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IBM InfoSphere
preview of book IBM InfoSphere
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IBM InfoSphere

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Publisher: MC Press Online
Publication Date: February 2013
Subject: Computer: Database & Data Management
Number of Pages: 113

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About this title
In IBM InfoSphere: A Platform for Big Data Governance and Process Data Governance, Sunil Soares provides a big data governance framework with three dimensions:
  • Big data types--Web and social media, machine-to-machine, big transaction data, biometrics, and human-generated
  • Information governance disciplines--The traditional disciplines of information governance also apply to big data. These disciplines are organization, metadata, privacy, data quality, business process integration, master data integration, and information lifecycle management
  • Industries and functions--Big data analytics are driven by use cases that are specific to a given industry or function.
Use the knowledge presented in this book to understand big data support across the IBM InfoSphere portfolio, including DataStage, Streams, QualityStage, MDM, Guardium, Optim, and Data Explorer. Understand the integration between IBM Business Process Manager Express and IBM InfoSphere in terms of integrating information governance, big data, and business process management.
About author
Sunil Soares
Sunil Soares is the founder and managing partner of Information Asset, LLC, a consulting firm that specializes in data governance. Prior to this role, Sunil was director of information governance at IBM, where he worked with clients across six continents and multiple industries. Before joining IBM, Sunil consulted with major financial institutions at the Financial Services Strategy Consulting Practice of Booz Allen & Hamilton in New York. Sunil lives in New Jersey and holds an MBA in Finance and Marketing from the University of Chicago Booth School of Business.

The Chief Data Officer Handbook for Data Gocernance is Sunil's fifth book about data governance. His first book, The IBM Data Governance Unified Process, details the almost 100 steps to implement a data governance program. This book has been used by several organizations as the blueprint for their data governance programs and has been translated into Chinese. Sunil's second book, Selling Information Governance to the Business, reviews the best practices to approach information governance by industry and function. Sunil's third book, IBM InfoSphere: A Platform for Big Data Governance and Process Data Governance, focuses on IBM's InfoSphere product. Sunil's fourth book, Big Data Governance, addresses the specific issues associated with the governance of big data.

Contents
Contents
Foreword by David Corrigan
Foreword by Inderpal Bhandari

PART I: Big Data Integration and Governance with IBM InfoSphere
Chapter 1: An Introduction to Big Data Governance

Chapter 2: The Big Data Governance Framework
2.1 Big Data Types
2.2 Information Governance Disciplines
2.3 Industry and Functional Scenarios for Big Data Governance

Chapter 3: The IBM Big Data Platform
3.1 IBM Big Data Products
3.2 IBM Big Data Platform Differentiators

Chapter 4: Big Data Integration
4.1 Bulk Data Movement
4.2 Data Replication
4.3 Data Virtualization

Chapter 5: Metadata
5.1 Establish a Glossary That Represents the Business Definitions for Key Big Data Terms
5.2 Tag Sensitive Big Data Within the Business Glossary
5.3 Maintain Technical Metadata to Support Data Lineage and Impact Analysis
5.4 Gather Metadata from Unstructured Documents to Support Enterprise Search

Chapter 6: Big Data Security and Privacy
6.1 Identify Sensitive Big Data
6.2 Flag Sensitive Big Data Within the Metadata Repository
6.3 Mask Sensitive Big Data in Production and Non-Production Environments
6.4 Monitor Access to Sensitive Big Data by Privileged Users

Chapter 7: Big Data Quality
7.1 Leverage Semi-Structured and Unstructured Data to Improve the Quality of Sparsely Populated Structured Data
7.2 Use Streaming Analytics to Address Data Quality Issues In-Memory Without Landing Interim Results to Disk
7.3 Cleanse Big Data Before or After Processing in Hadoop

Chapter 8: Master Data Integration
8.1 Improve the Quality of Master Data to Support Big Data Analytics
8.2 Leverage Big Data to Improve the Quality of Master Data
8.3 Improve the Quality and Consistency of Key Reference Data to Support the Big Data Governance Program
8.4 Extract Meaning from Unstructured Text to Enrich Master Data
8.5 Enrich Customer Master Data with Insights from Social Media to Create Social MDM
8.6 Turbo-Charge MDM with Hadoop Technologies

Chapter 9: Managing the Lifecycle of Big Data
9.1 Expand the Retention Schedule to Include Big Data Based on Local Regulations and Business Needs
9.2 Document Legal Holds and Support eDiscovery Requests
9.3 Compress and Archive Big Data on Hadoop to Reduce Storage Costs
9.4 Archive Big Data in Immutable Format with Seamless Access to Hadoop for Analytics
9.5 Manage the Lifecycle of Real-Time, Streaming Data
9.6 Defensibly Dispose of Big Data No Longer Required Based on Regulations and Business Needs

PART II: Process Data Governance with IBM InfoSphere
Chapter 10: An Introduction to Process Data Governance
Chapter 11: Retail Case Study: Process Data Governance of Social Media
Chapter 12: Oil and Gas Case Study: Process Data Governance of Sensor Data

Chapter 13: Healthcare Case Study: Process Data Governance of Big Claims Transaction Data
13.1 A Primer on Claim Codes Used by Health Plans

Notes
Appendix: Reviewer and Contributor Profiles
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