Big Data
The Business Proposition
1st Edition
Published on 28. October 2014
Book
Paperback/Softback
325 pages
978-1-4302-5737-0 (ISBN)
Description
Big Data: The Business Proposition gives business professionals the knowledge and insight they need to create business value by applying the capabilities of big data technology to the opportunities and challenges of their businesses in ways that are appropriate to the existing resources and strategic potential of their business units and IT shops. In particular, this book teaches you what you need to understand about the tools of big data technology in order to deploy them strategically to achieve business value. These tools include not only those of the MapReduce/Hadoop ecosystems, but also new technologies for the compression,analysis, and interactive visualization of big data which are boosting the power of business analytics.
Authors Belanger and Pittenger-who were respectively Chief Scientist and CIO at AT&T-walk you through the business applications of big data techniques for mining relational data produced by communication networks ,social networks, and communities of interest. They proceed to examine the challenges, technology, and tools-especially MapReduce, Hadoop, and many other NoSQL data management tools, many of which are open source-that are required to work at big data scale on the semi-structured and unstructured data of text analysis, speech recognition, video analysis, and machine learning. They consider the critical governance and security issues raised when an enterprise ventures into the big data space. They speculate on the future business value of emergent trends such as Big-Data-as-a-Service open platforms. Finally, they present a road map and action plan for companies small and large who see a competitive need to set in motion changes to their organization that will deliver maximum ROI from applying big data techniques to their proprietary and public databases.
Authors Belanger and Pittenger-who were respectively Chief Scientist and CIO at AT&T-walk you through the business applications of big data techniques for mining relational data produced by communication networks ,social networks, and communities of interest. They proceed to examine the challenges, technology, and tools-especially MapReduce, Hadoop, and many other NoSQL data management tools, many of which are open source-that are required to work at big data scale on the semi-structured and unstructured data of text analysis, speech recognition, video analysis, and machine learning. They consider the critical governance and security issues raised when an enterprise ventures into the big data space. They speculate on the future business value of emergent trends such as Big-Data-as-a-Service open platforms. Finally, they present a road map and action plan for companies small and large who see a competitive need to set in motion changes to their organization that will deliver maximum ROI from applying big data techniques to their proprietary and public databases.
More details
Language
English
Place of publication
Berkley
United States
Target group
Professional and scholarly
Popular/general
Dimensions
Height: 229 mm
Width: 152 mm
ISBN-13
978-1-4302-5737-0 (9781430257370)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
David Belanger is a Senior Research Fellow at Stevens Institute of Technology. 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 took his MS and PhD in mathematics from Case Western Reserve University.
Content
Chapter 1. The Data Revolution in Business: Getting Your Head in the Game
Most books on big data are tactical in nature. This book is strategic: it gives you, the business professional, the knowledge and insight you need to create business value by applying big data technology capabilities to the opportunities and challenges that are unique to your business in ways that are appropriate to the existing resources and strategic potential of your business unit and IT shop.
Chapter 2. Big Data Technology: Scoping the Business Landscape
Fundamental to the evolution of big data is the ongoing development of new tools adequate to process and integrate data in its present scale, volume, and variety. Some of these tools, such as MapReduce and Hadoop, are well known, broadly adopted, and extensively developed to support ecosystems and stacks of complementary tools. Others-such as new technologies for compression, analysis, and interactive visualization-are less widely touted, but they figure prominently in this book as tools for extracting business value from big data. This chapter teaches business professionals what they need to understand about the tools of big data technology in order to deploy them strategically to achieve business value.
Chapter 3. Big Data Techniques: Optimizing Business Processes Instead of Expanding Silos
Every business operates as a collection of major processes in providing products/services. These processes span a corresponding collection of data silos, often represented by individual data warehouses. The big-data approach is not to create bigger data warehouses, but to organize data by major relational processes. There is exponentially more power in analyzing relationships among entities than in analyzing the entities themselves. You can think of this as analyzing the edges of a social network graph vs. analyzing the individual nodes. Yet, from an application point of view, two of the highest-im
Most books on big data are tactical in nature. This book is strategic: it gives you, the business professional, the knowledge and insight you need to create business value by applying big data technology capabilities to the opportunities and challenges that are unique to your business in ways that are appropriate to the existing resources and strategic potential of your business unit and IT shop.
Chapter 2. Big Data Technology: Scoping the Business Landscape
Fundamental to the evolution of big data is the ongoing development of new tools adequate to process and integrate data in its present scale, volume, and variety. Some of these tools, such as MapReduce and Hadoop, are well known, broadly adopted, and extensively developed to support ecosystems and stacks of complementary tools. Others-such as new technologies for compression, analysis, and interactive visualization-are less widely touted, but they figure prominently in this book as tools for extracting business value from big data. This chapter teaches business professionals what they need to understand about the tools of big data technology in order to deploy them strategically to achieve business value.
Chapter 3. Big Data Techniques: Optimizing Business Processes Instead of Expanding Silos
Every business operates as a collection of major processes in providing products/services. These processes span a corresponding collection of data silos, often represented by individual data warehouses. The big-data approach is not to create bigger data warehouses, but to organize data by major relational processes. There is exponentially more power in analyzing relationships among entities than in analyzing the entities themselves. You can think of this as analyzing the edges of a social network graph vs. analyzing the individual nodes. Yet, from an application point of view, two of the highest-im