Beyond Big Data: Using Social MDM to Drive Deep Customer Insight

Drive Powerful Business Value by Extending MDM to Social, Mobile, Local, and Transactional Data   Enterprises have long relied on Master Data Management (MDM) to improve customer-related processes. But MDM was designed primarily for structured data. Today, crucial information is increasingly captured in unstructured, transactional, and social formats: from tweets and Facebook posts to call center transcripts. Even with tools like Hadoop, extracting usable insight is difficult—often, because it’s so difficult to integrate new and legacy data sources.   In Beyond Big Data, five of IBM’s leading data management experts introduce powerful new ways to integrate social, mobile, location, and traditional data. Drawing on pioneering experience with IBM’s enterprise customers, they show how Social MDM can help you deepen relationships, improve prospect targeting, and fully engage customers through mobile channels.   Business leaders and practitioners will discover powerful new ways to combine social and master data to improve performance and uncover new opportunities. Architects and other technical leaders will find a complete reference architecture, in-depth coverage of relevant technologies and use cases, and domain-specific best practices for their own projects. Coverage Includes How Social MDM extends fundamental MDM concepts and techniques Architecting Social MDM: components, functions, layers, and interactions Identifying high value relationships: person to product and person to organization Mapping Social MDM architecture to specific products and technologies Using Social MDM to create more compelling customer experiences Accelerating your transition to highly-targeted, contextual marketing Incorporating mobile data to improve employee productivity Avoiding privacy and ethical pitfalls throughout your ecosystem Previewing Semantic MDM and other emerging trends    

Table of Contents:
Preface xviii Chapter 1  Introduction to Social MDM 1 Definition of Social MDM 1 Customer Insight and Opportunities with Social Data 2 Product Insight and Opportunities with Product Reviews 3 Traditional Master Data Management 4 Master Data Defined 5 Master Data Management—Today 8 Business Value of Traditional MDM 10 Customer Service    11 Marketing and Targeted Product Offers 11 Compliance 11 Hidden IT Costs 11 Case Study: Financial Institution 11 Social MDM 13 Data Distillation 14 Profile Linking 16 Available Throughout the Enterprise 16 Governance 16 Business Value of Social MDM 16 Conclusion 17 References 17 Additional Reading 17 Chapter 2  Use Cases and Requirements for Social MDM 19 Business Value of Social MDM—Use Cases and Customer Value 19 Improved Customer Experience Use Cases 20 Improved Target Marketing Use Cases 26 Underlying Capabilities Required for Social MDM 30 Cultural Awareness Capabilities for Social MDM 30 Locale, Location, and Location Awareness in Social MDM 32 Advanced Relationships in Social MDM 34 Person-to-Person Relationships 35 Person-to-Product Relationships: Sentiment 37 Person@Organization: The Social MDM–Driven Evolution of the B2B Business Model 40 Conclusion 43 References 43 Chapter 3  Capability Framework for Social MDM 47 Introduction 47 Data Domains 49 Differences Between Metadata, Reference Data, and Master Data 53 Embedding of the Social MDM RA in Enterprise Architecture 57 Capability Framework 58 Insight 60 Information Virtualization 61 Information Preparation 64 Information Engines 65 Deployment 73 Information Governance 74 Server Administration 76 Conclusion 78 References 78 Chapter 4  Social MDM Reference Architecture 81 Introduction 81 Architecture Overview 81 MDM as Central Nervous System for Enterprise Data 82 MDM: Architecture Overview 83 Component Model 87 Component Relationship Diagram from an Enterprise SOA Perspective 88 Component Relationship Diagram for Social MDM from an Information Architecture Perspective 89 Component Interaction Diagram 91 Subject-Oriented Integration 94 Conclusion 95 References 95 Chapter 5  Product Capabilities for Social MDM 97 Social Master Data Management (MDM) 99 Master Data Governance and Data Stewardship 100 Probabilistic Matching Engine (PME) 102 Social MDM Matching 104 InfoSphere BigInsights Architecture 106 Connectivity, Integration, and Security 108 Infrastructure 112 Analytics and Discovery 115 InfoSphere MDM and BigInsights Integration 119 IBM Watson Explorer Integration with BigInsights and Streams 120 Trusted Information Integration 121 InfoSphere Information Server 122 InfoSphere DataStage Balanced Optimization for Hadoop 124 Real-Time Data Processing 125 Pervasive Analytics Capabilities 127 References 129 Chapter 6  Social MDM and Customer Care 133 Gauging Social Media Data 133 Customer Centricity 135 Moving Toward Social Customer Centricity 135 Social Customer Care Reference Model 136 Customer Lifetime View 140 Next Best Action (NBA) 142 NBA Technology Components 143 NBA Solution Architecture 143 Sentiment Analytics 147 Scope of Sentiment Analytics 147 Solution Capabilities 148 MDM and Sentiment Analytics Scenario 148 Social Influencer Determination 150 Solution Capabilities 151 Key Concepts and Methodology 152 Social Network Analytics 154 Types of Social Networks 154 Insight Derived from Social Networks 157 Trustworthiness of Social Media for Customer Care 158 References 161 Chapter 7  Social MDM and Marketing 165 Social Media Marketing and the Role of MDM 166 Social Media–Enabled Marketing Campaigns 169 Contextual Marketing: Location and Time 172 Social Media Marketing 173 Mobile Marketing 176 Viral Marketing 178 Interest Groups 184 Summary 187 References 188 Chapter 8  Mobile MDM 191 Evolution of Interaction with Consumers 191 Master Data and the Mobile Revolution 193 Combining Location and Sensor Data with Master Data 193 Empowering Knowledge Workers on the Go: Data Stewardship 195 IT Impact of Mobile MDM 195 Architecture Overview for Mobile MDM in the Banking Industry 196 IBM MobileFirst 197 Mobile Banking Applications 198 IT Impact of a Mobile Channel 200 Security 204 Conclusion 204 References 205 Chapter 9  Future Trends in MDM 207 Entity Resolution and Matching 208 Semantic MDM 209 Ethics of Information 214 Explore and Analyze 219 Decide and Act 220 An Ethical Framework 221 Conclusion 223 References 223 Index 225    

