| customer master data management: 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. |
| customer master data management: 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 |
| customer master data management: 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 |
| customer master data management: Master Data Management in Practice Dalton Cervo, Mark Allen, 2011-07-05 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. |
| customer master data management: 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 |
| customer master data management: 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 |
| customer master data management: 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. |
| customer master data management: 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. |
| customer master data management: The Practitioner's Guide to Data Quality Improvement David Loshin, 2010-11-22 The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. - Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. - Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. - Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. |
| customer master data management: 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. |
| customer master data management: 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 |
| customer master data management: 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. |
| customer master data management: Master Data Management for SaaS Applications Whei-Jen Chen, Bhavani Eshwar, Ramya Rajendiran, Shettigar Srinivas, Manjunath B Subramanian, Bharathi Venkatasubramanian, IBM Redbooks, 2014-10-19 Enterprises today understand the value of employing a master data management (MDM) solution for managing and governing mission critical information assets. chief data officers and chief information officers drive MDM initiatives with IBM® InfoSphere® Master Data Management to improve business results and operational efficiencies, which can help to lower costs and to reduce the risk of using untrusted master information in business process. Cloud computing introduces new considerations where enterprise IT architectures are extended beyond the corporate networks into the cloud. Many enterprises are now adopting turnkey business applications offered as software as a service (SaaS) solutions, such as customer relationship management (CRM), payroll processing, human resource management, and many more. However, in the context of MDM solutions, many organizations perceive risks in having these solutions deployed on the cloud. In some cases, organization are concerned with the legal restrictions of deploying solutions on the cloud, whereas in other cases organizations have policies and strategies in force that limit solution deployment on the cloud. Immaterial of what all the cases might be, industry trends point to a prediction that many extended enterprises will keep MDM solutions on premises and will want its integrations with SaaS applications, specifically customer and asset domains. This trend puts a key focus on an important component in the solution construct, that is, the cloud integration middleware and how it fits with hybrid cloud architectures that span on premises and cloud services. As this trend pans out, the on-premises MDM solution integration with SaaS applications will be the key pain point for the extended enterprise. This IBM Redbooks® publication provides guidance to chief data officers, chief information officers, MDM practitioners, integration architects, and others who are interested in the integration of IBM InfoSphere Master Data Management with SaaS applications. This book lays the background on how mastering and governance needs for SaaS applications is quite similar to what on-premises business applications would need. It draws the perspective for serving the on-premises application and the SaaS application with the same MDM hub. This book describes how IBM WebSphere® Cast Iron® Cloud Integration can serve as the de-facto cloud integration middleware to integrate the on-premises InfoSphere Master Data Management systems with any SaaS application by using Saleforce.com integration as an example. This book also covers aspects of handling bulk operations with IBM InfoSphere Information Server. After reading this book, you will have a good understanding about the considerations for on-premises InfoSphere Master Data Management integration with SaaS applications in general and Salesforce.com in particular. The MDM practitioners and integration architects will understand the deployable integrations patterns and, in general, will be able to effectively contribute to delivering strategies that involve building solutions in this area. Additionally, SaaS vendors and customers looking to build or implement SaaS solutions that might require trusted master information will be able to use this compilation to ensure that the right architecture is put together and adhered to as a set of standard integrations patterns with all the core building blocks is essential for the longevity of a solution in this space. |
| customer master data management: Managing Data in Motion April Reeve, 2013-02-26 Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the data in motion in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and big data applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. - Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types - Explains, in non-technical terms, the architecture and components required to perform data integration - Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of Big Data |
| customer master data management: Effective Master Data Management with SAP NetWeaver MDM Andy N. Walker, Jagadeesh Ganapathy, 2009 This must-have reference for Master Data Management teaches you why and how to successfully integrate SAP NetWeaver MDM into your organization. Discover the key business reasons and benefits of implementing business partner master data processes with SAP NetWeaver MDM. You'll learn the business drivers for MDM, as well as the value of integrating with the Dun & Bradstreet services. From there, you'll travel through the complete process of planning for and implementing an MDM program. This is the complete guide for understanding what MDM is and what it can do for your business, teaching you how to develop the practical skills necessary to integrate SAP NetWeaver MDM into your systems landscape. Throughout the book, you'll find useful case studies and solution examples for implementing your MDM processes in SAP NetWeaver MDM 5.5 SP 06. |
