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| customer data management meaning: 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 data management meaning: 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 data management meaning: 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 data management meaning: Data Driven Thomas C. Redman, 2008-09-22 Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the Data Doc, shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that. |
| customer data management meaning: 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 data management meaning: 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 data management meaning: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| customer data management meaning: 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 data management meaning: 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 data management meaning: 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 data management meaning: 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. |
| customer data management meaning: Data Management: a gentle introduction Bas van Gils, 2020-03-03 The overall objective of this book is to show that data management is an exciting and valuable capability that is worth time and effort. More specifically it aims to achieve the following goals: 1. To give a “gentle” introduction to the field of DM by explaining and illustrating its core concepts, based on a mix of theory, practical frameworks such as TOGAF, ArchiMate, and DMBOK, as well as results from real-world assignments. 2. To offer guidance on how to build an effective DM capability in an organization.This is illustrated by various use cases, linked to the previously mentioned theoretical exploration as well as the stories of practitioners in the field. The primary target groups are: busy professionals who “are actively involved with managing data”. The book is also aimed at (Bachelor’s/ Master’s) students with an interest in data management. The book is industry-agnostic and should be applicable in different industries such as government, finance, telecommunications etc. Typical roles for which this book is intended: data governance office/ council, data owners, data stewards, people involved with data governance (data governance board), enterprise architects, data architects, process managers, business analysts and IT analysts. The book is divided into three main parts: theory, practice, and closing remarks. Furthermore, the chapters are as short and to the point as possible and also make a clear distinction between the main text and the examples. If the reader is already familiar with the topic of a chapter, he/she can easily skip it and move on to the next. |
| customer data management meaning: 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 data management meaning: Marketing Philip Kotler, Suzan Burton, Kenneth Deans, Linen Brown, Gary Armstrong, 2015-05-20 The ultimate resource for marketing professionals Today’s marketers are challenged to create vibrant, interactive communities of consumers who make products and brands a part of their daily lives in a dynamic world. Marketing, in its 9th Australian edition, continues to be the authoritative principles of marketing resource, delivering holistic, relevant, cutting edge content in new and exciting ways. Kotler delivers the theory that will form the cornerstone of your marketing studies, and shows you how to apply the concepts and practices of modern marketing science. Comprehensive and complete, written by industry-respected authors, this will serve as a perennial reference throughout your career. |
| customer data management meaning: A Practical Guide to Data Mining for Business and Industry Andrea Ahlemeyer-Stubbe, Shirley Coleman, 2014-03-31 Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest. |
| customer data management meaning: Principles of Marketing Gary Armstrong, Stewart Adam, Sara Denize, Philip Kotler, 2014-10-01 The 6th edition of Principles of Marketing makes the road to learning and teaching marketing more effective, easier and more enjoyable than ever. Today’s marketing is about creating customer value and building profitable customer relationships. With even more new Australian and international case studies, engaging real-world examples and up-to-date information, Principles of Marketing shows students how customer value–creating and capturing it–drives every effective marketing strategy. The 6th edition is a thorough revision, reflecting the latest trends in marketing, including new coverage of social media, mobile and other digital technologies. In addition, it covers the rapidly changing nature of customer relationships with both companies and brands, and the tools marketers use to create deeper consumer involvement. |
