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The IBM Data Governance Unified Process by Sunil Soares Pdf
Anyone considering a data governance program within their organization will find an invaluable step-by-step methodology using IBM tools and best practices in this structured how-to. While many in the IT industry hold separate definitions in their minds, this authoritative manual defines data governance as the discipline of treating data as an enterprise asset. The intricate process of data governance involves the exercise of decision rights to optimize, secure, and leverage data. Providing a rigorous explanation of the 14 steps and almost 100 substeps to enact unified data governance, this extensive handbook also shows that the core issues to be tackled are not about technology but rather about people and process.
Written by a leading expert in the field of information governance, this book is the only available that focuses specifically on IBM solutions and the convergence of two major trends in information management today: big data and information governance. Nontechnical in nature and geared toward business audiences responsible for the analytics of big data, the book provides a big data governance framework with three dimensions: big data types, information governance disciplines, and industries and functions. Practitioners can use the knowledge presented in this book to understand big data support across the IBM InfoSphere portfolio, including DataStage, Streams, QualityStage, MDM, Guardium, Optim, and Data Explorer. They will also learn about the integration between IBM Business Process Manager Express and IBM InfoSphere in terms of integrating information governance, big data, and business process 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.
Data governance programs often start off using programs such as Microsoft Excel and Microsoft SharePoint to document and share data governance artifacts. But these tools often lack critical functionality. Meanwhile, vendors have matured their data governance offerings to the extent that today's organizations need to consider tools as a critical component of their data governance programs. In this book, data governance expert Sunil Soares reviews the Enterprise Data Management (EDM) reference architecture and discusses key data governance tasks that can be automated by tools for business glossa.
The Chief Data Officer Handbook for Data Governance by Sunil Soares Pdf
A practical guide for today's chief data officers to define and manage data governance programs The relatively new role of chief data officer (CDO) has been created to address the issue of managing a company's data as a strategic asset, but the problem is that there is no universally accepted "playbook" for this role. Magnifying the challenge is the rapidly increasing volume and complexity of data, as well as regulatory compliance as it relates to data. In this book, Sunil Soares provides a practical guide for today's chief data officers to manage data as an asset while delivering the trusted data required to power business initiatives, from the tactical to the transformative. The guide describes the relationship between the CDO and the data governance team, whose task is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. Soares provides unique insight into the role of the CDO and presents a blueprint for implementing data governance successfully within the context of the position. With practical advice CDOs need, this book helps establish new data governance practices or mature existing practices.
Metadata Management with IBM InfoSphere Information Server by Wei-Dong Zhu,Tuvia Alon,Gregory Arkus,Randy Duran,Marc Haber,Robert Liebke,Frank Morreale Jr.,Itzhak Roth,Alan Sumano,IBM Redbooks Pdf
What do you know about your data? And how do you know what you know about your data? Information governance initiatives address corporate concerns about the quality and reliability of information in planning and decision-making processes. Metadata management refers to the tools, processes, and environment that are provided so that organizations can reliably and easily share, locate, and retrieve information from these systems. Enterprise-wide information integration projects integrate data from these systems to one location to generate required reports and analysis. During this type of implementation process, metadata management must be provided along each step to ensure that the final reports and analysis are from the right data sources, are complete, and have quality. This IBM® Redbooks® publication introduces the information governance initiative and highlights the immediate needs for metadata management. It explains how IBM InfoSphereTM Information Server provides a single unified platform and a collection of product modules and components so that organizations can understand, cleanse, transform, and deliver trustworthy and context-rich information. It describes a typical implementation process. It explains how InfoSphere Information Server provides the functions that are required to implement such a solution and, more importantly, to achieve metadata management. This book is for business leaders and IT architects with an overview of metadata management in information integration solution space. It also provides key technical details that IT professionals can use in a solution planning, design, and implementation process.