| customer master data management: Customer Data Platforms Martin Kihn, Christopher B. O'Hara, 2020-11-06 Master the hottest technology around to drive marketing success Marketers are faced with a stark and challenging dilemma: customers demand deep personalization, but they are increasingly leery of offering the type of personal data required to make it happen. As a solution to this problem, Customer Data Platforms have come to the fore, offering companies a way to capture, unify, activate, and analyze customer data. CDPs are the hottest marketing technology around today, but are they worthy of the hype? Customer Data Platforms takes a deep dive into everything CDP so you can learn how to steer your firm toward the future of personalization. Over the years, many of us have built byzantine “stacks” of various marketing and advertising technology in an attempt to deliver the fabled “right person, right message, right time” experience. This can lead to siloed systems, disconnected processes, and legacy technical debt. CDPs offer a way to simplify the stack and deliver a balanced and engaging customer experience. Customer Data Platforms breaks down the fundamentals, including how to: Understand the problems of managing customer data Understand what CDPs are and what they do (and don't do) Organize and harmonize customer data for use in marketing Build a safe, compliant first-party data asset that your brand can use as fuel Create a data-driven culture that puts customers at the center of everything you do Understand how to use AI and machine learning to drive the future of personalization Orchestrate modern customer journeys that react to customers in real-time Power analytics with customer data to get closer to true attribution In this book, you’ll discover how to build 1:1 engagement that scales at the speed of today’s customers. |
| customer master data management: 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. |
| customer master data management: Mastering Customer Value Management Ray Kordupleski, 2003 There is an emerging art and science of customer value management that is proving its worth inincreased market share and shareholder value for the companies that practice it. Customer value management is about: choosing value (determining what customers really value and developing your value proposition ) delivering value (making sure business processes are aligned with value proposition) communicating value (educating the market on your value proposition)The concepts of customer value management and the practical tools that have been developed to support them are the subject of this book. |
| customer master data management: 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. |
| customer master data management: Customer Relationship Management V. Kumar, Werner Reinartz, 2018-05-15 This book presents an extensive discussion of the strategic and tactical aspects of customer relationship management as we know it today. It helps readers obtain a comprehensive grasp of CRM strategy, concepts and tools and provides all the necessary steps in managing profitable customer relationships. Throughout, the book stresses a clear understanding of economic customer value as the guiding concept for marketing decisions. Exhaustive case studies, mini cases and real-world illustrations under the title “CRM at Work” all ensure that the material is both highly accessible and applicable, and help to address key managerial issues, stimulate thinking, and encourage problem solving. The book is a comprehensive and up-to-date learning companion for advanced undergraduate students, master's degree students, and executives who want a detailed and conceptually sound insight into the field of CRM. The new edition provides an updated perspective on the latest research results and incorporates the impact of the digital transformation on the CRM domain. |
| customer master data management: SAP Master Data Governance Homiar Kalwachwala, Sandeep Chahal, Santhosh Cheekoti, Antony Isacc, Rajani Khambhampati, Vikas Lodha, Syama Srinivasan, David Quirk, 2019 Ready to improve the handling of your master data? Walk through implementing, configuring, and using SAP Master Data Governance (SAP MDG)! Whether your organization requires custom applications or works with out-of-the-box central governance, consolidation, and mass processing, you'll find detailed instructions for every step. From data, process, and UI modeling to data replication, master your data! Highlights include: 1) Deployment 2) Data modeling 3) Process modeling 4) Data quality 5) Data replication 6) Data migration 7) Consolidation 8) Operations 9) Mass processing 10) Integrations 11) Extensions 12) Analytics |
| customer master data management: Customer Relationship Management V. Kumar, Werner Reinartz, 2012-04-30 Customer relationship management (CRM) as a strategy and as a technology has gone through an amazing evolutionary journey. The initial technological approach was followed by many disappointing initiatives only to see the maturing of the underlying concepts and applications in recent years. Today, CRM represents a strategy, a set of tactics, and a technology that have become indispensible in the modern economy. This book presents an extensive treatment of the strategic and tactical aspects of customer relationship management as we know it today. It stresses developing an understanding of economic customer value as the guiding concept for marketing decisions. The goal of the book is to serve as a comprehensive and up-to-date learning companion for advanced undergraduate students, master's degree students, and executives who want a detailed and conceptually sound insight into the field of CRM. |
