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| enterprise data management services: 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 |
| enterprise data management services: 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 |
| enterprise data management services: 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 |
| enterprise data management services: 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. |
| enterprise data management services: Linking Enterprise Data David Wood, 2010-11-10 Enterprise data is growing at a much faster rate than traditional technologies allow. New enterprise architectures combining existing technologies are desperately needed. This book suggests a way forward by applying new techniques of the World Wide Web to enterprise information systems. Linking Enterprise Data is an edited volume contributed by worldwide leaders in Semantic Web and Linked Data research, standards development and adoption. Linking enterprise data is the application of World Wide Web architecture principles to real-world information management issues faced by commercial, not-for-profit and government enterprises. This book is divided into four sections: Benefits of applying Linked Data principles in enterprise settings, enterprise approval and support of Linked Data projects, specific Linked Data techniques and a number of real-world success stories from early enterprise adopters. Linking Enterprise Data targets professionals working as CTOs, CIOs, enterprise architects, project managers and application developers in commercial, not-for-profit and government organizations concerned with scalability, flexibility and robustness of information management systems. Computer science graduate students and researchers focusing on enterprise information integration will also benefit. |
| enterprise data management services: Managing Reference Data in Enterprise Databases Malcolm Chisholm, 2001 This is a great book! I have to admit I wasn't enthusiastic about the idea of a book with such a narrow topic initially, but, frankly, it's the first professional book I've read page to page in one sitting in a long time. It should be of interest to DBAs, data architects and modelers, programmers who have to write database programs, and yes, even managers. This book is a winner. - Karen Watterson, Editor SQL Server Professional Malcolm Chisholm has produced a very readable book. It is well-written and with excellent examples. It will, I am sure, become the Reference Book on Reference Data. - Clive Finkelstein, Father of Information Engineering, Managing Director, Information Engineering Services Pty Ltd Reference data plays a key role in your business databases and must be free from defects of any kind. So why is it so hard to find information on this critical topic? Recognizing the dangers of taking reference data for granted, Managing Reference Data in Enterprise Databases gives you precisely what you've been seeking: A complete guide to the implementation and management of reference data of all kinds. This book begins with a thorough definition of reference data, then proceeds with a detailed examination of all reference data issues, fully describing uses, common difficulties, and practical solutions. Whether you're a database manager, architect, administrator, programmer, or analyst, be sure to keep this easy-to-use reference close at hand. Features Solves special challenges associated with maintaining reference data. Addresses a wide range of reference data issues, including acronyms, redundancy, mapping, life cycles, multiple languages, and querying. Describes how reference data interacts with other system components, what problems can arise, and how to mitigate these problems. Offers examples of standard reference data types and matrices for evaluating management methods. Provides a number of standard reference data tables and more specialized material to help you deal with reference data, via a companion Web site |
| enterprise data management services: Enterprise Data Governance Pierre Bonnet, 2013-03-04 In an increasingly digital economy, mastering the quality of data is an increasingly vital yet still, in most organizations, a considerable task. The necessity of better governance and reinforcement of international rules and regulatory or oversight structures (Sarbanes Oxley, Basel II, Solvency II, IAS-IFRS, etc.) imposes on enterprises the need for greater transparency and better traceability of their data. All the stakeholders in a company have a role to play and great benefit to derive from the overall goals here, but will invariably turn towards their IT department in search of the answers. However, the majority of IT systems that have been developed within businesses are overly complex, badly adapted, and in many cases obsolete; these systems have often become a source of data or process fragility for the business. It is in this context that the management of ‘reference and master data’ or Master Data Management (MDM) and semantic modeling can intervene in order to straighten out the management of data in a forward-looking and sustainable manner. This book shows how company executives and IT managers can take these new challenges, as well as the advantages of using reference and master data management, into account in answering questions such as: Which data governance functions are available? How can IT be better aligned with business regulations? What is the return on investment? How can we assess intangible IT assets and data? What are the principles of semantic modeling? What is the MDM technical architecture? In these ways they will be better able to deliver on their responsibilities to their organizations, and position them for growth and robust data management and integrity in the future. |
