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| Foreword | |
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| Foreword | |
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| Preface | |
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| Getting Started | |
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| An Introduction to Big Data Governance | |
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| The Big Data Governance Framework | |
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| Big Data Types | |
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| Information Governance Disciplines | |
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| Industry and Functional Scenarios for Big Data Governance | |
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| Summary | |
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| The Maturity Assessment | |
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| The IBM Information Governance Council Maturity Model | |
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| Sample Questions to Assess Maturity | |
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| Summary | |
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| The Business Case | |
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| Improve On-Time Performance and Passenger Safety Through Big Data Governance | |
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| Quantify the Financial Impact of Big Data Governance on Customer Privacy | |
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| Reduce IT Costs by Governing the Lifecycle of Big Data | |
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| Estimate the Impact of Data Quality and Master Data on Big Data Initiatives | |
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| Summary | |
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| The Roadmap | |
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| The Roadmap Case Studies | |
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| Summary | |
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| Big Data Governance Disciplines | |
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| Organizing for Big Data Governance | |
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| Map Key Processes and Establish a RACI Matrix to Identify Stakeholders in Big Data Governance | |
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| Determine the Appropriate Mix of New and Existing Roles for Information Governance | |
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| Appoint Big Data Stewards as Appropriate | |
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| Add Big Data Responsibilities to Traditional Information Governance Roles as Appropriate | |
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| Establish a Merged Information Governance Organization with Responsibilities That Include Big Data | |
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| Summary | |
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| Metadata | |
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| Establish a Glossary That Represents the Business Definitions for Key Big Data Terms | |
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| Understand the Ongoing Support for Metadata Within Apache Hadoop | |
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| Tag Sensitive Big Data Within the Business Glossary | |
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| Import Technical Metadata from the Relevant Big Data Stores | |
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| Link the Relevant Data Sources to the Terms in the Business Glossary | |
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| Leverage Operational Metadata to Monitor the Movement of Big Data | |
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| Maintain Technical Metadata to Support Data Lineage and Impact Analysis | |
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| Gather Metadata from Unstructured Documents to Support Enterprise Search | |
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| Extend Existing Metadata Roles to Include Big Data | |
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| Summary | |
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| Big Data Privacy | |
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| Identify Sensitive Big Data | |
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| Flag Sensitive Big Data Within the Metadata Repository | |
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| Address Privacy Laws and Regulations by Country, State, and Province | |
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| Manage Situations Where Personal Data Crosses International Boundaries | |
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| Monitor Access to Sensitive Big Data by Privileged Users | |
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| Summary | |
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| Big Data Quality | |
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| Work with Business Stakeholders to Establish and Measure Confidence Intervals for the Quality of Big Data | |
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| Leverage Semi-Structured and Unstructured Data to Improve the Quality of Sparsely Populated Structured Data | |
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| Use Streaming Analytics to Address Data Quality Issues In-Memory Without Landing Interim Results to Disk | |
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| Appoint Data Stewards Accountable to the Information Governance Council for Improving the Metrics Over Time | |
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| Summary | |
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| Business Process Integration | |
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| Identify the Key Processes That Will Be Impacted by Big Data Governance | |
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| Build a Process Map with Key Activities | |
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| Map Big Data Governance Policies to the Key Steps in the Process | |
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| Summary | |
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| Master Data Integration | |
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| Improve the Quality of Master Data to Support Big Data Analytics | |
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| Leverage Big Data to Improve the Quality of Master Data | |
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| Improve the Quality and Consistency of Key Reference Data to Support the Big Data Governance Program | |
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| Consider Social Media Platform Policies to Determine the Level of Integration with Master Data Management | |
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| Extract Meaning from Unstructured Text to Enrich Master Data | |
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| Summary | |
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| Managing the Lifecycle of Big Data | |
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| Expand the Retention Schedule to Include Big Data Based on Local Regulations and Business Needs | |
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| Document Legal Holds and Support eDiscovery Requests | |
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| Compress and Archive Big Data to Reduce IT Costs and Improve Application Performance | |
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| Manage the Lifecycle of Real-Time, Streaming Data | |
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| Retain Social Media Records to Comply with Regulations and Support eDiscovery Requests | |
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| Defensibly Dispose of Big Data No Longer Required Based on Regulations and Business Needs | |
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| Summary | |
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| The Governance of Big Data Types | |
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| Web and Social Media | |
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| Consider Evolving Regulations and Customs When Establishing Policies Regarding the Acceptable Use of Social Media Data About Customers | |
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| Set Up Policies Regarding the Acceptable Use of Social Media Data About Employees and Job Candidates | |