About the Author :
Martin Oberhofer works as Executive Architect in the area of Enterprise Information Architecture with large clients world-wide. He helps customers to define their Enterprise Information Strategy and Architecture solving information-intense business problems. His areas of expertise include master data management based on an SOA, data warehousing, Big Data solutions, information integration, and database technologies. Martin delivers Enterprise Information Architecture and Solution workshops to large customers and major system integrators and provides expert advice in a lab advocate role for Information Management to large IBM clients. He started his career at IBM in the IBM Silicon Valley Labs in the United States at the beginning of 2002 as a software engineer and is currently based in the IBM Research and Development Lab in Germany. Martin co-authored the books Enterprise Master Data Management: An SOA Approach to Managing Core Information (IBM Press, 2008) and The Art of Enterprise Information Architecture: A Systems-Based Approach for Unlocking Business Insight (IBM Press, 2010) as well as numerous research articles and developerWorks articles. As inventor, he contributed to more than 70 patent applications for IBM and received the IBM Master Inventor title. Martin is certified by The Open Group as a Distinguished Architect and holds a master’s degree in mathematics from the University of Constance/ Germany.   Eberhard Hechler is an Executive Architect who works out of the IBM Boeblingen R&D Lab in Germany. He is currently on a three-year assignment to IBM Singapore, working as the Lead Architect in the Communications Sector of IBM’s Software Group. Prior to moving to Asia, he was a member of IBM’s Information Management “Integration and Solutions Engineering” development organization. After a two-and-a-half year international assignment to the IBM Kingston Development Lab in New York, he has worked in software development, performance optimization and benchmarking, IT/solution architecture and design, and technical consultancy. In 1992, he began to work with DB2 for MVS, focusing on testing and performance measurements. Since 1999, he has concentrated on Information Management and DB2 on distributed platforms. His main expertise includes the areas of relational database management systems, data warehouse and BI solutions, IT architectures and industry solutions, information integration, and Master Data Management (MDM). He has worked worldwide with communication service providers and IBM clients from other industries. Eberhard Hechler is a member of the IBM Academy of Technology, the IBM InfoSphere Architecture Board, and the IBM Asset Architecture Board. He coauthored the books Enterprise Master Data Management (IBM Press, 2008) and The Art of Enterprise Information Architecture: A Systems-Based Approach for Unlocking Business Insight (IBM Press, 2010). He holds a master’s degree (Diplom-Mathematiker) in Pure Mathematics and a bachelor’s degree (Diplom-Ingenieur (FH)) in Electrical Engineering (Telecommunications).   Ivan Milman is a Senior Technical Staff Member at IBM working as a security and governance architect for IBM’s Master Data Management (MDM) and InfoSphere product groups. Ivan co-authored the leading book on MDM: Enterprise Master Data Management: SOA Approach to Managing Core Information (IBM Press, 2008). Over the course of his career, Ivan has worked on a variety of distributed systems and security technology, including OS/2® Networking, DCE, IBM Global Sign-On, and Tivoli® Access Manager. Ivan has also represented IBM to standards bodies, including The Open Group and IETF. Prior to his current position, Ivan was the lead architect for the IBM Tivoli Access Manager family of security products. Ivan is a member of the IBM Academy of Technology and the IBM Data Governance Council. Ivan is a Certified Information Systems Security Professional and a Master Inventor at IBM, and has been granted 14 U.S. patents. Ivan’s current focus is the integration of InfoSphere technology, including reference data management, data quality and security tools, and information governance processes.   Scott Schumacher, Ph.D., is an IBM Distinguished Engineer, the InfoSphere MDM Chief Scientist, and a technology expert specializing in statistical matching algorithms for healthcare, enterprise, and public sector solutions. For more than 20 years, Dr. Schumacher has been heavily involved in research, development, testing, and implementation of complex data analysis solutions, including work commissioned by the Department of Defense. As chief scientist, Scott is responsible for the InfoSphere MDM product architecture. He is also responsible for the research and development of the InfoSphere Initiate matching algorithms, and holds multiple patents in the entity resolution area. Scott has a Bachelor of Science degree in Mathematics from the University of California, Davis, and received his Master of Arts and Doctorate degrees in Mathematics from the University of California, Los Angeles (UCLA). He is currently a member of the Institute for Mathematical Statistics, the American Statistical Association, and IEEE.   Dan Wolfson is an IBM Distinguished Engineer and the chief architect/CTO for the Info- Sphere segment of the IBM Information Management Division of the IBM Software Group. He is responsible for architecture and technical leadership across the rapidly growing areas of Information Integration and Quality for Big Data including Information Quality Tools, Information Integration, Master Data Management, and Metadata Management. Dan is also CTO for Cloud and Mobile within Information Management, working closely with peers throughout IBM. Dan has more than 30 years of experience in research and commercial distributed computing, covering a broad range of topics including transaction and object-oriented systems, software fault tolerance, messaging, information integration, business integration, metadata management, and database systems. He has written numerous papers, blogs, and is the coauthor of Enterprise Master Data Management: An SOA Approach to Managing Core Business Information (IBM Press, 2008). Dan is a member of the IBM Academy of Technology Leadership Team and an IBM Master Inventor. In 2010, Dan was also recognized by the Association of Computing Machinery (ACM) as an ACM Distinguished Engineer.