Big Data Governance An Emerging Imperative

ISBN-10: 1583473777

ISBN-13: 9781583473771

Edition: N/A

Authors: Sunil Soares

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Description:

Written by a leading expert in the field, this account focuses on the convergence of two major trends in information management—big data and information governance—by taking a strategic approach oriented around business cases and industry imperatives. With the advent of new technologies, enterprises are expanding and handling very large volumes of data; this book, nontechnical in nature and geared toward business audiences, encourages the practice of establishing appropriate governance over big data initiatives and addresses how to manage and govern big data, highlighting the relevant processes, procedures, and policies. It teaches readers to understand how big data fits within an overall information governance program; quantify the business value of big data; apply information governance concepts such as stewardship, metadata, and organization structures to big data; appreciate the wide-ranging business benefits for various industries and job functions; sell the value of big data governance to businesses; and establish step-by-step processes to implement big data governance.
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Book details

List price: $65.95
Publisher: MC Press, LLC
Publication date: 1/1/2013
Binding: Paperback
Pages: 368
Size: 7.00" wide x 9.00" long x 1.00" tall
Weight: 1.298
Language: English

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