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Big Data in ICT and Telecom: Transforming Industry Verticals 2013 - 2018

  • ID: 2755101
  • December 2013
  • Region: Global
  • 434 Pages
  • Mind Commerce LLC

FEATURED COMPANIES

  • 1010Data
  • Cloudera
  • Google
  • Intersystems
  • Parstream
  • Sas
  • MORE

Big Data is much more than its technical definition implies: A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tool. From a business perspective, Big Data represents a major inflection point for the ICT and Telecom sectors as it will transform business asset utility and value forever more. Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. However, value realization and the implications for using this data is often little understood and underappreciated.

This research evaluates Big Data challenges including management, mining, and analytics as well as the impact on telecom and ICT systems. This research also analyzes Big Data in key industry verticals including retail, financial services, healthcare, government, manufacturing, energy, and transportation. This report also assesses Big Data within government including homeland security, defense, and law enforcement. The report includes a market value assessment and forecasts for 2013 - 2018 for Big Data in telecommunications and READ MORE >

Note: Product cover images may vary from those shown

FEATURED COMPANIES

  • 1010Data
  • Cloudera
  • Google
  • Intersystems
  • Parstream
  • Sas
  • MORE

1.0 Executive Summary

2.0 Introduction
2.1 Data Types
2.1.1 Internal Data
2.1.2 External Data
2.2 Big Data
2.2.1 Key Characteristics Of Big Data
2.2.2 Distinguishing Characteristics Of Data Size

3.0 The Importance Of Big Data
3.1 Why Big Data?
3.2 Big Data Benefits
3.2.1 Better Investment Decision And Operational Changes
3.2.2 Real Time Customization
3.2.3 Improved Performance And Risk Management
3.2.4 New Business Models

4.0 The Big Data Environment
4.1 The Current State Of Industry Data And Analytics
4.1.1 Heterogeneity And Incompleteness
4.1.2 Scale
4.1.3 Timeliness
4.1.4 Privacy
4.1.5 Human Collaboration
4.2 Big Data Allows Enterprise To Uncover Opportunities
4.3 What Data Is Meaningful?
4.3.1 Operations Management Data:
4.3.2 Sales And Marketing Data:
4.3.3 Accounting And Finance Data:

5.0 Big Data In Telecom And Ict
5.1 How Much Data Is There In Telecom And Ict?
5.1.1 Exponential Growth
5.1.2 Putting The Amount Of Data Into Perspective
5.2 Opportunities To Telecom Carriers
5.2.1 Direct Benefits To Telecom
5.2.2 Benefits To Industry Verticals
5.2.3 Specific Opportunities
5.3 Challenges To Telecom Carriers
5.3.1 Planning For Big Data
5.3.2 Implementing Improved Technologies To Manage Data
5.4 Sources Of Data In Telecom
5.4.1 Subscriber Data
5.4.2 Network Data
5.4.3 Specific Carrier Systems
5.4.4 Sourcing Telecom Data And Privacy Concerns
5.5 Accessing Data Via Telecom Api
5.5.1 What Is An Api?
5.5.2 What Is A Telecom Api?
5.5.3 Accessing Data Over A Telecom Api
5.5.4 Future Of Carriers And Telecom Apis

6.0 Big Data Architecture
6.1 Traditional Information Architecture Capabilities
6.2 Big Data And The Cloud
6.3 Adding Big Data Capabilities

7.0 Big Data Technologies, Techniques, And Solutions
7.1 Technologies
7.1.1 Sensors And Sensor Networks
7.1.2 Networks Connection
7.1.3 Data Storage
7.1.4 Data Mining
7.1.5 Advanced Computing Systems
7.1.6 Data Analysis Algorithms
7.2 Big Data Techniques
7.2.1 A/B Testing
7.2.2 Association Rule Learning
7.2.3 Classification
7.2.4 Cluster Analysis
7.2.5 Crowd Sourcing
7.2.6 Data Fusion And Data Integration
7.2.7 Data Mining
7.2.8 Other Techniques
7.3 Big Data Solutions
7.3.1 Hadoop
7.3.2 Nosql
7.3.3 Mpp Databases
7.3.4 Other And Emerging Solutions