| enterprise data management services: Universal Meta Data Models David Marco, Michael Jennings, 2004-03-25 * The heart of the book provides the complete set of models that will support most of an organization's core business functions, including universal meta models for enterprise-wide systems, business meta data and data stewardship, portfolio management, business rules, and XML, messaging, and transactions * Developers can directly adapt these models to their own businesses, saving countless hours of development time * Building effective meta data repositories is complicated and time-consuming, and few IT departments have the necessary expertise to do it right-which is why this book is sure to find a ready audience * Begins with a quick overview of the Meta Data Repository Environment and the business uses of meta data, then goes on to describe the technical architecture followed by the detailed models |
| enterprise data management services: Enterprise Knowledge Management David Loshin, 2001 This volume presents a methodology for defining, measuring and improving data quality. It lays out an economic framework for understanding the value of data quality, then outlines data quality rules and domain- and mapping-based approaches to consolidating enterprise knowledge. |
| enterprise data management services: 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 |
| enterprise data management services: Enterprise Data at Huawei Yun Ma, Hao Du, 2021-11-22 This book systematically introduces the data governance and digital transformation at Huawei, from the perspectives of technology, process, management, and so on. Huawei is a large global enterprise engaging in multiple types of business in over 170 countries and regions. Its differentiated operation is supported by an enterprise data foundation and corresponding data governance methods. With valuable experience, methodology, standards, solutions, and case studies on data governance and digital transformation, enterprise data at Huawei is ideal for readers to learn and apply, as well as to get an idea of the digital transformation journey at Huawei. This book is organized into four parts and ten chapters. Based on the understanding of “the cognitive world of machines,” the book proposes the prospects for the future of data governance, as well as the imaginations about AI-based governance, data sovereignty, and building a data ecosystem. |
| enterprise data management services: Master Data Management and Customer Data Integration for a Global Enterprise Alex Berson, Larry Dubov, 2007-05-22 Transform your business into a customer-centric enterprise Gain a complete and timely understanding of your customers using MDM-CDI and the real-world information contained in this comprehensive volume. Master Data Management and Customer Data Integration for a Global Enterprise explains how to grow revenue, reduce administrative costs, and improve client retention by adopting a customer-focused business framework. Learn to build and use customer hubs and associated technologies, secure and protect confidential corporate and customer information, provide personalized services, and set up an effective data governance team. You'll also get full details on regulatory compliance and the latest pre-packaged MDM-CDI software solutions. Design and implement a dynamic MDM-CDI architecture that fits the needs of your business Implement MDM-CDI holistically as an integrated multi-disciplinary set of technologies, services, and processes Improve solution agility and flexibility using SOA and Web services Recognize customers and their relationships with the enterprise across channels and lines of business Ensure compliance with local, state, federal, and international regulations Deploy network, perimeter, platform, application, data, and user-level security Protect against identity and data theft, worm infection, and phishing and pharming scams Create an Enterprise Information Governance Group Perform development, QA, and business acceptance testing and data verification |
| enterprise data management services: Data Strategy Sid Adelman, Larissa Terpeluk Moss, Majid Abai, 2005 Without a data strategy, the people within an organization have no guidelines for making decisions that are absolutely crucial to the success of the IT organization and to the entire organization. The absence of a strategy gives a blank check to those who want to pursue their own agendas, including those who want to try new database management systems, new technologies (often unproven), and new tools. This type of environment provides no hope for success. Data Strategy should result in the development of systems with less risk, higher quality systems, and reusability of assets. This is key to keeping cost and maintenance down, thus running lean and mean. Data Strategy provides a CIO with a rationale to counter arguments for immature technology and data strategies that are inconsistent with existing strategies. This book uses case studies and best practices to give the reader the tools they need to create the best strategy for the organization. |
| enterprise data management services: 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. |
| enterprise data management services: Building the Customer-Centric Enterprise Claudia Imhoff, Lisa Loftis, Jonathan G. Geiger, 2001-02-19 Strategies for leveraging information technologies to improve customer relationships With E-business comes the opportunity for companies to really get to know their customers--who they are and their buying patterns. Business managers need an integrated strategy that supports customers from the moment they enter the front door--or Web site--right through to fulfillment, support, and promotion of new products and services. Along the way, IT managers need an integrated set of technologies--from Web sites to databases and data mining tools--to make all of this work. This book shows both IT and business managers how to match business strategies to the technologies needed to make them work. Claudia Imhoff helped pioneer this set of technologies, called the Corporate Information Factory (CIF). She and her coauthors take readers step-by-step through the process of using the CIF for creating a customer-focused enterprise in which the end results are increased market share and improved customer satisfaction and retention. They show how the CIF can be used to ensure accuracy, identify customer needs, tailor promotions, and more. |
