data, and Content as true assets; information management maturity; and how EIM affects organizations. It also reviews the basic process that builds and maintains an EIM program, including two case studies that provide a birds-eye view of the products of the EIM program. Part 2 deals with the methods and artifacts necessary to maintain EIM and have the business manage information. Along with overviews of Information Asset concepts and the EIM process, it discusses how to initiate an EIM program and the necessary building blocks to manage the changes to managed data and content. - Organizes information modularly, so you can delve directly into the topics that you need to understand - Based in reality with practical case studies and a focus on getting the job done, even when confronted with tight budgets, resistant stakeholders, and security and compliance issues - Includes applicatory templates, examples, and advice for executing every step of an EIM program
Learn how to form and execute an enterprise information strategy: topics include data governance strategy, data architecture strategy, information security strategy, big data strategy, and cloud strategy. Manage information like a pro, to achieve much better financial results for the enterprise, more efficient processes, and multiple advantages over competitors. As you’ll discover in Enterprise Information Management in Practice, EIM deals with both structured data (e.g. sales data and customer data) as well as unstructured data (like customer satisfaction forms, emails, documents, social network sentiments, and so forth). With the deluge of information that enterprises face given their global operations and complex business models, as well as the advent of big data technology, it is not surprising that making sense of the large piles of data is of paramount importance. Enterprises must therefore put much greater emphasis on managing and monetizing both structured and unstructured data. As Saumya Chaki—an information management expert and consultant with IBM—explains in Enterprise Information Management in Practice, it is now more important than ever before to have an enterprise information strategy that covers the entire life cycle of information and its consumption while providing security controls. With Fortune 100 consultant Saumya Chaki as your guide, Enterprise Information Management in Practice covers each of these and the other pillars of EIM in depth, which provide readers with a comprehensive view of the building blocks for EIM. Enterprises today deal with complex business environments where information demands take place in real time, are complex, and often serve as the differentiator among competitors. The effective management of information is thus crucial in managing enterprises. EIM has evolved as a specialized discipline in the business intelligence and enterprise data warehousing space to address the complex needs of information processing and delivery—and to ensure the enterprise is making the most of its information assets.
How an organization manages its information is arguably the most important skill in today’s dynamic and hyper-competitive environment. In Enterprise Information Management, editor Paul Baan and a team of expert contributors present a holistic approach to EIM, with an emphasis on action-oriented decision making. The authors demonstrate that EIM must be promoted from the top down, in order to ensure that the entire organization is committed to establishing and supporting the systems and processes designed to capture, store, analyze, and disseminate information. They identify three key “pillars” of applications: (1) business intelligence (the information and knowledge management process itself); (2) enterprise content management (company-wide management of unstructured information, including document management, digital asset management, records management, and web content management); and (3) enterprise search (using electronic tools to retrieve information from databases, file systems, and legacy systems). The authors explore EIM from economic and socio-psychological perspectives, considering the “ROI” (return on information) of IT and related technological investments, and the cultural and behavioral aspects through which people and machines interact. Illustrating concepts through case examples, the authors provide a variety of tools for managers to assess and improve the effectiveness of their EIM infrastructure, considering its implications for customer and client relations, process and system improvements, product and service innovations, and financial performance.
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
Global security threats have created a complex risk landscape that is challenging and transforming society. These global security issues intersect and influence the political, economic, social, technological, ecological and legal dimensions of the complex risk landscape and are now transborder thereby becoming national security issues. Accessing the innovation space to support safety, security and defence capabilities is critical in order to mitigate new and evolving threats. Through real-world examples of innovation, this book provides a detailed examination of the innovation space as it pertains to the application of S&T to safety and security threats and challenges. This book is of most interest to public and private sector innovators as well as academician and graduate students working in the safety and security domain.
For organizations operating in a modern business environment, adopting the latest information technologies (IT) is of paramount importance. Organizational decision makers are increasingly interested in IT acquisition, constantly seeking the most advanced solutions in order to give their constituents a distinct competitive advantage. Managing Enterprise Information Technology Acquisitions: Assessing Organizational Preparedness provides leaders and innovators with research and strategies to make the most of their options involving IT and organizational management approaches. This book will serve as a critical resource for leaders, managers, strategists, and other industry professionals who must be prepared to meet the constant changes in the field of information technologies in order to effectively guide their organizations and achieve their respective goals.
This report highlights the important role data can play in creating conditions that improve public services, increase the effectiveness of public spending and inform ethical and privacy considerations. It presents a data-driven public sector framework that can help countries or organisations assess the elements needed for using data to make better-informed decisions across public sectors.
​This book undertakes to marry the concepts of "Concept Mapping" with a "Design Thinking" approach in the context of business analysis. While in the past a lot of attention has been paid to the business process side, this book now focusses information quality and valuation, master data and hierarchy management, business rules automation and business semantics as examples for business innovation opportunities. The book shows how to take "Business Concept Maps" further as information models for new IT paradigms. In a way this books redefines and extends business analysis towards solutions that can be described as business synthesis or business development. Business modellers, analysts and controllers, as well as enterprise information architects, will benefit from the intuitive modelling and designing approach presented in this book. The pragmatic and agile methods presented can be directly applied to improve the way organizations manage their business concepts and their relationships. "This book is a great contribution to the information management community. It combines a theoretical foundation with practical methods for dealing with important problems. This is rare and very useful. Conceptual models that communicate business reality effectively require some degree of creative imagination. As such, they combine the results of business analysis with communication design, as is extensively covered in this book." Dr. Malcolm Chisholm, President at AskGet.com Inc. “Truly understanding business requirements has always been a major stumbling block in business intelligence (BI) projects. In this book, Thomas Frisendal introduces a powerful technique—business concept mapping—that creates a virtual mind-meld between business users and business analysts. Frisendal does a wonderful explaining and demonstrating how this tool can improve the outcome of BI and other development projects ." Wayne Eckerson, executive director, BI Leadership Forum
Is your organization rapidly accumulating more information than you know how to manage? This updated edition helps you create an enterprise search solution based on more than just technology. Author Martin White shows you how to plan and implement a managed search environment that meets the needs of your business and your employees. Learn why it's vital to have a dedicated staff manage your search technology and support your users.
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