8.0 Big Data Sources, Capture, And Management
8.1 Acquiring Data
8.2 Big Data Sources
8.2.1 Entertainment Systems
8.2.2 Communications Systems
8.2.3 Social Networks
8.2.4 Shopping Activities
8.2.5 Sensor And Sensor Networks
8.2.6 Gamification
8.3 Capturing Big Data
8.3.1 Capturing Big Data In Commerce Activities
8.3.2 Capturing Big Data In Social Activities
8.3.1 Capturing Big Data In Lifestyle Activities
8.4 Big Data Management
8.4.1 Aggregation
8.4.2 Storage
8.4.3 Processing

9.0 Big Data Ecosystem And Value Chain
9.1 Big Data Value Chain
9.1.1 A Fragmented Big Data Value Chain
9.1.2 Value Chain Functions
9.1.3 Value Chain Goals
9.2 Big Data Ecosystem And Landscape
9.2.1 Emerging Data As A Service (Daas) Ecosystem
9.3 Leading Companies In Big Data
9.3.1 1010Data
9.3.2 Actian
9.3.3 Agilis International
9.3.4 Alteryx
9.3.5 Amanzitel
9.3.6 Amazon
9.3.7 Apache Software Foundation
9.3.8 Aptean
9.3.9 Attivio
9.3.10 Cataphora
9.3.11 Cisco
9.3.12 Cloudera
9.3.13 Csc
9.3.14 Cvidya
9.3.15 Datameer
9.3.16 Dell
9.3.17 Emc
9.3.18 Eplorys
9.3.19 Fujitsu
9.3.20 Fusion-Io
9.3.21 Gooddata
9.3.22 Google
9.3.23 Guavus
9.3.24 Hitachi Data Systems
9.3.25 Hortonworks
9.3.26 Hp
9.3.27 Humedica
9.3.28 Hitachi
9.3.29 Ibm
9.3.30 Informatica
9.3.31 Intel
9.3.32 Intersystems
9.3.33 Jaspersoft
9.3.34 Marklogic
9.3.35 Microsoft
9.3.36 Mongodb
9.3.37 Mu Sigma
9.3.38 Netapp
9.3.39 Opera Solutions
9.3.40 Oracle
9.3.41 Paraccel
9.3.42 Parstream
9.3.43 Pentaho
9.3.44 Pervasive
9.3.45 Platfora
9.3.46 Qliktech
9.3.47 Quantum
9.3.48 Rackspace
9.3.49 Revolution Analytics
9.3.50 Salesforce
9.3.51 Sap
9.3.52 Sas
9.3.53 Sisense
9.3.54 Software Ag/Terracotta
9.3.55 Splunk
9.3.56 Sqrrl
9.3.57 Subex
9.3.58 Supermicro
9.3.59 Tableau
9.3.60 Teoco
9.3.61 Teradata
9.3.62 Think Big Analytics
9.3.63 Tidemark Systems
9.3.64 Tibco
9.3.65 Vmware (Emc)
9.3.66 Wedo Technologies

10.0 Obstacles To Implementing And Operating Big Data
10.1 Organizational Challenges
10.1.1 Human Capital: The Need For Data Scientists
10.2 Data Challenges
10.2.1 Data Quality
10.2.2 Timely Data Delivery
10.2.3 Data Storage Capacity
10.3 Process Challenges
10.4 Privacy And Public Policy Issues
10.4.1 Commercialization Of Private Data
10.4.2 Privacy Rules And Regulations
10.4.3 Learning From Past Mistakes
10.5 Big Data Standardization
10.5.1 National Institute Of Standards And Technology
10.5.2 Alliance For Telecommunication Industry Solutions
10.5.3 Cloud Security Alliance
10.5.4 International Telecommunications Union
10.5.5 Open Mobile Alliance
10.5.6 De Facto Standardization Driven By Leading Companies

