David Belanger, Rashmi Jain

Big Data: Planning Your Big Data Strategy

• Broschiertes Buch

Produktbeschreibung

Big Data, the Second Generation gives business professionals the basic technological understanding, analytic tools, and diagnostic checklists they need to identify and plan out the big data strategy most appropriate to their particular company. This book examines the technologies and tools that are required to work at big data scale on the semi-structured and unstructured data of text analysis, speech recognition, video analysis, machine learning, and more.

We are fast entering the second generation of big data: namely, big data that is accessible to nearly all organizations, as opposed to a few big companies. This book systematically maps the MapReduce and Apache Hadoop 2 ecosystems to the second-generation array of big data business applications, provides you the guidelines to match your company's profile with suitable applications, and shows you how to boost the power of your business analytics with new technologies for the compression, visualization, and actionable analysis of big data.

The authors draw their insights into the second generation of big data from deep experience in industry as well as research. David Belanger is the co-leader of the IEEE Big Data Initiative and was previously the Chief Scientist of AT&T Labs. Rashmi Jain is her university's representative in the New Jersey Big Data Alliance and was previously an IT systems architect for Accenture.
Belanger and Jain walk you through the business applications of big data techniques for mining data produced by communication networks, social networks, and communities of interest. They consider the critical governance and security issues raised when an enterprise ventures into the big data space. They forecast the business value of emergent trends such as big-data-as-a-service open platforms. Finally, they present an action plan for executives and managers in companies large and small that will deliver maximum ROI from applying big data techniques to proprietary and public databases.

A strategic planning guide rather than a technical implementation manual, Big Data, the Second Generation: Planning Your Big Data Strategy equips you to chart your organization's roadmap to harnessing big data and big analytics to beat your competition in the race to higher topline sales and a fatter bottom line.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Apress / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4842-1014-7
• 1st ed.
• Seitenzahl: 325
• Erscheinungstermin: 1. Dezember 2015
• Englisch
• Abmessung: 229mm x 152mm
• ISBN-13: 9781484210147
• ISBN-10: 148421014X
• Artikelnr.: 42715886

Autorenporträt

David Belanger is a Senior Research Fellow at Stevens Institute of Technology. In October 2014, he was appointed to the co-leadership of the three-year IEEE Big Data Initiative. He leads the Center for Big Data innovation at Stevens and is Stevens' representative to the New Jersey Big Data Alliance. In August 2012, he retired from AT&T Labs where he was Chief Scientist and Vice President of Information, Software, & Systems Research of AT&T Shannon Labs. He built the Software Engineering Research Department which provided software tools and techniques used across AT&T Bell Labs and via open source across the world. He was the creator of the AT&T InfoLab, an organization aimed at optimizing the value gained from data within AT&T and a pioneer of Big Data research and practice. Belanger was awarded the AT&T Science and Technology Medal for his contributions in very large scale information mining technology and was named an AT&T Fellow for "lifetime contributions in software, software tools, and information mining." He received the Institute of Electrical and Electronic Engineers (IEEE) Communications Society Industrial Innovator Award and the Distinguished Engineer Award from the Association for Computing Machinery (ACM). As part of the Obama administration, Dr. Belanger chaired the Tech America Commercial Policy Board from 2011 to 2012. He is the author of the opening chapter of Big Data and Business Analytics (ed. Jay Liebowitz), "Architecting the Enterprise via Big Data Analytics." He took his MS and PhD in mathematics from Case Western Reserve University.

Inhaltsangabe

Chapter 1. The Trajectory of Big Data Technology and the Big Analytics Culture

Scaling Bigness: The Virtue of Brevity vs. the Vice of Vogue
Expanding the Playing Field: The Arc from the First to the Second Generation of Big Data
Spreading the Analytics Culture: Smart Devices, Embedded Technologies, and Better Decisions
Chapter 2. The Heart of Big Data

Six Classic Application Types at the Heart of Big Data

Metasearch
Fraud Detection and Security
Customer Experience and Marketing
Speech and Call Centers
Machine Translation
Location
Sources of Big Data

The World Wide Web
The Mobile Crowd
Operations
Experiments
Transactions
Sensors and Actuators
Manufacturing Processes
Types of Big Data

Structured
Semi-Structured Chapter 3. Application Changes Driven by Big Data

Granularity
Rare Events
Relationships
Personalization
Transparency
Latency
Chapter 4. Evaluating What Big Data to Capture and Mine in Different Industries

Finance
Telecom
Media and Retail
E-Commerce
Healthcare
Government
Chapter 5. The Twin Engines of Big Data: Analysis and Visualization

The Large View: The Confluence of Scale, Commodity Hardware, Parallelism, and Data Scientists
The Triad of Data Mining, Analytics, and Visualization
The MapReduce Family and the Hadoop Ecosystem
Graphs and Relationships
Learning
Fundamentals of Visualization: Thinking about Pictures and Thinking with Your Eyes
Visualization at Scale
Chapter 6. Managing All of That Data

Data Management Today: Data Warehouses, Data Marts, RDBMS, and Ad Hoc Systems
Next-Generation Databases

Column Store
Document, Text, Speech, and Video
Integration
Data Stream Management Systems: When Time Is of the Essence
Chapter 7. Architecting the Enterprise for Big Analytics

The Role of Systems Architecture for Big Data and Analytics
IT Infrastructural Readiness
IT Governance for Big Data and Business Analytics
Chapter 8. The Organizational Challenges of Transitioning an Enterprise to Big Data Functionality

The Challenge of Integrating Big Data Capabilities with Legacy Systems and Business Processes
The Challenge of Ramping Up to Big Data 2.0
Big Data Professionals: The Expanding Universe of Skill Sets and Competencies
Chapter 9. Governance: The Sine Qua Non of Big Data

Principles
Policy
Processes
Practices
Challenges
Chapter 10. The Meta-Issues of Big Data Governance

Data Integrity and Quality
Data Security
Privacy
Operations, Administration, and Management
Data Retention
Chapter 11. Convergent Trends toward Meta-Integration of Canonical Application Architectures

Integrating Metasearch, Forensics, and UX
Integrating Call Centers, Machine Translation, Geolocation, and Automated Marketing
Chapter 12. Big Data 3.0: Critical Conditions for the Coming Big Bang

Open Data
Data as a Service
Internet of Things
Automatic Control Proliferation
Chapter 13. Preliminaries for Framing Out the Role of Big Data in Your Organization

Down to Cases: Thinking Out Your Organization's Transition to Second-Generation Big Data
Finessing Your Organization's Structure, Processes, and Culture for Smart Big Data Integration