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| data management architecture framework: 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 management architecture framework: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data management architecture framework: 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 management architecture framework: The Enterprise Data Model Andy Graham, 2012-05 Wouldn't it be great to understand all the data in your organisation? Just imagine being able to define, agree and manage information concepts that impact on business strategy? Then image that these information concepts can be linked to the physical database attributes that ultimately are used to create them. That's what this book is about. It focuses on the data model as the foundation for achieving this understanding. This book provides a framework for the enterprise data model, the business reasons behind it and the differences between conceptual, logical and physical data models. The question of how, and why, to use a data model artifact as part of the data governance toolkit for the whole enterprise is also addressed. This publication is not an in-depth manual on how to model data for a new database system or your next design project. It instead focuses at a level above these implementation projects and addresses the issues that organisations typical struggling with such as: * How do we provide a framework within which we can manage our data assets? * How do we develop applications that adhere to a set of data standards; without creating a nightmare of administration and governance that is both unwieldy and unusable? * How can we get business value from our enterprise data? Chapter headings are: * Chapter 1 - Introduction * Chapter 2 - Information and Data * Chapter 3 - Pillars of Value * Chapter 4 - An Overview of Data Modelling * Chapter 5 - Data Architecture * Chapter 6 - The Enterprise Data Model * Chapter 7 - Build the Model one Project at a Time * Chapter 8 - Master Data * Chapter 9 - Data Governance * Chapter 10 - The Enterprise Data Framework This 2nd edition revises the original text to add extra details around key areas such as the enterprise data model framework and the pillars of value. It also improves the quality of the original text. |
| data management architecture framework: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| data management architecture framework: Data as a Service Pushpak Sarkar, 2015-07-31 Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions |
| data management architecture framework: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| data management architecture framework: 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 management architecture framework: TOGAF® Version 9.1 The Open Group, 2011-01-01 For trainers free additional material of this book is available. This can be found under the Training Material tab. Log in with your trainer account to access the material. TOGAF is a framework - a detailed method and a set of supporting tools - for developing an enterprise architecture, developed by members of The Open Group Architecture Forum. TOGAF Version 9.1 is a maintenance update to TOGAF 9, addressing comments raised since the introduction of TOGAF 9 in 2009. It retains the major features and structure of TOGAF 9, thereby preserving existing investment in TOGAF, and adds further detail and clarification to what is already proven.It may be used freely by any organization wishing to develop an enterprise architecture for use within that organization (subject to the Conditions of Use). This Book is divided into seven parts: Part I - Introduction This part provides a high-level introduction to the key concepts of enterprise architecture and in particular the TOGAF approach. It contains the definitions of terms used throughout TOGAF and release notes detailing the changes between this version and the previous version of TOGAF. Part II - Architecture Development Method This is the core of TOGAF. It describes the TOGAF Architecture Development Method (ADM) a step-by-step approach to developing an enterprise architecture. Part III - ADM Guidelines & Techniques This part contains a collection of guidelines and techniques available for use in applying TOGAF and the TOGAF ADM. Part IV - Architecture Content Framework This part describes the TOGAF content framework, including a structured metamodel for architectural artifacts, the use of re-usable architecture building blocks, and an overview of typical architecture deliverables. Part V - Enterprise Continuum & Tools This part discusses appropriate taxonomies and tools to categorize and store the outputs of architecture activity within an enterprise. Part VI - TOGAF Reference Models This part provides a selection of architectural reference models, which includes the TOGAF Foundation Architecture, and the Integrated Information Infrastructure Reference Model (III-RM). Part VII Architecture Capability Framework This section looks at roles, Governance, compliance skills and much more practical guidance |
| data management architecture framework: Data Model Patterns: A Metadata Map David C. Hay, 2010-07-20 Data Model Patterns: A Metadata Map not only presents a conceptual model of a metadata repository but also demonstrates a true enterprise data model of the information technology industry itself. It provides a step-by-step description of the model and is organized so that different readers can benefit from different parts. It offers a view of the world being addressed by all the techniques, methods, and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) and presents several concepts that need to be addressed by such tools. This book is pertinent, with companies and government agencies realizing that the data they use represent a significant corporate resource recognize the need to integrate data that has traditionally only been available from disparate sources. An important component