IBM Rational Unified Process Reference and Certification Guide by Ahmad K. Shuja,Jochen Krebs Pdf
The Only Official RUP® Certification Prep Guide and Compact RUP Reference The IBM® Rational Unified Process® has become the de facto industry-standard process for large-scale enterprise software development. The IBM Certified Solution Designer - IBM Rational Unified Process V7.0 certification provides a powerful way for solutions developers to demonstrate their proficiency with RUP. The first and only official RUP certification guide, this book fully reflects the latest versions of the Rational Unified Process and of the IBM RUP exam. Authored by two leading RUP implementers, it draws on extensive contributions and careful reviews by the IBM RUP process leader and RUP certification manager. This book covers every facet of RUP usage. It has been carefully organized to help you prepare for your exam quickly and efficiently--and to provide a handy, compact reference you can rely on for years to come. Coverage includes A full section on RUP exam preparation and a 52-question practice exam Core RUP concepts, the new RUP process architecture, and key principles of business-driven development RUP’s architecture-centric approach to iterative development: practical issues and scenarios Patterns for successful RUP project implementation–and “anti-patterns” to avoid The Unified Method Architecture (UMA): basic content and process elements RUP content disciplines, in depth: Business Modeling, Requirements, Analysis and Design, Implementation, Test, Deployment, Project Management, Change and Configuration Management, and Environment Essential RUP work products, roles, and tasks RUP phases, activities, and milestones RUP tailoring and tools for your organization--including introductions to IBM Rational Method Composer (RMC) and MyRUP
Aligning MDM and BPM for Master Data Governance, Stewardship, and Enterprise Processes by Chuck Ballard,Trey Anderson,Dr. Lawrence Dubov,Alex Eastman,Jay Limburn,Umasuthan Ramakrishnan,IBM Redbooks Pdf
An enterprise can gain differentiating value by aligning its master data management (MDM) and business process management (BPM) projects. This way, organizations can optimize their business performance through agile processes that empower decision makers with the trusted, single version of information. Many companies deploy MDM strategies as assurances that enterprise master data can be trusted and used in the business processes. IBM® InfoSphere® Master Data Management creates trusted views of data assets and elevates the effectiveness of an organization's most important business processes and applications. This IBM Redbooks® publication provides an overview of MDM and BPM. It examines how you can align them to enable trusted and accurate information to be used by business processes to optimize business performance and bring more agility to data stewardship. It also provides beginning guidance on these patterns and where cross-training efforts might focus. This book is written for MDM or BPM architects and MDM and BPM architects. By reading this book, MDM or BPM architects can understand how to scope joint projects or to provide reasonable estimates of the effort. BPM developers (or MDM developers with BPM training) can learn how to design and build MDM creation and consumption use cases by using the MDM Toolkit for BPM. They can also learn how to import data governance samples and extend them to enable collaborative stewardship of master data.
Knowledge Management, Innovation and Big Data by Patricia Ordóñez de Pablos,Miltiadis D. Lytras Pdf
The evolution of knowledge management theory and the special emphasis on human and social capital sets new challenges for knowledge-driven and technology-enabled innovation. Emerging technologies including big data and analytics have significant implications for sustainability, policy making, and competitiveness. This edited volume promotes scientific research into the potential contributions knowledge management can make to the new era of innovation and social inclusive economic growth. We are grateful to all the contributors of this edition for their intellectual work. The organization of the relevant debate is aligned around three pillars: SECTION A. DATA, KNOWLEDGE, HUMAN AND SOCIAL CAPITAL FOR INNOVATION We elaborate on the new era of knowledge types and the emerging forms of social capital and their impact on technology-driven innovation. Topics include: · Social Networks · Smart Education · Social Capital · Corporate Innovation · Disruptive Innovation · Knowledge integration · Enhanced Decision-Making. SECTION B. KNOWLEDGE MANAGEMENT & BIG DATA ENABLED INNOVATION In this section, knowledge management and big data applications and systems are presented. Selective topic include: · Crowdsourcing Analysis · Natural Language Processing · Data Governance · Knowledge Extraction · Ontology Design Semantic Modeling SECTION C. SUSTAINABLE DEVELOPMENT In the section, the debate on the impact of knowledge management and big data research to sustainability is promoted with integrative discussion of complementary social and technological factors including: · Big Social Networks on Sustainable Economic Development · Business Intelligence
IBM Information Governance Solutions by Chuck Ballard,John Baldwin,Alex Baryudin,Gary Brunell,Christopher Giardina,Marc Haber,Erik A O'neill,Sandeep Shah,IBM Redbooks Pdf
Managing information within the enterprise has always been a vital and important task to support the day-to-day business operations and to enable analysis of that data for decision making to better manage and grow the business for improved profitability. To do all that, clearly the data must be accurate and organized so it is accessible and understandable to all who need it. That task has grown in importance as the volume of enterprise data has been growing significantly (analyst estimates of 40 - 50% growth per year are not uncommon) over the years. However, most of that data has been what we call "structured" data, which is the type that can fit neatly into rows and columns and be more easily analyzed. Now we are in the era of "big data." This significantly increases the volume of data available, but it is in a form called "unstructured" data. That is, data from sources that are not as easily organized, such as data from emails, spreadsheets, sensors, video, audio, and social media sites. There is valuable information in all that data but it calls for new processes to enable it to be analyzed. All this has brought with it a renewed and critical need to manage and organize that data with clarity of meaning, understandability, and interoperability. That is, you must be able to integrate this data when it is from within an enterprise but also importantly when it is from many different external sources. What is described here has been and is being done to varying extents. It is called "information governance." Governing this information however has proven to be challenging. But without governance, much of the data can be less useful and perhaps even used incorrectly, significantly impacting enterprise decision making. So we must also respect the needs for information security, consistency, and validity or else suffer the potential economic and legal consequences. Implementing sound governance practices needs to be an integral part of the information control in our organizations. This IBM® Redbooks® publication focuses on the building blocks of a solid governance program. It examines some familiar governance initiative scenarios, identifying how they underpin key governance initiatives, such as Master Data Management, Quality Management, Security and Privacy, and Information Lifecycle Management. IBM Information Management and Governance solutions provide a comprehensive suite to help organizations better understand and build their governance solutions. The book also identifies new and innovative approaches that are developed by IBM practice leaders that can help as you implement the foundation capabilities in your organizations.