| enterprise data management services: Enterprise Analytics Thomas H. Davenport, 2013 International Institute for Analytics--Dust jacket. |
| enterprise data management services: 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 |
| enterprise data management services: 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. |
| enterprise data management services: Data Management and Governance Services Tejasvi Addagada, 2017-06-22 Organizations across industries are embracing data management and governance practices, primarily driven by regulation and service excellence. While it is equally important to set up a data office, it is also crucial to ensure sustainability of the function. Also, data governance is a pervasive enabler that supports a firm's corporate governance principles. The book highlights how an Enterprise can: -Overcome challenges in data offices today -Analyze existing data management strategy and capabilities to traverse maturity -Set up metadata and data quality management as services and successfully operationalize them -Formalize governance as a function through an operating model, based on its enabling culture -Define a benefits realization model to assess and monitor the value of managing and governing data |
| enterprise data management services: The Enterprise Big Data Lake Alex Gorelik, 2019-02-21 The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries |
| enterprise data management services: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| enterprise data management services: Semantics Empowered Web 3.0 Amit Sheth, Krishnaprasad Thirunarayan, 2022-05-31 After the traditional document-centric Web 1.0 and user-generated content focused Web 2.0, Web 3.0 has become a repository of an ever growing variety of Web resources that include data and services associated with enterprises, social networks, sensors, cloud, as well as mobile and other devices that constitute the Internet of Things. These pose unprecedented challenges in terms of heterogeneity (variety), scale (volume), and continuous changes (velocity), as well as present corresponding opportunities if they can be exploited. Just as semantics has played a critical role in dealing with data heterogeneity in the past to provide interoperability and integration, it is playing an even more critical role in dealing with the challenges and helping users and applications exploit all forms of Web 3.0 data. This book presents a unified approach to harness and exploit all forms of contemporary Web resources using the core principles of ability to associate meaning with data through conceptual or domain models and semantic descriptions including annotations, and through advanced semantic techniques for search, integration, and analysis. It discusses the use of Semantic Web standards and techniques when appropriate, but also advocates the use of lighter weight, easier to use, and more scalable options when they are more suitable. The authors' extensive experience spanning research and prototypes to development of operational applications and commercial technologies and products guide the treatment of the material. Table of Contents: Role of Semantics and Metadata / Types and Models of Semantics / Annotation -- Adding Semantics to Data / Semantics for Enterprise Data / Semantics for Services / Semantics for Sensor Data / Semantics for Social Data / Semantics for Cloud Computing / Semantics for Advanced Applications |
| enterprise data management services: Enterprise API Management Luis Weir, 2019-07-23 A strategy and implementation guide for building, deploying, and managing APIs Key FeaturesComprehensive, end-to-end guide to business-driven enterprise APIsDistills years of experience with API and microservice strategiesProvides detailed guidance on implementing API-led architectures in any businessBook Description APIs are the cornerstone of modern, agile enterprise systems. They enable access to enterprise services from a wide variety of devices, act as a platform for innovation, and open completely new revenue streams. Enterprise API Management shows how to define the right architecture, implement the right patterns, and define the right organization model for business-driven APIs. Drawing on his experience of developing API and microservice strategies for some of the world's largest companies, Luis Weir explains how APIs deliver value across an enterprise. The book explores the architectural decisions, implementation patterns, and management practices for successful enterprise APIs, as well as providing clear, actionable advice on choosing and executing the right API strategy in your enterprise. With a relentless focus on creating business value, Luis Weir reveals an effective method for planning, building, and running business products and services with APIs. What you will learnCreate API strategies to deliver business valueMonetize APIs, promoting them through public marketplaces and directoriesDevelop API-led architectures, applying best practice architecture patternsChoose between REST, GraphQL, and gRPC-style API architecturesManage APIs and microservices through the complete life cycleDeploy APIs and business products, as well as Target Operating ModelsLead product-based organizations to embrace DevOps and focus on delivering business capabilitiesWho this book is for Architects, developers, and technology executives who want to deliver successful API strategies that bring business value. |
| enterprise data management services: Data Warehousing in the Age of Big Data Krish Krishnan, 2013-05-02 Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. - Learn how to leverage Big Data by effectively integrating it into your data warehouse. - Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies - Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements |
