PRN: Global Big Data in Leading Industry Verticals Market Report 2015-2020: Retail, Insurance, Healthcare, Government, and Manufacturing

27/gen/2016 19.11.07 PR Newswire Turismo Contatta l'autore

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Global Big Data in Leading Industry Verticals Market Report 2015-2020: Retail, Insurance, Healthcare, Government, and Manufacturing

 
[27-January-2016]
 

DUBLIN, Jan. 27, 2016 /PRNewswire/ --

Research and Markets (http://www.researchandmarkets.com/research/t3kf8m/big_data_in) has announced the addition of the "Big Data in Leading Industry Verticals: Retail, Insurance, Healthcare, Government, and Manufacturing 2015 - 2020" report to their offering.

There are certain sectors that are early adopters of Big Data and Analytics and expect to be big beneficiaries of advancing solutions. This comprehensive research offering includes detailed analysis, insights, and forecast for 2015 - 2020 for the following industries:

- Retail

- Insurance

- Healthcare

- Government

- Manufacturing

This research evaluates unique problems in each industry, companies and solutions, market outlook, and forecasts for each industry vertical.

Target Audience:

- Big Data vendors

- Telecom service providers

- Telecom equipment providers

- Global infrastructure suppliers

- Communications component providers

- Cloud services and datacenter companies

- Big Data, analytics, and data processing companies

Key Topics Covered:

Big Data In Retail 2015: Market Analysis, Companies, Solutions, And Forecasts 2015 - 2020

1.0 Executive Summary

2.0 Introductory Concepts 2.1 What Is Big Data? 2.2 Sources Of Big Data 2.3 Data Mangement And The Four V'S Of Big Data 2.4 Big Data Product And Services 2.5 Big Data Analytics 2.6 Big Data Approaches For Analytics 2.7 Retail Analytics 2.8 Retail Use Case Of Analytics 2.9 Omni Channel Platform 2.10 Customer Centric Analytics

3.0 Data Management And Retail Digital Transformation 3.1 Multichannel To Omni-Channel 3.2 Same Day Delivery 3.3 Execution Of Strategy 3.4 Showrooming 3.5 Solomome 3.6 Predictive Analytics 3.7 Omni-Channel Customer Experience 3.8 Big Data Analytics 3.9 Omni-Channel Experience Use Cases 3.10 Omni-Channel Predictive Analytics 3.11 Customer Behavioral Analytics 3.12 Developing An Omni-Channel Strategy

4.0 Big Data In Retail: Technologies, Solutions, And Approach 4.1 What Does Big Data Mean For Retail? 4.2 Variant Of Retail Analytic Approach 4.2.1 Descriptive Analytics 4.2.2 Inquisitive Or Diagnostic Analytics 4.2.3 Predictive Analytics 4.2.4 Prescriptive Aanalytics 4.2.5 Pre-Emptive Analytics 4.3 Data Management And Big Data Apps In Retail 4.3.1 Direct Mail Marketing 4.3.2 Customer Relationship Management 4.3.3 Category Management And Inventory Control 4.3.4 Market Basket Analysis 4.3.5 Website Analysis And Personalization 4.3.6 Additional Possible Retail Applications 4.4 Benefits From Big Data Analytics For Retailers 4.5 Retailers Behavior 4.5.1 Innovators 4.5.2 Unlocking Big Data 4.5.3 Maximize Technology Use 4.5.4 Use Analytics To Personalize Products 4.5.5 Omni-Channel Oriented 4.5.6 Measure What Matters 4.5.7 Stay True To Their Company Strategy 4.6 Four V'S Of Big Data In Retail Business 4.6.1 Volume 4.6.2 Velocity 4.6.3 Variety 4.6.4 Value 4.7 Impact Of Four V'S ! In Retail Business 4.7.1 Right Product 4.7.2 Right Place 4.7.3 Right Time 4.7.4 Right Price 4.8 Big Data Technology 4.8.1 Sensors 4.8.2 Computer Networks 4.8.3 Data Storage 4.8.4 Cluster Computer Systems 4.8.5 Cloud Computing Facilities 4.8.6 Data Analysis Algorithms 4.8.7 Big Data Technology Stack 4.9 Role And Importance Of Big Data In Retail 4 4.9.1 Pattern Discovery 4.9.2 Decision Making 4.9.3 Process Invention 4.9.4 Increasing Revenue 4.10 Role And Importance Of Big Data Analytics In Retail 4.10.1 Intelligent Enterprise