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| Leverage Confidence Intervals to Assess the Quality of Social Media Data | |
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| Establish Policies Regarding the Acceptable Use of Cookies and Other Web Tracking Devices | |
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| Define Policies to Link Online and Offline Data in a Way That Does Not Violate Privacy Concerns and Regulations | |
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| Ensure the Consistency of Web Metrics | |
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| Summary | |
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| Machine-to-Machine Data | |
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| Assess the Types of Geolocation Data Currently Available | |
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| Establish Policies Regarding the Acceptable Use of Geolocation Data Pertaining to Customers | |
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| Establish Policies Regarding the Acceptable Use of Geolocation Data Pertaining to Employees | |
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| Ensure the Privacy of RFID Data | |
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| Define Policies Relating to the Privacy of Other Types of M2M Data | |
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| Address the Metadata and Quality of M2M Data | |
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| Establish Policies Regarding the Retention Period for M2M Data | |
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| Improve the Quality of Master Data to Support M2M Initiatives | |
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| Secure the SCADA Infrastructure from Vulnerability to Cyber Attacks | |
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| Summary | |
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| Big Transaction Data | |
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| Summary | |
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| Biometrics | |
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| Assess the Privacy Implications Relating to the Acceptable Use of Biometric Data | |
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| Work with Legal Counsel to Determine the Impact of Evolving Regulations on the Use of Biometric Data for Customers and Employees | |
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| Summary | |
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| Human-Generated Data | |
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| Establish Policies to Mask Sensitive Human-Generated Data | |
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| Use Unstructured Human-Generated Data to Improve the Quality of Structured Data | |
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| Manage the Lifecycle of Human-Generated Data to Reduce Costs and Comply with Regulations | |
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| Extract Insights from Unstructured Human-Generated Data to Enrich MDM | |
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| Summary | |
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| Industry Perspectives | |
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| Healthcare | |
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| Leverage Unstructured Data to Improve the Quality of Sparsely Populated Structured Data | |
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| Extract Additional Relevant Clinical Factors Not Available Within Structured Data | |
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| Define Consistent Definitions for Key Business Terms | |
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| Ensure Consistency in Patient Master Data Across Facilities | |
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| Adhere to Privacy Requirements for Protected Health Information in Accordance with HIPAA | |
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| Creatively Manage Reference Data to Yield Effective Clinical Insights | |
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| Summary | |
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| Utilities | |
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| Duplicate Meter Readings | |
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| Referential Integrity of the Primary Key | |
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| Anomalous Meter Readings | |
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| Data Quality for Customer Addresses | |
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| Information Lifecycle Management | |
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| Database Monitoring | |
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| Technical Architecture | |
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| Summary | |
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| Communications Service Providers | |
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| Big Data Types | |
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| Integrating Big Data with Master Data | |
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| Big Data Privacy | |
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| Big Data Quality | |
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| Big Data Lifecycle Management | |
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| Summary | |
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| Big Data Technology | |
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| Big Data Reference Architecture | |
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| Big Data Sources | |
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| Open Source Foundational Components | |
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| Hadoop Distributions | |
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| Streaming Analytics | |
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| Databases | |
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| Big Data Integration | |
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| Text Analytics | |
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| Big Data Discovery | |
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| Big Data Quality | |
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| Metadata for Big Data | |
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| Information Policy Management | |
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| Master Data Management | |
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| Data Warehouses and Data Marts | |
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| Big Data Analytics and Reporting | |
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| Big Data Security and Policy | |
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| Big Data Lifecycle Management | |
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| The Cloud | |
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| Summary | |
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| Big Data Platforms | |
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| IBM | |
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| Oracle | |
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| SAP | |
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| The Microsoft Big Data Platform | |
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| HP | |
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| Informatica | |
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| SAS | |
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| Teradata | |
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| EMC | |
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| Amazon | |
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| Google | |
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| Pentaho | |
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| Talend | |
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| Summary | |
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| List of Acronyms | |
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| Glossary | |
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| Reviewer Profiles | |
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| Contributor Profiles | |
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| Index | |