Big Data Opportunities, Challenges and Solutions for Industry Verticals

Big Data Opportunities, Challenges and Solutions for Industry Verticals

$1,995.00

Publication ID
MIC1013004
Publication Date
October 4, 2013
Pages
120
Regions Covered
Publisher
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.
 
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
 
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
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
  1. Action
  2. Aetna
  3. Amazon
  4. Amazon Web Services
  5. Apache
  6. apigee
  7. Bloomberg
  8. BloomReach
  9. Capgemini
  10. Computer Science Corp
  11. Craigslist
  12. Data Mining Research
  13. Dataguise
  14. Datameer
  15. Ebizq
  16. EMC
  17. Facebook
  18. Forbes
  19. Forrester
  20. General Electric
  21. Globe Telecom
  22. Google
  23. HP
  24. IBM
  25. IDC
  26. Instagram
  27. Intel
  28. Ironside
  29. LinkedIn
  30. Mashery
  31. McKinsey Global
  32. Microsoft
  33. Oracle
  34. Orkut
  35. PadMapper
  36. Pinterest
  37. Programmable Web
  38. RainStor
  39. Riak
  40. SAS
  41. SpotFire
  42. Tata Consultancy Services
  43. Twitter
  44. Walmart
  45. Watalon
  46. Wikipedia
  47. Yahoo
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