5.0 Big Data In Retail Market Analysis 5.1 Current Market Trends 5.1.1 Heavy Influence Of Boomers And Millennials 5.1.2 Social Networks As Shopping Platforms 5.1.3 Doubling Trend Of Corporate Social Responsibility 5.1.4 Gamification Loyalty 5.1.5 Experiment With Tehcnology 5.1.6 Data Driven Metrics 5.1.7 Better Ways To Manage Risk And Protect Customers 5.1.8 Control Over Value Chain And Improve Order Fulfillment 5.1.9 Ecommerce To Offline Shop 5.1.10 Localization Of Product Mix And Store Formats 5.1.11 Mobile Shopping 5.1.12 Stores With Omnichannel Strategies 5.2 Big Data And Anticipated Retail Growth Drivers 5.2.1 Awareness 5.2.2 Software 5.2.3 Services 5.2.4 Investment 5.2.5 Other Drivers 5.3 Big Data Market Challenges 5.3.1 Data Challenges 5.3.2 Process Challenges 5.3.3 Management Challenges 5.4 Online Shopping Market Challenges 5.5 Big Data Risks 5.5.1 Governance 5.5.2 Management! 5.5.3 Architecture 5.5.4 Usage 5.5.5 Quality 5.5.6 Security 5.5.7 Privacy 5.6 Adoption Barriers 5.7 Market Opportunity 5.8 Market Investment Opportunity 5.8.1 Investment Within Hadoop 5.8.2 Splunk Capitalizing Big Data 5.8.3 Teradata Expecting Big Growth 5.8.4 Hortonworks Commercializes Hadoop 5.8.5 Mapr Distribution Of Hadoop

6.0 Big Data Ecosystem In Retail 6.1 Big Data Stakeholders 6.2 Business Models

7.0 Case Studies Of Big Data In Retail 7.1 Consumer Electronics 7.1.1 Best Buy 7.1.2 Insourcesm Solution From Experian 7.2 For The Home 7.2.1 Bed Bath And Beyond (Bbb) 7.3 General Consumer Items Including Food 7.3.1 Walmart 7.3.2 Social Genome 7.3.3 Shoppycat 7.3.4 Get On The Shelf 7.3.5 Macy'S 7.3.6 Sas® Business Analytics 7.3.7 Debenhams 7.3.8 Sky Iq 7.3.9 Williams-Sonoma 7.4 Luxury And Fashion Including Sports 7.4.1 Luxottica 7.4.2 Elie Tahari 7.5 Real Life Impact 7.5.1 Tesco 7.5.2 Kroger 7.5.3 Delhaize 7.5.4 Food Lion 7.5.5 Red Roof 7.5.6 Pizza Chain 7.5.7 Emi 7.5.8 Financial Services Company 7.5.9 Target

8.0 Big Data Vendors In Retail 8.1 Personalization 8.1.1 Synqera 8.1.2 Ngdata 8.2 Dynamic Pricing 8.2.1 Altierre 8.3 Customer Service 8.3.1 Retention Science 8.4 Fraud Management 8.4.1 Rsa 8.5 Supply Chain Visibility 8.5.1 Opera Supply Chain Solutions 8.6 Predictive Analytics 8.6.1 Sumall 8.7 Key Players 8.7.1 1010Data 8.7.2 Ibm 8.7.3 Teradata 8.7.4 Oracle 8.7.5 Hp

9.0 Big Data In Retail Market Forecasts 2015 - 2020 9.1 Big Data In Retail Market Revenue 2015 - 2020 9.2 Big Data In Retail Market Revenue By Type 2015 - 2020 9.3 Big Data In Retail Market Revenue By Sub-Type 2015 - 2020 9.4 Hadoop Based Big Data Solution Revenue Retail Market 2015 - 2020 9.5 Big Data In Retail Market Revenue By Region 2015 - 2020 9.6 Big Data In Retail Market Revenue By Country 2015 - 2020 9.7 Big Data Revenue Of Top Five Leaders 2013 - 2014 9.8 Data Growth 2008 - 2020

10.0 Conclusions And Recommendations 10.1 General Recommendations 10.2 Recommendations To Big Data Vendors 10.3 Recommendation To Retailers

Big Data In Insurance Industry

1.0 Executive Summary 2.0 Introduction 2.1 What Is Big Data? 2.2 The Relevance And Importance Of Big Data 2.3 Analytics And Big Data 2.4 Big Data And Business Intelligence 3.0 Big Data And Analytics In Insurance 3.1 Big Data And Analytic Opportunities 3.1.1 Customer Related 3.1.2 Risk Related 3.1.3 Finance Related 3.2 Big Data Benefits Areas In Insurance Enterprises 3.2.1 Claims Fraud Detection And Mitigation 2 3.2.2 Customer Retention, Profiling And Insights 3.2.3 Customer Needs Analysis 3.2.4 Risk Evaluation, Management, And Planning 3.2.5 Product Personalization 3.2.6 Claims Management 3.2.7 Cross Selling And Up-Selling 3.2.8 Catastrophe Planning 3.2.9 Customer Sentiment Analysis 4.0 Areas Of High Roi Potential 4.1 Group Health Insurance And Disability Insurance 4.2 Auto Insurers 4.3 Advertising And Campaign Management 4.4 Agents Analysis 4.5 Call Detail Records 4.6 Personalized Pricing 4.7 Underwriting And Loss Modeling 5.0 Big Data Impact Areas 5.1 Risk Evaluation An! d Management 5.2 Insurance Industry Structure 5.3 Customer Insights 5.4 Claims Management 5.5 Regulatory Compliance 6.0 Big Data Trends In Insurance 6.1 Organizational And Tech Aspects 6.2 Diversity In Business And Data Priorities 6.3 Risk Assessment With Granular Data 6.4 Use Of External Device Data And Telematics 6.5 New Big Data And Analytics Paradigms 7.0 Conclusions And Recommendations