| customer master data management: The Data Governance Imperative Steve Sarsfield, 2009-04-23 This practical book covers both strategies and tactics around managing a data governance initiative to help make the most of your data. |
| customer master data management: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| customer master data management: SAP CRM Chandrakant Agarwal, 2015 Master the business processes and configuration for SAP Customer Relationship Management! This guide offers the details you need about key SAP CRM functionality and customization. Understand the key SAP CRM business processes and then configure the system for marketing, sales, and service. From master data to middleware to the web UI, get the answers you need to tailor SAP CRM for your own requirements. |
| customer master data management: Customer Relationship Management Roger J. Baran, Robert J. Galka, 2016-12-08 This book balances the behavioral and database aspects of customer relationship management, providing students with a comprehensive introduction to an often overlooked, but important aspect of marketing strategy. Baran and Galka deliver a book that helps students understand how an enhanced customer relationship strategy can differentiate an organization in a highly competitive marketplace. This edition has several new features: Updates that take into account the latest research and changes in organizational dynamics, business-to-business relationships, social media, database management, and technology advances that impact CRM New material on big data and the use of mobile technology An overhaul of the social networking chapter, reflecting the true state of this dynamic aspect of customer relationship management today A broader discussion of the relationship between CRM and the marketing function, as well as its implications for the organization as a whole Cutting edge examples and images to keep readers engaged and interested A complete typology of marketing strategies to be used in the CRM strategy cycle: acquisition, retention, and win-back of customers With chapter summaries, key terms, questions, exercises, and cases, this book will truly appeal to upper-level students of customer relationship management. Online resources, including PowerPoint slides, an instructor’s manual, and test bank, provide instructors with everything they need for a comprehensive course in customer relationship management. |
| customer master data management: Customer Data Integration Jill Dyché, Evan Levy, 2011-01-31 Customers are the heart of any business. But we can't succeed if we develop only one talk addressed to the 'average customer.' Instead we must know each customer and build our individual engagements with that knowledge. If Customer Relationship Management (CRM) is going to work, it calls for skills in Customer Data Integration (CDI). This is the best book that I have seen on the subject. Jill Dyché is to be complimented for her thoroughness in interviewing executives and presenting CDI. -Philip Kotler, S. C. Johnson Distinguished Professor of International Marketing Kellogg School of Management, Northwestern University In this world of killer competition, hanging on to existing customers is critical to survival. Jill Dyché's new book makes that job a lot easier than it has been. -Jack Trout, author, Differentiate or Die Jill and Evan have not only written the definitive work on Customer Data Integration, they've made the business case for it. This book offers sound advice to business people in search of innovative ways to bring data together about customers-their most important asset-while at the same time giving IT some practical tips for implementing CDI and MDM the right way. -Wayne Eckerson, The Data Warehousing Institute author of Performance Dashboards: Measuring, Monitoring, and Managing Your Business Whatever business you're in, you're ultimately in the customer business. No matter what your product, customers pay the bills. But the strategic importance of customer relationships hasn't brought companies much closer to a single, authoritative view of their customers. Written from both business and technicalperspectives, Customer Data Integration shows companies how to deliver an accurate, holistic, and long-term understanding of their customers through CDI. |
| customer master data management: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together |
| customer master data management: Building a Scalable Data Warehouse with Data Vault 2.0 Daniel Linstedt, Michael Olschimke, 2015-09-15 The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. Building a Scalable Data Warehouse covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: - How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. - Important data warehouse technologies and practices. - Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. - Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast - Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse - Demystifies data vault modeling with beginning, intermediate, and advanced techniques - Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0 |
| customer master data management: Implementing Order to Cash Process in SAP Chandrakant Agarwal, 2021-05-14 Implement critical business processes with mySAP Business Suite to integrate key functions that add value to every facet of your organization Key FeaturesLearn master data concepts and UI technologies in SAP systemsExplore key functions of different sales processes, order fulfillment options, transportation planning, logistics execution processes, and customer invoicingConfigure the Order to Cash process in SAP systems and apply it to your business needsBook Description Using different SAP systems in an integrated way to gain maximum benefits while running your business is made possible by this book, which covers how to effectively implement SAP Order to Cash Process with SAP Customer Relationship Management (CRM), SAP Advanced Planning and Optimization (APO), SAP Transportation Management System (TMS), SAP Logistics Execution System (LES), and SAP Enterprise Central Component (ECC). You'll understand the integration of different systems and how to optimize the complete Order to Cash Process with mySAP Business Suite. With the help of this book, you'll learn to implement