of this integration is management of the metadata that describe, catalogue, and provide access to the various forms of underlying business data. The metadata repository is essential to keep track of the various physical components of these systems and their semantics. The book is ideal for data management professionals, data modeling and design professionals, and data warehouse and database repository designers. - A comprehensive work based on the Zachman Framework for information architecture—encompassing the Business Owner's, Architect's, and Designer's views, for all columns (data, activities, locations, people, timing, and motivation) - Provides a step-by-step description of model and is organized so that different readers can benefit from different parts - Provides a view of the world being addressed by all the techniques, methods and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) - Presents many concepts that are not currently being addressed by such tools — and should be |
| data management architecture framework: MASTER DATA MANAGEMENT AND DATA GOVERNANCE, 2/E Alex Berson, Larry Dubov, 2010-12-06 The latest techniques for building a customer-focused enterprise environment The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works. -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc. Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods. Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume. Plan and implement enterprise-scale MDM and Data Governance solutions Develop master data model Identify, match, and link master records for various domains through entity resolution Improve efficiency and maximize integration using SOA and Web services Ensure compliance with local, state, federal, and international regulations Handle security using authentication, authorization, roles, entitlements, and encryption Defend against identity theft, data compromise, spyware attack, and worm infection Synchronize components and test data quality and system performance |
| data management architecture framework: Modern Data Strategy Mike Fleckenstein, Lorraine Fellows, 2018-02-12 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. |
| data management architecture framework: 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 management architecture framework: 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 management architecture framework: Design Patterns for Cloud Native Applications Kasun Indrasiri, Sriskandarajah Suhothayan, 2021-05-17 With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development. You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step. Learn the fundamentals of cloud native applications Explore key cloud native communication, connectivity, and composition patterns Learn decentralized data management techniques Use event-driven architecture to build distributed and scalable cloud native applications Explore the most commonly used patterns for API management and consumption Examine some of the tools and technologies you'll need for building cloud native systems |
| data management architecture framework: Cloud Data Management Liang Zhao, Sherif Sakr, Anna Liu, Athman Bouguettaya, 2016-09-03 In practice, the design and architecture of a cloud varies among cloud providers. We present a generic evaluation framework for the performance, availability and reliability characteristics of various cloud platforms. We describe a generic benchmark architecture for cloud databases, specifically NoSQL database as a service. It measures the performance of replication delay and monetary cost. Service Level Agreements (SLA) represent the contract which captures the agreed upon guarantees between a service provider and its customers. The specifications of existing service level agreements (SLA) for cloud services are not designed to flexibly handle even relatively straightforward performance and technical requirements of consumer applications. We present a novel approach for SLA-based management of cloud-hosted databases from the consumer perspective and an end-to-end framework for consumer-centric SLA management of cloud-hosted databases. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms and requires zero source code changes of the cloud-hosted software applications. |
| data management architecture framework: Enterprise Architecture for Global Companies in a Digital IT Era Yoshimasa Masuda, Murlikrishna Viswanathan, 2019-01-24 This book investigates solutions incorporated by architecture boards in global enterprises to resolve issues and mitigate related architecture risks, while also proposing and implementing an adaptive integrated digital architecture framework (AIDAF) and related models and approaches/platforms, which can be applied in companies to promote IT strategies using cloud/mobile IT/digital IT. The book is divided into three main parts, the first of which (Chapters 1–2) addresses the background and motivation for AIDAF aligned with digital IT strategies. The second part (Chapter 3) provides an overview of strategic enterprise architecture (EA) frameworks for digital IT, elaborates on the essential elements of EA frameworks in the digital IT era, and advocates using AIDAF, models for architecture assessment/risk management, knowledge management on digital platforms. In turn, the third part (Chapters 4–7) demonstrates the application and benefits of AIDAF and related models, as shown in three case studies. “I found this book to be a very nice contribution to the EA community of practice. I can recommend this book as a textbook for digital IT strategists/practitioners, EA practitioners, students in universities and graduate schools.” (From the Foreword by Scott A. Bernard) “In this new age of the digital information society, it is necessary to advocate a new EA framework. This book provides state-of-the art knowledge and practices about EA frameworks beneficial for IT practitioners, IT strategists, CIO, IT architects, and even students. It serves as an introductory textbook for all who drive the information society in this era.”(From the Foreword by Jun Murai) |
