About the Book
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.