11.0 Big Data And Telecom Analytics
11.1 What Is Big Data And Telecom Analytics
11.1.1 Pattern Discovery
11.1.2 Predictive Analytics
11.1.1 Decision Making
11.1.2 Process Innovation
11.2 The Importance Of Analytics
11.2.1 From Analytics To Business Intelligence
11.2.2 The Importance Of Analytics In Telecom
11.2.3 Telecom Analytics Solutions
11.3 Challenges In Big Data Analysis
11.3.1 Heterogeneity And Incompleteness
11.3.2 Scale
11.3.3 Timeliness
11.3.4 Privacy
11.3.5 Human Collaboration
11.4 Evaluation Of Analytics Companies
11.4.1 Analytics Companies
11.4.2 Swot Analysis Of Analytics Providers

12.0 Other Aspects Of Big Data
12.1 Big Data Vs. Api Strategies
12.1.1 Structured And Unstructured Solutions: Apis
12.2 Big Data Vs. Small Data
12.2.1 What Is “Small Data”?
12.2.2 Why Pursue A Small Data Strategy?
12.2.3 Big Data Vs. Small Data Differentiation
12.2.4 Decision Parameters For Big Vs. Small Data
12.2.5 Big Data Vs. Small Data: The Key Differences
12.2.1 Small Data Driven Emerging Business Models

13.0 Big Data In Industry Verticals
13.1 Big Data And The Internet
13.1.1 Search
13.1.2 Digital Marketing And Commerce
13.2 Big Data In Financial Services
13.2.1 Why Big Data In Financial Services?
13.2.2 How Banks Are Leveraging Data
13.2.3 Big Data Challenges And Opportunities In Financial Services
13.2.4 Big Data In Financial Services Case Analysis
13.3 Big Data In Retail Sales And Customer Relationship Management
13.3.1 The Current State Of Retail
13.3.2 Emerging Technology Trends In Retail
13.3.3 Big Data In Retail
13.3.4 Big Data In Customer Relationship Management (Crm)
13.4 Big Data In Healthcare
13.4.1 Why Big Data In Healthcare?
13.4.2 Healthcare Data
13.4.3 Emerging Business Models With Big Data In Healthcare
13.4.4 Big Data Deployment Challenges In Healthcare
13.4.5 Big Data In Healthcare Stakeholders
13.5 Big Data In Manufacturing
13.5.1 Manufacturing Overview
13.5.2 Value Chain And Challenges Of Manufacturing
13.5.3 Market Drivers And Barriers For Big Data Applications In Manufacturing
13.5.4 Performance Measurement
13.5.5 Applications & Processes In Manufacturing
13.5.6 Current State Of Manufacturing
13.6 Big Data In Transportation Sector
13.6.1 Intelligent Transportation Systems
13.6.2 Intelligent Automobiles
13.6.3 Automobile Development, Assembly, And Distribution
13.7 Big Data In Energy And Smartgrid
13.7.1 Analyzing Data In The Energy Sector
13.7.2 Big Data And Smartgrid
13.7.3 Big Companies Help Utilities Solve Big Problems With Big Data
13.7.4 Identifying Problems Is The Start To Solving Them

14.0 Big Data In Government
14.1 Steps The Government Is Taking Towards Leveraging Big Data
14.1.1 Recognizing Problems And Opportunities
14.1.2 Launching Initiatives
14.1.3 Identifying Inputs To The Problems
14.1.4 Identifying Solutions
14.1.5 Recognizing Challenges
14.2 Big Data Government Applications
14.2.1 Homeland Security
14.2.2 Department Of Defense
14.2.3 Crime Prevention
14.2.4 Public Services Administration