| data management architecture framework: The Data Model Resource Book, Volume 1 Len Silverston, 2011-08-08 A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful 1st Edition, while adding a companion volume to take care of more specific requirements of different businesses. This updated volume provides a common set of data models for specific core functions shared by most businesses like human resources management, accounting, and project management. These models are standardized and are easily replicated by developers looking for ways to make corporate database development more efficient and cost effective. This guide is the perfect complement to The Data Model Resource CD-ROM, which is sold separately and provides the powerful design templates discussed in the book in a ready-to-use electronic format. A free demonstration CD-ROM is available with each copy of the print book to allow you to try before you buy the full CD-ROM. |
| data management architecture framework: 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 management architecture framework: Data Architecture: A Primer for the Data Scientist W.H. Inmon, Daniel Linstedt, Mary Levins, 2019-04-30 Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the bigger picture and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. - New case studies include expanded coverage of textual management and analytics - New chapters on visualization and big data - Discussion of new visualizations of the end-state architecture |
| data management architecture framework: SOA Source Book The Open Group, 2020-06-11 Software services are established as a programming concept, but their impact on the overall architecture of enterprise IT and business operations is not well-understood. This has led to problems in deploying SOA, and some disillusionment. The SOA Source Book adds to this a collection of reference material for SOA. It is an invaluable resource for enterprise architects working with SOA.The SOA Source Book will help enterprise architects to use SOA effectively. It explains: What SOA is How to evaluate SOA features in business terms How to model SOA How to use The Open Group Architecture Framework (TOGAF ) for SOA SOA governance This book explains how TOGAF can help to make an Enterprise Architecture. Enterprise Architecture is an approach that can help management to understand this growing complexity. |
| data management architecture framework: High Confidence Software Reuse in Large Systems Hong Mei, 2008-07-29 Software reuse depicts a great vision for the software industry. It has been widely viewed as a promising way to improve both the productivity and quality of software development. However, despite of the successes we have achieved, there are still many issues that have limited the promotion of software reuse in the real world. Therefore, software reuse has remained an important hotspot of research. ICSR is the premier international conference in the field of software reuse. It has been an important venue for presenting advances and improvements within the software reuse domain, and a powerful driving force in promoting the interaction between researchers and practitioners. The theme of ICSR 10 was High Confidence Software Reuse in Large Systems. A high confidence system is one that behaves in a well-understood and predictable fashion. Today’s trends towards widespread use of commercial off-the-shelf (COTS) technology, increased integration, continuous evolution, and larger scale are yielding more complex software systems. So, the problem of how to build high confidence complex systems and how to reuse software with a high level of confidence has become a new attractive topic for research. Furthermore, high-level software asset reuse has been a goal for the last 20–30 years, and it can still be considered an unsolved question. Components-based development, MDA-MDE-MDD, extreme programming, and other techniques or methods are promising approaches to software reuse that still need more research. These proceedings report on the current state of the art in software reuse. |
| data management architecture framework: Software Architecture for Big Data and the Cloud Ivan Mistrik, Rami Bahsoon, Nour Ali, Maritta Heisel, Bruce Maxim, 2017-06-12 Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors. - Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques - Presents case studies involving enterprise, business, and government service deployment of big data applications - Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data |
| data management architecture framework: Business Intelligence Guidebook Rick Sherman, 2014-11-04 Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources. |
| data management architecture framework: Data Lake Architecture Bill Inmon, 2016 Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities |
| data management architecture framework: Data Leadership Anthony J. Algmin, 2020-10-14 Data has never been more important to your success than it is today, yet you are surrounded with data you can't trust, and the overwhelming burden of fixing it. Everyone deserves data that helps-not hurts-their organization. |