| customer data management meaning: Web Data Management Serge Abiteboul, Ioana Manolescu, Philippe Rigaux, Marie-Christine Rousset, Pierre Senellart, 2011-11-28 The Internet and World Wide Web have revolutionized access to information. Users now store information across multiple platforms from personal computers to smartphones and websites. As a consequence, data management concepts, methods and techniques are increasingly focused on distribution concerns. Now that information largely resides in the network, so do the tools that process this information. This book explains the foundations of XML with a focus on data distribution. It covers the many facets of distributed data management on the Web, such as description logics, that are already emerging in today's data integration applications and herald tomorrow's semantic Web. It also introduces the machinery used to manipulate the unprecedented amount of data collected on the Web. Several 'Putting into Practice' chapters describe detailed practical applications of the technologies and techniques. The book will serve as an introduction to the new, global, information systems for Web professionals and master's level courses. |
| customer data management meaning: Impact Mapping Gojko Adzic, 2012-10 A practical guide to impact mapping, a simple yet incredibly effective method for collaborative strategic planning that helps organizations make an impact with software. |
| customer data management meaning: Building a Data Warehouse Vincent Rainardi, 2008-03-11 Here is the ideal field guide for data warehousing implementation. This book first teaches you how to build a data warehouse, including defining the architecture, understanding the methodology, gathering the requirements, designing the data models, and creating the databases. Coverage then explains how to populate the data warehouse and explores how to present data to users using reports and multidimensional databases and how to use the data in the data warehouse for business intelligence, customer relationship management, and other purposes. It also details testing and how to administer data warehouse operation. |
| customer data management meaning: 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 data management meaning: The Effortless Experience Matthew Dixon, Nick Toman, Rick DeLisi, 2013-09-12 Everyone knows that the best way to create customer loyalty is with service so good, so over the top, that it surprises and delights. But what if everyone is wrong? In their acclaimed bestseller The Challenger Sale, Matthew Dixon and his colleagues at CEB busted many longstanding myths about sales. Now they’ve turned their research and analysis to a new vital business subject—customer loyalty—with a new book that turns the conventional wisdom on its head. The idea that companies must delight customers by exceeding service expectations is so entrenched that managers rarely even question it. They devote untold time, energy, and resources to trying to dazzle people and inspire their undying loyalty. Yet CEB’s careful research over five years and tens of thousands of respondents proves that the “dazzle factor” is wildly overrated—it simply doesn’t predict repeat sales, share of wallet, or positive wordof-mouth. The reality: Loyalty is driven by how well a company delivers on its basic promises and solves day-to-day problems, not on how spectacular its service experience might be. Most customers don’t want to be “wowed”; they want an effortless experience. And they are far more likely to punish you for bad service than to reward you for good service. If you put on your customer hat rather than your manager or marketer hat, this makes a lot of sense. What do you really want from your cable company, a free month of HBO when it screws up or a fast, painless restoration of your connection? What about your bank—do you want free cookies and a cheerful smile, even a personal relationship with your teller? Or just a quick in-and-out transaction and an easy way to get a refund when it accidentally overcharges on fees? The Effortless Experience takes readers on a fascinating journey deep inside the customer experience to reveal what really makes customers loyal—and disloyal. The authors lay out the four key pillars of a low-effort customer experience, along the way delivering robust data, shocking insights and profiles of companies that are already using the principles revealed by CEB’s research, with great results. And they include many tools and templates you can start applying right away to improve service, reduce costs, decrease customer churn, and ultimately generate the elusive loyalty that the “dazzle factor” fails to deliver. The rewards are there for the taking, and the pathway to achieving them is now clearly marked. |
| customer data management meaning: 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 data management meaning: 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. |
| customer data management meaning: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| customer data management meaning: 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 data management meaning: Journal of Database Management ( Vol 23 ISS 1) Keng Siau, 2011-12 |
| customer data management meaning: 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 |
| customer data management meaning: Win with Advanced Business Analytics Jean-Paul Isson, Jesse Harriott, 2012-09-25 Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. Provides the essential concept and framework to implement business analytics Written clearly for a nontechnical audience Filled with case studies across a variety of industries Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition. |