mySAP Business Suite and understand the shortcomings in your existing SAP ECC environment. As you advance through the chapters, you'll get to grips with master data attributes in different SAP environments and then shift focus to the Order to Cash cycle, including order management in SAP CRM, order fulfillment in SAP APO, transportation planning in SAP TMS, logistics execution in SAP LES, and billing in SAP ECC. By the end of this SAP book, you'll have gained a thorough understanding of how different SAP systems work together with the Order to Cash process. What you will learnDiscover master data in different SAP environmentsFind out how different sales processes, such as quotations, contracts, and order management, work in SAP CRMBecome well-versed with the steps involved in order fulfillment, such as basic and advanced ATP checks in SAP APOGet up and running with transportation requirement and planning and freight settlement with SAP TMSExplore warehouse management with SAP LES to ensure high transparency and predictability of processesUnderstand how to process customer invoicing with SAP ECCWho this book is for This book is for SAP consultants, SME managers, solution architects, and key users of SAP with knowledge of end-to-end business processes. Customers operating SAP CRM, SAP TMS, and SAP APO as part of daily operations will also benefit from this book by understanding the key capabilities and integration touchpoints. Working knowledge of SAP ECC, SAP CRM, SAP APO, SAP TMS, and SAP LES is necessary to get started with this book. |
| customer master data management: 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. |
| customer master data management: Data Management for Researchers Kristin Briney, 2015-09-01 A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline. —Robert Buntrock, Chemical Information Bulletin |
| customer master data management: Managing Customer Experience and Relationships Don Peppers, Martha Rogers, 2022-04-19 Every business on the planet is trying to maximize the value created by its customers Learn how to do it, step by step, in this newly revised Fourth Edition of Managing Customer Experience and Relationships: A Strategic Framework. Written by Don Peppers and Martha Rogers, Ph.D., recognized for decades as two of the world's leading experts on customer experience issues, the book combines theory, case studies, and strategic analyses to guide a company on its own quest to position its customers at the very center of its business model, and to treat different customers differently. This latest edition adds new material including: How to manage the mass-customization principles that drive digital interactions How to understand and manage data-driven marketing analytics issues, without having to do the math How to implement and monitor customer success management, the new discipline that has arisen alongside software-as-a-service businesses How to deal with the increasing threat to privacy, autonomy, and competition posed by the big tech companies like Facebook, Amazon, and Google Teaching slide decks to accompany the book, author-written test banks for all chapters, a complete glossary for the field, and full indexing Ideal not just for students, but for managers, executives, and other business leaders, Managing Customer Experience and Relationships should prove an indispensable resource for marketing, sales, or customer service professionals in both the B2C and B2B world. |
| customer master data management: 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 |
| customer master data management: 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 |
| customer master data management: Entity Information Life Cycle for Big Data John R. Talburt, Yinle Zhou, 2015-04-20 Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data's impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics. - Explains the business value and impact of entity information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems - Offers practical guidance to help you design and build an EIM system that will successfully handle big data - Details how to measure and evaluate entity integrity in MDM systems and explains the principles and processes that comprise EIM - Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems - Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system - Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as identity capture, identity update, and assertions |
| customer master data management: SAP Billing and Revenue Innovation Management Chaitanaya Desai, Sheikna Kulam, Chun Wei Ooi, Maniprakash Balasubramanian, Clement Sanjivi, Andreas Tan, Rakesh Rajagopal, 2019 Whether you're upgrading an existing billing system or moving to a subscription- or consumption-based model, SAP BRIM is ready--and here's is your guide! From subscription order management and charging to invoicing and contract accounting, get step-by-step instructions for each piece of the billing puzzle. For setup, execution, or analytics, follow a continuous case study through each billing process. With this book, join the future of billing! a. End-to-End Billing Learn the what and the why of SAP BRIM, and then master the how! Charging, invoicing, contract accounts receivable and payable, and subscription order management--see how to streamline billing with the SAP BRIM solutions. b. Configuration and Functionality Set up and use SAP BRIM tools: Subscription Order Management, SAP Convergent Charging, SAP Convergent Invoicing, FI-CA, and more. Implement them individually or as part of an integrated landscape. c. SAP BRIM in Action Meet Martex Corp., a fictional telecommunications case study and your guide through the SAP BRIM suite. Follow its path to subscription-based billing and learn from billing industry best practices! 1) SAP Billing and Revenue Innovation Management 2) Subscription order management 3) SAP Convergent Charging 4) SAP Convergent Invoicing 5) Contracts accounting (FI-CA) 6) SAP Convergent Mediation 7) Reporting and analytics 8) Implementation 9) Project management |