| data management architecture framework: Data Architecture Charles Tupper, 2011-05-09 Data Architecture: From Zen to Reality explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data. Using a holistic approach to the field of data architecture, the book describes proven methods and technologies to solve the complex issues dealing with data. It covers the various applied areas of data, including data modelling and data model management, data quality, data governance, enterprise information management, database design, data warehousing, and warehouse design. This text is a core resource for anyone customizing or aligning data management systems, taking the Zen-like idea of data architecture to an attainable reality. The book presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios. It teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions. It includes the detail needed to illustrate how the fundamental principles are used in current business practice. The book is divided into five sections, one of which addresses the software-application development process, defining tools, techniques, and methods that ensure repeatable results. Data Architecture is intended for people in business management involved with corporate data issues and information technology decisions, ranging from data architects to IT consultants, IT auditors, and data administrators. It is also an ideal reference tool for those in a higher-level education process involved in data or information technology management. - Presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios - Teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions - Includes the detail needed to illustrate how the fundamental principles are used in current business practice |
| data management architecture framework: Design Patterns Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995 Software -- Software Engineering. |
| data management architecture framework: Navigating the Labyrinth Laura Sebastian-Coleman, An Executive Guide to Data Management |
| data management architecture framework: Smarter Modeling of IBM InfoSphere Master Data Management Solutions Jan-Bernd Bracht, Joerg Rehr, Markus Siebert, Rouven Thimm, IBM Redbooks, 2012-08-09 This IBM® Redbooks® publication presents a development approach for master data management projects, and in particular, those projects based on IBM InfoSphere® MDM Server. The target audience for this book includes Enterprise Architects, Information, Integration and Solution Architects and Designers, Developers, and Product Managers. Master data management combines a set of processes and tools that defines and manages the non-transactional data entities of an organization. Master data management can provide processes for collecting, consolidating, persisting, and distributing this data throughout an organization. IBM InfoSphere Master Data Management Server creates trusted views of master data that can improve applications and business processes. You can use it to gain control over business information by managing and maintaining a complete and accurate view of master data. You also can use InfoSphere MDM Server to extract maximum value from master data by centralizing multiple data domains. InfoSphere MDM Server provides a comprehensive set of prebuilt business services that support a full range of master data management functionality. |
| data management architecture framework: Cad Frameworks Pieter van der Wolf, 2012-12-06 Since the early 1980s, CAD frameworks have received a great deal of attention, both in the research community and in the commercial arena. It is generally agreed that CAD framework technology promises much: advanced CAD frameworks can turn collections of individual tools into effective and user-friendly design environments. But how can this promise be fulfilled? CAD Frameworks: Principles and Architecture describes the design and construction of CAD frameworks. It presents principles for building integrated design environments and shows how a CAD framework can be based on these principles. It derives the architecture of a CAD framework in a systematic way, using well-defined primitives for representation. This architecture defines how the many different framework sub-topics, ranging from concurrency control to design flow management, relate to each other and come together into an overall system. The origin of this work is the research and development performed in the context of the Nelsis CAD Framework, which has been a working system for well over eight years, gaining functionality while evolving from one release to the next. The principles and concepts presented in this book have been field-tested in the Nelsis CAD Framework. CAD Frameworks: Principles and Architecture is primarily intended for EDA professionals, both in industry and in academia, but is also valuable outside the domain of electronic design. Many of the principles and concepts presented are also applicable to other design-oriented application domains, such as mechanical design or computer-aided software engineering (CASE). It is thus a valuable reference for all those involved in computer-aided design. |
| data management architecture framework: Frontiers in Massive Data Analysis National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on the Analysis of Massive Data, 2013-09-03 Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data. |
| data management architecture framework: Modeling Enterprise Architecture with TOGAF Philippe Desfray, Gilbert Raymond, 2014-08-04 Modeling Enterprise Architecture with TOGAF explains everything you need to know to effectively model enterprise architecture with The Open Group Architecture Framework (TOGAF), the leading EA standard. This solution-focused reference presents key techniques and illustrative examples to help you model enterprise architecture. This book describes the TOGAF standard and its structure, from the architecture transformation method to governance, and presents enterprise architecture modeling practices with plenty of examples of TOGAF deliverables in the context of a case study. Although widespread and growing quickly, enterprise architecture is delicate to manage across all its dimensions. Focusing on the architecture transformation method, TOGAF provides a wide framework, which covers the repository, governance, and a set of recognized best practices. The examples featured in this book were realized using the open source Modelio tool, which includes extensions for TOGAF. - Includes intuitive summaries of the complex TOGAF standard to let you effectively model enterprise architecture - Uses practical examples to illustrate ways to adapt TOGAF to the needs of your enterprise - Provides model examples with Modelio, a free modeling tool, letting you exercise TOGAF modeling immediately using a dedicated tool - Combines existing modeling standards with TOGAF |