| customer data management meaning: Electronic Customer Relationship Management Jerry Fjermestad, Nicholas C Robertson Jr, 2015-05-15 This work offers a state-of-the art survey of information systems research on electronic customer relationship management (eCRM). It provides important new frameworks derived from current cases and applications in this emerging field. Each chapter takes a collaborative approach to eCRM that goes beyond the analytical and operational perspectives most often taken by researchers in the field. Chapters also stress integration with other enterprise information systems. The book is organized in four parts: Part I presents an overview of the role of CRM and eCRM in marketing and supply chain management; Part II focuses on the organizational success factors behind eCRM implementation; Part III presents cases of eCRM performance enhancement; and Part IV addresses eCRM issues in business-to-consumer commerce. |
| customer data management meaning: The Intelligent Marketer’s Guide to Data Privacy Robert W. Palmatier, Kelly D. Martin, 2019-02-02 **Winner of the 2021 Leonard L. Berry Marketing Book Award from the American Marketing Association.** Firms are collecting and analyzing customer data at an ever increasing rate in response to evidence that data analytics (precision targeting, improved selling) generates a positive return. Yet efforts often ignore customers’ privacy concerns and feelings of vulnerability with long-term effects on customers’ trust, relationships, and ultimately financial performance. Big data, privacy, and cybersecurity often is relegated to IT and legal teams with minimal regard for customer relationships. This book fills the void by taking a customer-centric approach to privacy. It offers both defensive and offensive marketing-based privacy strategies that strongly position firms in today’s data-intensive landscape. The book also helps managers anticipate future consumer and legislative trends. Drawing from the authors’ own work and extant research, this book offers a compelling guide for building and implementing big data- and privacy-informed business strategies. Specifically, the book: · -Describes the consumer psychology of privacy · -Deconstructs relevant legal and regulatory issues · - Offers defensive privacy strategies · - Describes offensive privacy strategies · Provides an executive summary with the Six Tenets for Effective Privacy Marketing This book will be useful to managers, students, or the casual reader who is interested in how and why big data and consumer privacy are transforming business. Moving beyond summary privacy insights, the book also offers a detailed and compelling action plan for improving performance by protecting against privacy threats as well as developing and implementing offensive privacy strategy. In the future, many firms will be competing through an integrated, customer-centric big data privacy strategy and this book will guide managers in this journey. |
| customer data management meaning: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| customer data management meaning: Big Data Management Fausto Pedro García Márquez, Benjamin Lev, 2016-11-15 This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples. |
| customer data management meaning: Encyclopedia of Decision Making and Decision Support Technologies Adam, Frederic, Humphreys, Patrick, 2008-04-30 As effective organizational decision making is a major factor in a company's success, a comprehensive account of current available research on the core concepts of the decision support agenda is in high demand by academicians and professionals. Through 110 authoritative contributions by over 160 of the world's leading experts the Encyclopedia of Decision Making and Decision Support Technologies presents a critical mass of research on the most up-to-date research on human and computer support of managerial decision making, including discussion on support of operational, tactical, and strategic decisions, human vs. computer system support structure, individual and group decision making, and multi-criteria decision making. |
| customer data management meaning: A Primer in Financial Data Management Martijn Groot, 2017-05-10 A Primer in Financial Data Management describes concepts and methods, considering financial data management, not as a technological challenge, but as a key asset that underpins effective business management. This broad survey of data management in financial services discusses the data and process needs from the business user, client and regulatory perspectives. Its non-technical descriptions and insights can be used by readers with diverse interests across the financial services industry. The need has never been greater for skills, systems, and methodologies to manage information in financial markets. The volume of data, the diversity of sources, and the power of the tools to process it massively increased. Demands from business, customers, and regulators on transparency, safety, and above all, timely availability of high quality information for decision-making and reporting have grown in tandem, making this book a must read for those working in, or interested in, financial management. - Focuses on ways information management can fuel financial institutions' processes, including regulatory reporting, trade lifecycle management, and customer interaction - Covers recent regulatory and technological developments and their implications for optimal financial information management - Views data management from a supply chain perspective and discusses challenges and opportunities, including big data technologies and regulatory scrutiny |