15.0 Big Data Market Drivers And Constraints
15.1 Growth Drivers
15.1.1 Overall Growth Drivers For Big Data
15.1.2 Data Volume And Variety
15.1.3 Increasing Adoption Of Big Data By Enterprises And Telecom
15.1.4 Maturation Of Big Data Software
15.1.5 Continued Investments In Big Data By Web Giants
15.2 Market Barriers
15.2.1 Privacy And Security: The “Big” Barrier To Bdaas
15.2.2 Workforce Re-Skilling And Organizational Resistance
15.2.3 Lack Of Clear Big Data Strategies
15.2.4 Technical Challenges: Scalability & Maintenance

16.0 Big Data Trends And Forecasts
16.1 Overall Big Data Trends
16.2 Big Data Trends By Industry Sector
16.2.1 Industrial Internet & M2M
16.2.2 Retail & Hospitality
16.2.3 Media
16.2.4 Utilities
16.2.5 Financial Services
16.2.6 Healthcare & Pharmaceutical
16.2.7 Telecommunications And Ict
16.2.8 Government & Homeland Security
16.2.9 Other Sectors
16.3 Big Data Forecasts
16.3.1 Big Data Revenue By Functional Area: 2013 - 2018
16.3.2 Big Data Revenue By Region 2013 - 2018
16.3.3 Big Data Revenue By Industry Vertical 2013 - 2018

17.0 Future Of Big Data In Telecom And Ict
17.1 Emerging Commercial Benefits
17.1.1 Improving Subscriber Experience
17.1.2 Building And Maintaining Smarter Networks
17.1.3 Churn/Risk Reduction And New Revenue Streams
17.1.4 Carrier Case Studies
17.2 Future Solutions, Approaches, And Challenges
17.2.1 Parallel Technology Advance
17.2.2 Multi-Platform
17.2.3 Self-Serve
17.2.4 Collaboration
17.2.5 More Real-Time
17.2.6 Privacy And Security Issues
17.2.7 Expanding In Mobile Market
17.2.8 Location-Based Information
17.3 Future Converged Technologies And Solutions
17.3.1 Self-Organizing Networks + Real-Time Data And Analytics
17.3.2 Big Data And The Internet Of Things (Iot)
17.3.3 M2M, Iot, And Big Data
17.4 Big Data Becomes A Part Of Everything
17.4.1 At Some Point It’S Not “Big” It’S Just “Data”

18.0 Conclusions And Recommendation
18.1 Recommendations For Telecom Carriers
18.1.1 Leverage New Technologies And Solutions
18.1.2 Improved Data Handling

19.0 Appendix
19.1 Big Data And The Evolution Of Everything To The Cloud
19.2 Relationship Between Big Data And The Internet Of Things (Iot)
19.3 Data Mining And Management
19.4 Comprehensive Assessment Of Select Companies In Big Data
19.4.1 Accenture
19.4.2 Computer Science Corporation
19.4.3 Fujitsu Ltd.
19.4.4 Hewlett-Packard Company
19.4.5 International Business Machines Corp.
19.4.6 Informatica Corporation
19.4.7 Mu Sigma Inc.
19.4.8 Opera Solutions, Llc
19.4.9 Oracle Corporation
19.4.10 Tata Consultancy Services