Implementing an InfoSphere Optim Data Growth Solution by Whei-Jen Chen,David Alley,Barbara Brown,Sunil Dravida,Saunnie Dunne,Tom Forlenza,Pamela S Hoffman,Tejinder S Luthra,Rajat Tiwary,Claudio Zancani,IBM Redbooks Pdf
Today, organizations face tremendous challenges with data explosion and information governance. InfoSphereTM OptimTM solutions solve the data growth problem at the source by managing the enterprise application data. The Optim Data Growth solutions are consistent, scalable solutions that include comprehensive capabilities for managing enterprise application data across applications, databases, operating systems, and hardware platforms. You can align the management of your enterprise application data with your business objectives to improve application service levels, lower costs, and mitigate risk. In this IBM® Redbooks® publication, we describe the IBM InfoSphere Optim Data Growth solutions and a methodology that provides implementation guidance from requirements analysis through deployment and administration planning. We also discuss various implementation topics including system architecture design, sizing, scalability, security, performance, and automation. This book is intended to provide various systems development professionals, Data Solution Architects, Data Administrators, Modelers, Data Analysts, Data Integrators, or anyone who has to analyze or integrate data structures, a broad understanding about IBM InfoSphere Optim Data Growth solutions. By being used in conjunction with the product manuals and online help, this book provides guidance about implementing an optimal solution for managing your enterprise application data.
Beyond Big Data by Martin Oberhofer,Eberhard Hechler,Ivan Milman,Scott Schumacher,Dan Wolfson Pdf
Drive Powerful Business Value by Extending MDM to Social, Mobile, Local, and Transactional Data Enterprises have long relied on Master Data Management (MDM) to improve customer-related processes. But MDM was designed primarily for structured data. Today, crucial information is increasingly captured in unstructured, transactional, and social formats: from tweets and Facebook posts to call center transcripts. Even with tools like Hadoop, extracting usable insight is difficult—often, because it’s so difficult to integrate new and legacy data sources. In Beyond Big Data, five of IBM’s leading data management experts introduce powerful new ways to integrate social, mobile, location, and traditional data. Drawing on pioneering experience with IBM’s enterprise customers, they show how Social MDM can help you deepen relationships, improve prospect targeting, and fully engage customers through mobile channels. Business leaders and practitioners will discover powerful new ways to combine social and master data to improve performance and uncover new opportunities. Architects and other technical leaders will find a complete reference architecture, in-depth coverage of relevant technologies and use cases, and domain-specific best practices for their own projects. Coverage Includes How Social MDM extends fundamental MDM concepts and techniques Architecting Social MDM: components, functions, layers, and interactions Identifying high value relationships: person to product and person to organization Mapping Social MDM architecture to specific products and technologies Using Social MDM to create more compelling customer experiences Accelerating your transition to highly-targeted, contextual marketing Incorporating mobile data to improve employee productivity Avoiding privacy and ethical pitfalls throughout your ecosystem Previewing Semantic MDM and other emerging trends
Hans Jochen Scholl,Olivier Glassey,Marijn Janssen,Bram Klievink,Ida Lindgren,Peter Parycek,Efthimios Tambouris,Maria A. Wimmer,Tomasz Janowski,Delfina Sá Soares
Author : Hans Jochen Scholl,Olivier Glassey,Marijn Janssen,Bram Klievink,Ida Lindgren,Peter Parycek,Efthimios Tambouris,Maria A. Wimmer,Tomasz Janowski,Delfina Sá Soares Publisher : Springer Page : 321 pages File Size : 55,7 Mb Release : 2016-08-13 Category : Computers ISBN : 9783319444215
Electronic Government by Hans Jochen Scholl,Olivier Glassey,Marijn Janssen,Bram Klievink,Ida Lindgren,Peter Parycek,Efthimios Tambouris,Maria A. Wimmer,Tomasz Janowski,Delfina Sá Soares Pdf
This book constitutes the refereed proceedings of the 15th IFIP WG 8.5 International Conference on Electronic Government, EGOV 2016, held in Guimaraes, Portugal, in September 2016, in conjunction with the 8th International Conference on eParticipation, ePart 2016. The 24 revised full papers presented were carefully reviewed and selected from 87 submissions. The papers are clustered under the following topical sections: foundations; benchmarking and evaluation; information integration and governance; services; evaluation and public values; EGOV success and failure; governance; social media; engagement; processes; policy-making; trust, transparency and accountability; open government and big/open data; smart government/governance/cities.
Modern Data Strategy by Mike Fleckenstein,Lorraine Fellows Pdf
This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.