| customer data management meaning: Digital Information Design (DID) – A Practitioner Guide Brian Johnson, Chris Verhoef, Leon-Paul de Rouw, 2023-06-02 We DID IT; so can you. DID is Digital Information Design. IT is of course the ubiquitous Information Technology that is so simple, so easy to design and change that it (sorry, IT) never goes wrong and all you need to do is to teach a few people a bit about coding, implementing and a best practice. More seriously, if all of IT projects were successful, Digital Information Design would be a waste of time. However, the failure rate of IT outsourcing deals is around 40%, and hiring a sourcing consultant increases the odds of failure. IT-enabled enterprises thus need to know themselves how to govern the IT function. DID is the only best practice that recognizes that to do just that. You need more than best practice; and inevitably more than one best practice as well as people who understand that there is no such thing as simple easy to design IT that never changes. Therefore, to support your work, Digital Information Design (DID) guidance has been developed as a good practice to get it actually governed and done! People working in IT rarely have proficient domain experience like working as a user/customer in the line of business that is employing their IT services to perform what once were manual activities. Vice versa, people working in the line of business are rarely well-versed in designing complex IT systems and processes, but times have changed. The DID framework aids in bringing together the right mix of IT and domain expertise, thereby helping to connect both views of the same, albeit complex, IT-enabled world. DID recognizes complexity, demands inclusivity of all stakeholders in design and provides a simple yet useful model to identify key resources. And it recognizes that you cannot do everything using a single governing concept. If you want to come to grips with designing business services that can be relied upon, try using DID. This book is about the design and functioning of enterprise-wide business information management using intelligent customer principles, with particular regard to digitization. The DID framework is used to describe, position and provide tools for the design of the intelligent customer function focusing on the enterprise information assets. This framework has been set up to effectively shape business information management within an enterprise, with the aim of ensuring a better use of information and technology in the enterprise. DID Practitioner guide is part of the DID library and specifically deals with the ability of an enterprise to manage and control data services from a practical viewpoint. The principles are written so that they can be used in various disciplines of supporting services and the primary processes of both for-profit or not for-profit enterprises. |
| customer data management meaning: The Fourth Industrial Revolution Klaus Schwab, 2017-01-03 World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress. |
| customer data management meaning: The Analytic Hospitality Executive Kelly A. McGuire, 2016-08-29 Targeted analytics to address the unique opportunities in hospitality and gaming The Analytic Hospitality Executive helps decision makers understand big data and how it can drive value in the industry. Written by a leading business analytics expert who specializes in hospitality and travel, this book draws a direct link between big data and hospitality, and shows you how to incorporate analytics into your strategic management initiative. You'll learn which data types are critical, how to identify productive data sources, and how to integrate analytics into multiple business processes to create an overall analytic culture that turns information into insight. The discussion includes the tools and tips that help make it happen, and points you toward the specific places in your business that could benefit from advanced analytics. The hospitality and gaming industry has unique needs and opportunities, and this book's targeted guidance provides a roadmap to big data benefits. Like most industries, the hospitality and gaming industry is experiencing a rapid increase in data volume, variety, and velocity. This book shows you how to corral this growing current, and channel it into productive avenues that drive better business. Understand big data and analytics Incorporate analytics into existing business processes Identify the most valuable data sources Create a strategic analytic culture that drives value Although the industry is just beginning to recognize the value of big data, it's important to get up to speed quickly or risk losing out on benefits that could drive business to greater heights. The Analytic Hospitality Executive provides a targeted game plan from an expert on the inside, so you can start making your data work for you. |