| customer master data management: MASTERING DATA MINING: THE ART AND SCIENCE OF CUSTOMER RELATIONSHIP MANAGEMENT Michael J. A. Berry, Gordon S. Linoff, 2008-09-01 Special Features: · Best-in-class data mining techniques for solving critical problems in all areas of business· Explains how to pick the right data mining techniques for specific problems· Shows how to perform analysis and evaluate results· Features real-world examples from across various industry sectors· Companion Web site with updates on data mining products and service providers About The Book: Companies have invested in building data warehouses to capture vast amounts of customer information. The payoff comes with mining or getting access to the data within this information gold mine to make better business decisions. Readers and reviewers loved Berry and Linoff's first book, Data Mining Techniques, because the authors so clearly illustrate practical techniques with real benefits for improved marketing and sales. Mastering Data Mining takes off from there-assuming readers know the basic techniques covered in the first book, the authors focus on how to best apply these techniques to real business cases. They start with simple applications and work up to the most powerful and sophisticated examples over the course of about 20 cases. (Ralph Kimball used this same approach in his highly successful Data Warehouse Toolkit). As with their first book, Mastering Data Mining is sufficiently technical for database analysts, but is accessible to technically savvy business and marketing managers. It should also appeal to a new breed of database marketing managers. |
| customer master data management: Impacts and Challenges of Cloud Business Intelligence Aljawarneh, Shadi, Malhotra, Manisha, 2020-12-18 Cloud computing provides an easier alternative for starting an IT-based business organization that requires much less of an initial investment. Cloud computing offers a significant edge of traditional computing with big data being continuously transferred to the cloud. For extraction of relevant data, cloud business intelligence must be utilized. Cloud-based tools, such as customer relationship management (CRM), Salesforce, and Dropbox are increasingly being integrated by enterprises looking to increase their agility and efficiency. Impacts and Challenges of Cloud Business Intelligence is a cutting-edge scholarly resource that provides comprehensive research on business intelligence in cloud computing and explores its applications in conjunction with other tools. Highlighting a wide range of topics including swarm intelligence, algorithms, and cloud analytics, this book is essential for entrepreneurs, IT professionals, managers, business professionals, practitioners, researchers, academicians, and students. |
| customer master data management: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
What is Customer Master Data Management | Tamr
May 19, 2021 · How Customer Master Data Management is Different for B2B and B2C Enterprises. While master data extends to both individual and business-focused customers, there are …
What is Master Data Management? | IBM
Jun 19, 2024 · Master data management (MDM) is a comprehensive approach to managing an organization's critical data across the enterprise. MDM uses technology, tools and processes to …
Customer [Client] Master Data - Data Model & Process Management
Feb 3, 2025 · Customer Master Data Management (CMDM) is a critical function for enterprises that want to maintain an accurate, unified, and structured record of their customers. It serves as the …
Elevating master data management in an organization | McKinsey
May 15, 2024 · This scenario is an example of poor master data management (MDM), which commonly results in suboptimal customer and employee experience, higher costs, and lost …
5 Key Aspects of Customer Master Data Management
4. Data Quality Management for Customer MDM. Data quality management is a critical aspect of customer MDM, as poor data quality can hinder effective decision-making and impact customer …
What Is Master Data Management? - Oracle
Customer master data management software consolidates, cleans, completes, and coordinates data to and from multiple sources. It can also standardize addresses, resolve duplicate record issues, …
What is Master Data Management (MDM)? - Informatica
What is a master record? Master data management creates a master record (also known as a “golden record” or “best version of the truth”) that contains the essential information upon which …
Customer Master Data Management (MDM) Best Practices
Sep 23, 2024 · Understanding your customer base can significantly differentiate your business from competitors. For organizations looking to get a better idea of what’s important to their …
What is Customer Master Data Management? - Credencys
Aug 10, 2023 · Effective customer master data management (MDM) is a crucial strategy for businesses seeking to optimize their customer data and gain a competitive edge. Recognizing …
8 Best Practices for Customer Master Data Management
May 16, 2024 · Unlocking customer success: The ultimate best practice guide to mastering customer master data management. Customer data is the backbone of any successful business …
What is Customer Master Data Management | Tamr
May 19, 2021 · How Customer Master Data Management is Different for B2B and B2C Enterprises. While master data extends to both individual and business-focused customers, …
What is Master Data Management? | IBM
Jun 19, 2024 · Master data management (MDM) is a comprehensive approach to managing an organization's critical data across the enterprise. MDM uses …
Customer [Client] Master Data - Data Model & Process Management
Feb 3, 2025 · Customer Master Data Management (CMDM) is a critical function for enterprises that want to maintain an accurate, unified, and structured record of their …
Elevating master data management in an organization | McKinsey
May 15, 2024 · This scenario is an example of poor master data management (MDM), which commonly results in suboptimal customer and employee experience, higher costs, and lost …
5 Key Aspects of Customer Master Data Management
4. Data Quality Management for Customer MDM. Data quality management is a critical aspect of customer MDM, as poor data quality can hinder effective decision-making and …