| enterprise data management services: Enterprise Information Systems: Concepts, Methodologies, Tools and Applications Management Association, Information Resources, 2010-09-30 This three-volume collection, titled Enterprise Information Systems: Concepts, Methodologies, Tools and Applications, provides a complete assessment of the latest developments in enterprise information systems research, including development, design, and emerging methodologies. Experts in the field cover all aspects of enterprise resource planning (ERP), e-commerce, and organizational, social and technological implications of enterprise information systems. |
| enterprise data management services: Organizational Data Mining Hamid R. Nemati, Christopher D. Barko, 2004-01-01 Mountains of business data are piling up in organizations every day. These organizations collect data from multiple sources, both internal and external. These sources include legacy systems, customer relationship management and enterprise resource planning applications, online and e-commerce systems, government organizations and business suppliers and partners. A recent study from the University of California at Berkeley found the amount of data organizations collect and store in enterprise databases doubles every year, and slightly more than half of this data will consist of reference information, which is the kind of information strategic business applications and decision support systems demand (Kestelyn, 2002). Terabyte-sized (1,000 megabytes) databases are commonplace in organizations today, and this enormous growth will make petabyte-sized databases (1,000 terabytes) a reality within the next few years (Whiting, 2002). By 2004 the Gartner Group estimates worldwide data volumes will be 30 times those of 1999, which translates into more data having been produced in the last 30 years than during the previous 5,000 (Wurman, 1989). |
| enterprise data management services: Enterprise Performance Management Done Right Ron Dimon, 2013-03-06 A workable blueprint for developing and implementing performance management in order to improve revenue growth and profit margins Enterprise performance management (EPM) technology has been rapidly advancing, especially in the areas of predictive analysis and cloud-based solutions. Real Enterprise Performance Management introduces a framework for implementing and managing next-generation functionality for better insight, focus, and alignment of EPM. This blueprint shows that EPM can have a direct positive impact on revenue growth, operating margin, asset utilization, and cash cycle efficiency. Introduces a framework for implementing and managing next-generation functionality for better insight, focus, and alignment Reveals that EPM can have a strong impact on revenue growth, operating margin, asset utilization, cash cycle efficiency Today's businesses have a great deal of data and technology, but less-than-fact decisions are still made. Executives need a structured framework for gathering, analyzing, and debating the best ways to deploy capital, people and time. Real Enterprise Performance Management joins IT and finance in a digestible blueprint for developing and implementing performance management in order to improve revenue growth and profit margins. |
| enterprise data management services: Management Information Systems for Enterprise Applications: Business Issues, Research and Solutions Koumpis, Adamantios, 2012-02-29 This book provides the conceptual and methodological foundations that reflect interdisciplinary concerns regarding research in management information systems, investigating the future of management information systems by means of analyzing a variety of MIS and service-related concepts in a wide range of disciplines--Provided by publisher. |
| enterprise data management services: Big Data Imperatives Soumendra Mohanty, Madhu Jagadeesh, Harsha Srivatsa, 2013-08-23 Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data. |
| enterprise data management services: 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 |
| enterprise data management services: Master Data Management in Practice Dalton Cervo, Mark Allen, 2011-05-25 In this book, authors Dalton Cervo and Mark Allen show you how to implement Master Data Management (MDM) within your business model to create a more quality controlled approach. Focusing on techniques that can improve data quality management, lower data maintenance costs, reduce corporate and compliance risks, and drive increased efficiency in customer data management practices, the book will guide you in successfully managing and maintaining your customer master data. You'll find the expert guidance you need, complete with tables, graphs, and charts, in planning, implementing, and managing MDM. |
| enterprise data management services: In-Memory Data Management Hasso Plattner, Alexander Zeier, 2012-04-17 In the last fifty years the world has been completely transformed through the use of IT. We have now reached a new inflection point. This book presents, for the first time, how in-memory data management is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. This book provides the technical foundation for processing combined transactional and analytical operations in the same database. In the year since we published the first edition of this book, the performance gains enabled by the use of in-memory technology in enterprise applications has truly marked an inflection point in the market. The new content in this second edition focuses on the development of these in-memory enterprise applications, showing how they leverage the capabilities of in-memory technology. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes. |
| enterprise data management services: Enterprise Cloud Strategy Barry Briggs, Eduardo Kassner, 2016-01-07 How do you start? How should you build a plan for cloud migration for your entire portfolio? How will your organization be affected by these changes? This book, based on real-world cloud experiences by enterprise IT teams, seeks to provide the answers to these questions. Here, you’ll see what makes the cloud so compelling to enterprises; with which applications you should start your cloud journey; how your organization will change, and how skill sets will evolve; how to measure progress; how to think about security, compliance, and business buy-in; and how to exploit the ever-growing feature set that the cloud offers to gain strategic and competitive advantage. |