List of Figures

Figure 1: Traditional Information Architecture Capabilities
Figure 2: Big Data Information Architecture Capabilities
Figure 3: Top Ten Challenges Preventing Big Data In Business
Figure 4: Ensuring A Data-Rich Future For Social Sciences
Figure 5: Big Data And Adoption Cycles
Figure 6: Data Generated By Various Industry Sectors
Figure 7: Big Data Sources
Figure 8: Big Data As A Service (Bdaas)
Figure 9: Telecom Apis Facilitate Many Services And Huge Amounts Of Data
Figure 10: Big Data Capabilities Integration
Figure 11: Hadoop In The Enterprise
Figure 12: Hadoop Data Detection And Protection
Figure 13: Mapreduce Framework
Figure 14: Hadoop And Nosql Vendor Revenue Share 2011-2013
Figure 15: Stored Data In Organizations
Figure 16: New Data Stored Across Geographies
Figure 17: Type Of Data Generated By Industry Verticals
Figure 13: Big Data Domains
Figure 19: Big Data Capture And Analysis For Telecom
Figure 20: Data Management
Figure 21: Big Data Value Chain
Figure 22: Big Data Ecosystem By Internet Business Type
Figure 23: Big Data Ecosystem Layered Topology View
Figure 23: Big Data Landscape
Figure 25: Big Data Revenue Share By Vendor Solutions 2013
Figure 26: Big Data And Analytics
Figure 27: Predictive Analytics
Figure 28: Effectiveness Of Critical Data In Decision Making
Figure 29: Top Challenges To Realizing Value From Big Data
Figure 30: Ensuring A Data-Rich Future Within Social Sciences
Figure 31: Data Volume In Old And New World
Figure 32: Shifting Control To The Enterprise
Figure 28: Api Growth
Figure 34: Small Data Implementation Framework
Figure 35: Stored Data In Organizations
Figure 36: Stored Data By Location
Figure 37: Big Data And Mobile Commerce/Marketing
Figure 38: Big Data As Competitive Differentiator For Financial Services
Figure 39: Big Data In Financial Services
Figure 40: Financial Big Data Management Paradigm
Figure 41: Big Data Approaches For Financial Services
Figure 42: Big Data Functional Levels
Figure 43: Big Data For Crime Prediction
Figure 44: Online Sales Forecast
Figure 45: Crm Aspects To Consider With Big Data
Figure 46: Traditional Crm And Social Crm
Figure 47: Samsung Internal Value Chain
Figure 48: Measurement Pyramid
Figure 49: United States Manufacturing
Figure 50: Big Data Use Case Scenarios
Figure 51: Attributes Of Big Data Value And Usability
Figure 52: Big Data And It Systems
Figure 53: Big Data And The Supply Chain
Figure 44: Big Data And Erp
Figure 55: Ict And The Smartgrid
Figure 56: Big Data Value Anticipated In Industry
Figure 57: Big Services Revenue By It Segment 2010 - 2013
Figure 58: Big Data Revenue By Functional Area: 2013 – 2018
Figure 59: Big Data Revenue By Region: 2013 – 2018
Figure 60: Big Data Revenue By Industry Vertical: 2013 – 2018
Figure 61: Big Data In Everyday Life

List of Tables

Table 1: Telecom Analytic Company: Competitors Summary
Table 2: Telecom Analytic Company: Swot Summarization Table - Part One
Table 3: Telecom Analytic Company: Swot Summarization Table - Part Two
Table 4: Decision Points For Using Big Data
Table 5: How Small Data Is Different
Table 6: Top 8 Countries’ Value-Added In Manufacturing In Gdp 2008-2012

Note: Product cover images may vary from those shown

- 1010Data
- Actian
- Agilis International
- Alteryx
- Amanzitel
- Amazon
- Apache Software Foundation
- Aptean
- Attivio
- Cataphora
- Cisco
- Cloudera
- Csc
- Cvidya
- Datameer
- Dell
- Emc
- Eplorys
- Fujitsu
- Fusion-Io
- Gooddata
- Google
- Guavus
- Hitachi Data Systems
- Hortonworks
- Hp
- Humedica
- Hitachi
- Ibm
- Informatica
- Intel
- Intersystems
- Jaspersoft
- Marklogic
- Microsoft
- Mongodb
- Mu Sigma
- Netapp
- Opera Solutions
- Oracle
- Paraccel
- Parstream
- Pentaho
- Pervasive
- Platfora
- Qliktech
- Quantum
- Rackspace
- Revolution Analytics
- Salesforce
- Sap
- Sas
- Sisense
- Software Ag/Terracotta
- Splunk
- Sqrrl
- Subex
- Supermicro
- Tableau
- Teoco
- Teradata
- Think Big Analytics
- Tidemark Systems
- Tibco
- Vmware (Emc)
- Wedo Technologies

Note: Product cover images may vary from those shown
Note: Product cover images may vary from those shown

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