Big Data In Healthcare 2015 - 2020

1.0 Executive Summary 2.0 Introduction 2.1 Personal Health Care Expenditures 2.2 Us Government Spending On Healthcare 2010 - 2020 2.3 Us Healthcare Budget Allocation In 2015 3.0 Big Data In Healthcare 3.1 Big Data As Basis For Insightful Action 3.2 Clinical And Advanced Analytics 3.3 Steps To Becoming A Data-Driven Healthcare Organization 3.3.1 Determine Quality Metrics 3.3.2 Data Source Integration 3.3.3 Data Security Management 3.4 Unstructured Data In Healthcare 3.4.1 Comprehensive Healthcare Systems 3.4.2 Improved Collaboration Among Key Players 3.4.3 Efficient Access To Healthcare 3.4.4 Healthcare And Big Data Treatment 3.5 Advantages Of Managing Big Data In Healthcare 3.5.1 Big Data For Earlier Disease Detection 3.5.2 Big Data For Fraud Detection 3.5.3 Healthcare As Vulnerable Target 3.5.4 Big Data Defers Prescription Abuse 3.5.5 Big Data For Precision Medicine 3.5.6 Customized Healthcare 3.5.7 Population Health Management 4.0 Impact Of Trends 4.1 Need To Leverage B! ig Data 4.1.1 Data Government Framework 4.1.2 Healthcare Provider Collaboration 4.1.3 Tailored Solutions 5.0 Big Data Health Care Solutions 5.1 Due North Analytics 5.2 Explorys 5.3 Humedica 5.4 Intersystems 5.5 Pervasive 5.6 Clinical Query 5.7 Gns Healthcare 5.8 Omedarx 5.9 Truven Health Analytics 5.10 Sogeti Healthcare 6.0 Future Outlook 6.1 More Research Big Data Analytics R&D 6.2 More Towards Personalized Medicine 6.3 Potential To Predict And Prevent Disease 6.4 More Analytics For Doctors 6.5 More Towards Drug Discovery 7.0 Conclusions

Big Data In Manufacturing: Key Trends, Opportunities And Market Forecasts 2015 - 2020

1 Introduction 1.1 Research Scope 1.2 Research Methodology 1.3 Target Audience 1.4 Companies Mentioned In This Report 2 Summary 3 Overview 3.1 Role Of Big Data In Modern Manufacturing 3.2 Big Data And Analytics Framework For Manufacturing 3.2.1 Big Data Infrastructure 3.2.2 Big Data Management 3.2.3 Big Data Integration 3.2.4 Big Data Analysis 3.3 Market Potential For Big Data In Manufacturing Will Increase Through 2020 4 Big Data Solutions In Manufacturing 4.1 Hardware Infrastructure 4.1.1 Servers / Data Computing Appliance 4.1.2 Sensors And Actuators 4.2 Software And Platforms 4.2.1 Big Data Integration Platform 4.2.2 Connectors For Hadoop 4.2.3 Big Data Analytics Platforms And Tools 4.3 Big Data Security Software 4.4 Managed Services For Big Data 5 Global Markets And Forecasts 2015 - 2020 5.1 Industrial Internet Of Things To Increase Scope For Big Data In Manufacturing 5.2 Connected Factory 5.3 Scope For Big Data In Iiot For Manufacturing 5.4 Manufacturing Sector To Ge! nerate 11.3 Zettabytes Of Data By 2020 5.5 Big Data Market In Manufacturing 2015 - 2020 5.5.1 Big Data In Manufacturing By Region 2015 - 2020 5.5.2 Big Data In Manufacturing By Products/Service Offering 2015 - 2020 6 Company Profiles 6.1 1010Data Inc. 6.2 3Sixty Analytics 6.3 Actian Corporation 6.4 Amazon Web Services 6.5 Bosch Software Innovations Gmbh 6.6 Cisco 6.7 Cloudera Inc. 6.8 Cloudwick Inc. 6.9 Computer Sciences Corp. (Csc) 6.10 Cray Inc. 6.11 Dell Software 6.12 Emc Corporation 6.13 Hp 6.14 Hortonworks Inc. 6.15 Mongodb 6.16 Oracle Corporation 6.17 Pivotal Software Inc 6.18 Pssc Labs 6.19 Silicon Graphics International Corp. (Sgi) 6.20 Teradata Corporation 6.21 Tibco Jaspersoft

For more information visit http://www.researchandmarkets.com/research/t3kf8m/big_data_in

Media Contact:

Laura Wood, +353-1-481-1716, press@researchandmarkets.net

 



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