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

Overview

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 ...

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

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Overview

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
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Product Details

  • ISBN-13: 9780133509809
  • Publisher: IBM Press
  • Publication date: 11/7/2014
  • Series: IBM Press Series
  • Edition number: 1
  • Pages: 272

Meet 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.

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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

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