Big Data Opportunities, Challenges and Solutions for Industry Verticals

 Published On: Aug, 2013 |    No of Pages: 120 |  Published By: Mind Commerce | Format: PDF
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Overview:


Big data is more than just one of the biggest buzz words in years. It represents a huge business opportunity to leverage arguably the most valuable enterprise asset: data about customers, operations, markets, competitors, and more.


Organizations across nearly every industry find that they not only require to manage growing large data volumes in their real-time systems, but also to analyze that information so they can quickly make more optimal decisions to help them compete more effectively in the marketplace.


Companies across a wide range of industry verticals and market segments are beginning to leverage Big Data and analytics to produce insights from hidden information floating in a sea of raw data that is otherwise too costly to process and discover.


Report Benefits:


Learn about Big Data solutions and strategies for enterprise

Understand the challenges and benefits for enterprise Big Data

Identify the market opportunities for Big Data in industry verticals

Learn about Big Data and analytics vendor solutions and strategies


Target Audience:


Big Data companies

Governmental organizations

Telecommunications companies

Analytics and data reporting companies

Data storage and processing companies

Research and development organizations

Cloud infrastructure and service providers

All industry verticals and market segments 


Companies in Report: 


Action

Aetna 

Amazon

Amazon Web Services

Apache

apigee

Bloomberg

BloomReach

Capgemini

Computer Science Corp

Craigslist

Data Mining Research

Dataguise

Datameer

Ebizq

EMC

Facebook

Forbes

Forrester

General Electric

Globe Telecom 

Google

HP

IBM 

IDC

Instagram

Intel

Ironside

LinkedIn

Mashery

McKinsey Global 

Microsoft

Oracle

Orkut

PadMapper

Pinterest

Programmable Web

RainStor

Riak

SAS

SpotFire

Tata Consultancy Services 

Twitter

Walmart 

Watalon

Wikipedia

Yahoo

Table of Contents

1 Executive Summary 6


2 What is Big Data 6

2.1 Breaking down Big Data 7

2.1.1 Internal Data 8

2.1.2 Structured External Data 8

2.1.3 Unstructured External Data 8

2.2 The important 'V's of Big Data 9

2.3 The Big Deal about Big Data 11

2.3.1 Exponential growth of Big Data 11

2.3.2 What the Numbers Mean 12

2.3.3 The Chance for Companies to Thrive 12

2.4 How Big Is Big Data? 12

2.5 What Data Is Meaningful? 14

2.5.1 Operations Management data 14

2.5.2 Sales and Marketing data 15

2.5.3 Accounting and Finance data 15

2.6 Improved Technologies to Manage Data 15

2.6.1 Analytic relational systems 15

2.6.2 Non-relational systems 15


3 Big Problems to Solve 16

3.1 Better Investment Decision and Operational Changes 16

3.2 Real Time customization 17

3.3 Improved Performance and Risk Management 17

3.4 New Business Model 18


4 Uses for Big Data 18


5 Challenges in Big Data Analysis 20

5.1 Heterogeneity and Incompleteness 21

5.2 Scale 22

5.3 Timeliness 22

5.4 Privacy 23

5.5 Human Collaboration 23


6 Big Data vs. API Strategies 24

6.1 Structured and Unstructured Solutions: APIs 24


7 Big Data Ecosystem 26

7.1 Big Data Landscape 28


8 Big Data Architecture 28

8.1 Traditional Information Architecture Capabilities 29

8.2 Adding Big Data Capabilities 29


9 Big Data Sources: What and How Much? 31

9.1 Where the data is getting generated? 33


10 Big Data Generation and Analytics 36

10.1 Predictive Analytics 37

10.2 Data Scientists 38

10.3 Big Data Technologies and Techniques 39

10.3.1 Hadoop 39

10.3.1.1 Problems solved by Hadoop 40

10.3.1.2 Hadoop Architecture 40

10.3.1.3 Cost Benefits of Hadoop 40

10.3.2 Hadoop and Spark 41

10.3.3 Hadoop and Data Security 41

10.3.3.1 Hadoop's Architecture Presents Unique Security Issues 41

10.3.3.2 Deploy a Purpose-Built Security Solution for Hadoop and Big Data 42

10.3.3 MapReduce 42

10.3.3.1 Features 43

10.3.3.2 When to Use MapReduce 43

10.3.3.3 When Not to Use MapReduce 43

10.3.3.4 How it Works 44

10.4 Data Mining 44

10.5 CRM Systems 44

10.6 Social Media 45

10.6.1 Ways to Tap Social Media 46

10.6.1.1 Google Trends 46

10.6.1.2 APIs and Mashups 46

10.6.1.3 Communicate Intelligence with Data Visualization Tools 46


11 Data Management 46

11.1 Acquire Big Data 46

11.2 Organize Big Data 47

11.3 Analyze Big Data 47

11.4 Interpretation 48


12 Big Data Standardization 48

12.1 Alliance for Telecommunication Industry Solutions 50

12.1.1 Business Challenges for CSPs 50

12.1.2 Promise of Big Data for Telecom 51

12.1.3 Catch problem spots before they affect service. 51

12.1.4 Put Big Data to use immediately 51

12.1.5 Give internal and external teams the tools they need 51


13 Major Service Providers 51

13.1 IBM 52

13.2 Datameer 54

13.3 Amazon Web Services 56

13.4 HP- Big Data 58

13.5 SpotFire 60

13.6 Intel 62

13.7 EMC 64


14 Big Data in Industry Verticals 67

14.1 Finance and Accounting 67

14.2 Retail and CRM 69

14.3 Government and Defense 71

14.5 Healthcare 72

14.6 Supply chain Management 73

14.7 Telecommunication 74


15 Summary and Conclusion 75


16. Appendix 92

16.1 Deeper Dive into Big Data in Retail 92

16.1.1 Retail Merchant Challenges 92

16.1.2 Big Data in Retail 94

16.1.3 Big Data in Retail Business Case 99

16.1.4 Retailers Business Models, Companies, and Solutions 105

16.1.5 Supply Chain Solutions 111

16.2 Deeper Dive into Big Data in Healthcare 113

16.2.1 Big Issues with Healthcare 113

16.2.2 Healthcare Stakeholders 114

16.2.3 Opportunities and Challenges 116

16.3 Big Data Industry Verticals vs. Functional Areas 119

16.3.1 Industry Verticals 119

16.3.2 Functional Areas 119

16.3.3 Big Data Functional Area Forecast 2013 - 2019 120


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