| enterprise data management services: Transforming Enterprise Cloud Services William Y Chang, Hosame Abu-Amara, Jessica Feng Sanford, 2010-11-15 The broad scope of Cloud Computing is creating a technology, business, sociolo- cal, and economic renaissance. It delivers the promise of making services available quickly with rather little effort. Cloud Computing allows almost anyone, anywhere, at anytime to interact with these service offerings. Cloud Computing creates a unique opportunity for its users that allows anyone with an idea to have a chance to deliver it to a mass market base. As Cloud Computing continues to evolve and penetrate different industries, it is inevitable that the scope and definition of Cloud Computing becomes very subjective, based on providers’ and customers’ persp- tive of applications. For instance, Information Technology (IT) professionals p- ceive a Cloud as an unlimited, on-demand, flexible computing fabric that is always available to support their needs. Cloud users experience Cloud services as virtual, off-premise applications provided by Cloud service providers. To an end user, a p- vider offering a set of services or applications in the Cloud can manage these off- ings remotely. Despite these discrepancies, there is a general consensus that Cloud Computing includes technology that uses the Internet and collaborated servers to integrate data, applications, and computing resources. With proper Cloud access, such technology allows consumers and businesses to access their personal files on any computer without having to install special tools. Cloud Computing facilitates efficient operations and management of comp- ing technologies by federating storage, memory, processing, and bandwidth. |
| enterprise data management services: Collaborative Enterprise Architecture Stefan Bente, Uwe Bombosch, Shailendra Langade, 2012-08-29 Why collaborative enterprise architecture? -- What is enterprise architecture -- What enterprise architects do: core activities of EA -- EA frameworks -- EA maturity models -- Foundations of collaborative EA -- Towards pragmatism: lean and agile EA -- Inviting to participation: eam 2.0 -- The next steps: taking collaborative EA forward. |
| enterprise data management services: Ahead in the Cloud Stephen Orban, 2018-03-27 Cloud computing is the most significant technology development of our lifetimes. It has made countless new businesses possible and presents a massive opportunity for large enterprises to innovate like startups and retire decades of technical debt. But making the most of the cloud requires much more from enterprises than just a technology change. Stephen Orban led Dow Jones's journey toward digital agility as their CIO and now leads AWS's Enterprise Strategy function, where he helps leaders from the largest companies in the world transform their businesses. As he demonstrates in this book, enterprises must re-train their people, evolve their processes, and transform their cultures as they move to the cloud. By bringing together his experiences and those of a number of business leaders, Orban shines a light on what works, what doesn't, and how enterprises can transform themselves using the cloud. |
| enterprise data management services: NoSQL Distilled Pramod J. Sadalage, Martin Fowler, 2013 'NoSQL Distilled' is designed to provide you with enough background on how NoSQL databases work, so that you can choose the right data store without having to trawl the whole web to do it. It won't answer your questions definitively, but it should narrow down the range of options you have to consider. |
| enterprise data management services: Aligning MDM and BPM for Master Data Governance, Stewardship, and Enterprise Processes Chuck Ballard, Trey Anderson, Dr. Lawrence Dubov, Alex Eastman, Jay Limburn, Umasuthan Ramakrishnan, IBM Redbooks, 2013-03-08 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. |
| enterprise data management services: Enterprise Business Intelligence and Data Warehousing Alan Simon, 2014-11-24 Corporations and governmental agencies of all sizes are embracing a new generation of enterprise-scale business intelligence (BI) and data warehousing (DW), and very often appoint a single senior-level individual to serve as the Enterprise BI/DW Program Manager. This book is the essential guide to the incremental and iterative build-out of a successful enterprise-scale BI/DW program comprised of multiple underlying projects, and what the Enterprise Program Manager must successfully accomplish to orchestrate the many moving parts in the quest for true enterprise-scale business intelligence and data warehousing. Author Alan Simon has served as an enterprise business intelligence and data warehousing program management advisor to many of his clients, and spent an entire year with a single client as the adjunct consulting director for a $10 million enterprise data warehousing (EDW) initiative. He brings a wealth of knowledge about best practices, risk management, organizational culture alignment, and other Critical Success Factors (CSFs) to the discipline of enterprise-scale business intelligence and data warehousing. |
| enterprise data management services: The Data and Analytics Playbook Lowell Fryman, Gregory Lampshire, Dan Meers, 2016-08-12 The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization. - Provides a mature and proven playbook approach (methodology) to enabling data governance that supports agile implementation - Features specific examples of current industry challenges in enterprise risk management, including anti-money laundering and fraud prevention - Describes business benefit measures and funding approaches using exposure based cost models that augment risk models for cost avoidance analysis and accelerated delivery approaches using data integration sprints for application, integration, and information delivery success |
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