| customer data management meaning: How Will You Measure Your Life? (Harvard Business Review Classics) Clayton M. Christensen, 2017-01-17 In the spring of 2010, Harvard Business School’s graduating class asked HBS professor Clay Christensen to address them—but not on how to apply his principles and thinking to their post-HBS careers. The students wanted to know how to apply his wisdom to their personal lives. He shared with them a set of guidelines that have helped him find meaning in his own life, which led to this now-classic article. Although Christensen’s thinking is rooted in his deep religious faith, these are strategies anyone can use. Since 1922, Harvard Business Review has been a leading source of breakthrough ideas in management practice. The Harvard Business Review Classics series now offers you the opportunity to make these seminal pieces a part of your permanent management library. Each highly readable volume contains a groundbreaking idea that continues to shape best practices and inspire countless managers around the world. |
| customer data management meaning: XML in Data Management Peter Aiken, M. David Allen, 2004-07-01 XML in Data Management is for IT managers and technical staff involved in the creation, administration, or maintenance of a data management infrastructure that includes XML. For most IT staff, XML is either just a buzzword that is ignored or a silver bullet to be used in every nook and cranny of their organization. The truth is in between the two. This book provides the guidance necessary for data managers to make measured decisions about XML within their organizations. Readers will understand the uses of XML, its component architecture, its strategic implications, and how these apply to data management. - Takes a data-centric view of XML - Explains how, when, and why to apply XML to data management systems - Covers XML component architecture, data engineering, frameworks, metadata, legacy systems, and more - Discusses the various strengths and weaknesses of XML technologies in the context of organizational data management and integration |
| customer data management meaning: Hands-On Salesforce Data Cloud Joyce Kay Avila, 2024-08-09 Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud platform. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data. Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and Einstein's built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's low-code/no-code functionality to expertly build a Data Cloud foundation that unlocks the power of structured and unstructured data. Use Data Cloud tools to build your own predictive models or leverage third-party machine learning platforms like Amazon SageMaker, Google Vertex AI, and Databricks. This book will help you: Develop a plan to execute a CDP project effectively and efficiently Connect Data Cloud to external data sources and build out a Customer 360 Data Model Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure Use Salesforce Data Cloud capabilities for identity resolution and segmentation Create calculated, streaming, visualization, and predictive insights Use Data Graphs to power Salesforce Einstein capabilities Learn Data Cloud best practices for all phases of the development lifecycle |
consumer、customer、client 有何区别？ - 知乎
对于customer和consumer，我上marketing的课的时候区分过这两个定义。 customer behavior：a broad term that covers individual consumers who buy goods and services for their own use …
Consumer与customer有区别吗？具体作什么区别？ - 知乎
Mar 18, 2014 · 一般把 customer 翻译做 “客户“ 比如你是杜蕾斯的生产商，那么中国总代，上海曼伦商贸有限公司，就是你的customer，然后从曼伦进货的全家就是曼伦的customer，然后隔 …
Windows 10 business 和 consumer 中的专业版有什么不同？ - 知乎
Mar 14, 2020 · Windows10 有business editions 和 consumer editions 版。其中每个都有 专业工作站版，可这2个专业工作…
想问一下大家web of science文献检索点不动 只能用作者检索怎么 …
手机电脑打开都是这样 我想用文献检索 不想用作者检索啊啊啊啊啊
什么是CRM系统？它的作用是什么？ - 知乎
CRM（Customer Relationship Management），即客户关系管理系统.。 是指利用软件、硬件和网络技术，为企业建立一个客户信息收集、管理、分析和利用的信息系统。通俗地讲， CRM就 …
请问金融系统中提到的KYC是做什么用的？ - 知乎
KYC看着高端，其实我们每个人都经历过。例如，当你去银行开户的时候，都必须要提交身份证件，甚至有时候还要提交家庭住址证明。这便是一个最简单的KYC。(也叫做CIP - Customer …
什么是SCRM？为什么企业要做SCRM？ - 知乎
SCRM翻译后的全程是：Social Customer Relationship Management ，可以看到这里的“S”原来是“Social”，也就是“社交”的意思。 尽管只是多了一个S，却将原先CRM呈现的客户管理行为转 …
什么是跨境电商，你们了解多少？ - 知乎
跨境电子商务是指不同国度或地域的买卖双方经过互联网以邮件或者快递等方式通关，将传统贸易中的展现、洽谈和成交环节数字化，完成产品进口的的新型贸易方式，当前主流的跨境电商形 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
新媒体的KOL、KOC是什么? - 知乎
KOC有双重身份，即Customer和Creator，KOC是消费者的同时也是创作者，是对消费者的消费决策起到关键作用的群体。 KOL与KOC在本质上截然不同，是两个群体。前者是推，而KOC是 …
consumer、customer、client 有何区别？ - 知乎
对于customer和consumer，我上marketing的课的时候区分过这两个定义。 customer behavior：a broad term that covers individual consumers who buy goods and services for their own use …
Consumer与customer有区别吗？具体作什么区别？ - 知乎
Mar 18, 2014 · 一般把 customer 翻译做 “客户“ 比如你是杜蕾斯的生产商，那么中国总代，上海曼伦商贸有限公司，就是你的customer，然后从曼伦进货的全家就是曼伦的customer，然后隔 …
Windows 10 business 和 consumer 中的专业版有什么不同？ - 知乎
Mar 14, 2020 · Windows10 有business editions 和 consumer editions 版。其中每个都有 专业工作站版，可这2个专业工作…
想问一下大家web of science文献检索点不动 只能用作者检索怎么 …
手机电脑打开都是这样 我想用文献检索 不想用作者检索啊啊啊啊啊
什么是CRM系统？它的作用是什么？ - 知乎
CRM（Customer Relationship Management），即客户关系管理系统.。 是指利用软件、硬件和网络技术，为企业建立一个客户信息收集、管理、分析和利用的信息系统。通俗地讲， CRM就 …
请问金融系统中提到的KYC是做什么用的？ - 知乎
KYC看着高端，其实我们每个人都经历过。例如，当你去银行开户的时候，都必须要提交身份证件，甚至有时候还要提交家庭住址证明。这便是一个最简单的KYC。(也叫做CIP - Customer …
什么是SCRM？为什么企业要做SCRM？ - 知乎
SCRM翻译后的全程是：Social Customer Relationship Management ，可以看到这里的“S”原来是“Social”，也就是“社交”的意思。 尽管只是多了一个S，却将原先CRM呈现的客户管理行为转 …
什么是跨境电商，你们了解多少？ - 知乎
跨境电子商务是指不同国度或地域的买卖双方经过互联网以邮件或者快递等方式通关，将传统贸易中的展现、洽谈和成交环节数字化，完成产品进口的的新型贸易方式，当前主流的跨境电商形 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
新媒体的KOL、KOC是什么? - 知乎
KOC有双重身份，即Customer和Creator，KOC是消费者的同时也是创作者，是对消费者的消费决策起到关键作用的群体。 KOL与KOC在本质上截然不同，是两个群体。前者是推，而KOC是 …
