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Big Data Opportunities, Challenges and Solutions for Industry Verticals

Big Data Opportunities, Challenges and Solutions for Industry Verticals

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


General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


1 Executive Summary
2 What is Big Data
2.1 Breaking down Big Data
2.1.1 Internal Data
2.1.2 Structured External Data
2.1.3 Unstructured External Data
2.2 The important ‘V’s of Big Data
2.3 The Big Deal about Big Data
2.3.1 Exponential growth of Big Data
2.3.2 What the Numbers Mean
2.3.3 The Chance for Companies to Thrive
2.4 How Big Is Big Data?
2.5 What Data Is Meaningful?
2.5.1 Operations Management data
2.5.2 Sales and Marketing data
2.5.3 Accounting and Finance data
2.6 Improved Technologies to Manage Data
2.6.1 Analytic relational systems
2.6.2 Non-relational systems
3 Big Problems to Solve
3.1 Better Investment Decision and Operational Changes
3.2 Real Time customization
3.3 Improved Performance and Risk Management
3.4 New Business Model
4 Uses for Big Data
5 Challenges in Big Data Analysis
5.1 Heterogeneity and Incompleteness
5.2 Scale
5.3 Timeliness
5.4 Privacy
5.5 Human Collaboration
6 Big Data vs. API Strategies
6.1 Structured and Unstructured Solutions: APIs
7 Big Data Ecosystem
7.1 Big Data Landscape
8 Big Data Architecture
8.1 Traditional Information Architecture Capabilities
8.2 Adding Big Data Capabilities
9 Big Data Sources: What and How Much?
9.1 Where the data is getting generated?
10 Big Data Generation and Analytics
10.1 Predictive Analytics
10.2 Data Scientists
10.3 Big Data Technologies and Techniques
10.3.1 Hadoop
10.3.1.1 Problems solved by Hadoop
10.3.1.2 Hadoop Architecture
10.3.1.3 Cost Benefits of Hadoop
10.3.2 Hadoop and Spark
10.3.3 Hadoop and Data Security
10.3.3.1 Hadoop’s Architecture Presents Unique Security Issues
10.3.3.2 Deploy a Purpose-Built Security Solution for Hadoop and Big Data
10.3.3 MapReduce
10.3.3.1 Features
10.3.3.2 When to Use MapReduce
10.3.3.3 When Not to Use MapReduce
10.3.3.4 How it Works
10.4 Data Mining
10.5 CRM Systems
10.6 Social Media
10.6.1 Ways to Tap Social Media
10.6.1.1 Google Trends
10.6.1.2 APIs and Mashups
10.6.1.3 Communicate Intelligence with Data Visualization Tools
11 Data Management
11.1 Acquire Big Data
11.2 Organize Big Data
11.3 Analyze Big Data
11.4 Interpretation
12 Big Data Standardization
12.1 Alliance for Telecommunication Industry Solutions
12.1.1 Business Challenges for CSPs
12.1.2 Promise of Big Data for Telecom
12.1.3 Catch problem spots before they affect service.
12.1.4 Put Big Data to use immediately
12.1.5 Give internal and external teams the tools they need
13 Major Service Providers
13.1 IBM
13.2 Datameer
13.3 Amazon Web Services
13.4 HP- Big Data
13.5 SpotFire
13.6 Intel
13.7 EMC
14 Big Data in Industry Verticals
14.1 Finance and Accounting
14.2 Retail and CRM
14.3 Government and Defense
14.5 Healthcare
14.6 Supply chain Management
14.7 Telecommunication
15 Summary and Conclusion
16. Appendix
16.1 Deeper Dive into Big Data in Retail
16.1.1 Retail Merchant Challenges
16.1.2 Big Data in Retail
16.1.3 Big Data in Retail Business Case
16.1.4 Retailers Business Models, Companies, and Solutions
16.1.5 Supply Chain Solutions
16.2 Deeper Dive into Big Data in Healthcare
16.2.1 Big Issues with Healthcare
16.2.2 Healthcare Stakeholders
16.2.3 Opportunities and Challenges
16.3 Big Data Industry Verticals vs. Functional Areas
16.3.1 Industry Verticals
16.3.2 Functional Areas
16.3.3 Big Data Functional Area Forecast 2013 - 2019

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