| data management architecture framework: 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 management architecture framework: Data Management courseware based on CDMP Fundamentals Raymond Slot, 2021-08-01 Besides the courseware publication (ISBN: 9789401807999), 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 management architecture framework: 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 management architecture framework: Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing Management Association, Information Resources, 2021-01-25 Distributed systems intertwine with our everyday lives. The benefits and current shortcomings of the underpinning technologies are experienced by a wide range of people and their smart devices. With the rise of large-scale IoT and similar distributed systems, cloud bursting technologies, and partial outsourcing solutions, private entities are encouraged to increase their efficiency and offer unparalleled availability and reliability to their users. The Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing is a vital reference source that provides valuable insight into current and emergent research occurring within the field of distributed computing. It also presents architectures and service frameworks to achieve highly integrated distributed systems and solutions to integration and efficient management challenges faced by current and future distributed systems. Highlighting a range of topics such as data sharing, wireless sensor networks, and scalability, this multi-volume book is ideally designed for system administrators, integrators, designers, developers, researchers, academicians, and students. |
| data management architecture framework: Lean Integration John G. Schmidt, David Lyle, 2010-05-18 Use Lean Techniques to Integrate Enterprise Systems Faster, with Far Less Cost and Risk By some estimates, 40 percent of IT budgets are devoted to integration. However, most organizations still attack integration on a project-by-project basis, causing unnecessary expense, waste, risk, and delay. They struggle with integration “hairballs”: complex point-to-point information exchanges that are expensive to maintain, difficult to change, and unpredictable in operation. The solution is Lean Integration. This book demonstrates how to use proven “lean” techniques to take control over the entire integration process. John Schmidt and David Lyle show how to establish “integration factories” that leverage the powerful benefits of repeatability and continuous improvement across every integration project you undertake. Drawing on their immense experience, Schmidt and Lyle bring together best practices; solid management principles; and specific, measurable actions for streamlining integration development and maintenance. Whether you’re an IT manager, project leader, architect, analyst, or developer, this book will help you systematically improve the way you integrate—adding value that is both substantial and sustainable. Coverage includes Treating integration as a business strategy and implementing management disciplines that systematically address its people, process, policy, and technology dimensions Providing maximum business flexibility and supporting rapid change without compromising stability, quality, control, or efficiency Applying improvements incrementally without “Boiling the Ocean” Automating processes so you can deliver IT solutions faster–while avoiding the pitfalls of automation Building in both data and integration quality up front, rather than inspecting quality in later More than a dozen in-depth case studies that show how real organizations are applying Lean Integration practices and the lessons they’ve learned Visit integrationfactory.com for additional resources, including more case studies, best practices, templates, software demos, and reference links, plus a direct connection to lean integration practitioners worldwide. |
| data management architecture framework: Handbook of Research on IT Applications for Strategic Competitive Advantage and Decision Making Idemudia, Efosa Carroll, 2020-06-05 To date, a plethora of companies and organizations are investing vast amounts of money on the latest technologies. Information technology can be used to improve market share, profits, sales, competitive advantage, and customer/employee satisfaction. Unfortunately, the individuals meant to use these technologies are not well equipped on how to effectively and efficiently use these tools for competitive advantage and decision making. The Handbook of Research on IT Applications for Strategic Competitive Advantage and Decision Making is a collection of innovative research relevant to the methodologies, theoretical frameworks, and latest empirical research findings in information technology applications, strategic competitive advantage, and decision making. While highlighting topics including agility, knowledge management, and business intelligence, this book is ideally designed for information technology professionals, academics, researchers, managers, executives, and government officials interested in using information technology for strategic competitive advantage and better decision making. |
| data management architecture framework: Multi-Domain Master Data Management Mark Allen, Dalton Cervo, 2015-03-21 Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. - Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. - Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. - Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data. |
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 enable a …
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 to …
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 …
