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市場調查報告書

主要垂直產業上的巨量資料:零售、保險、醫療、政府及製造 2015-2020年

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

出版商 Mind Commerce 商品編碼 347562
出版日期 內容資訊 英文 339 Pages
商品交期: 最快1-2個工作天內
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主要垂直產業上的巨量資料:零售、保險、醫療、政府及製造 2015-2020年 Big Data in Leading Industry Verticals: Retail, Insurance, Healthcare, Government, and Manufacturing 2015 - 2020
出版日期: 2015年12月22日 內容資訊: 英文 339 Pages
簡介

本報告提供零售、保險、醫療、政府及製造產業上的巨量資料的詳細分析、考察及預測、各產業獨特的問題、企業與解決方案 ,及市場展望等相關彙整。

零售的巨量資料:市場分析、企業、解決方案及預測

第1章 摘要整理

第2章 簡介

  • 巨量資料為何?
  • 巨量資料的資源
  • 資料管理及巨量資料的4個V
  • 巨量資料產品與服務
  • 巨量資料分析
  • 分析用巨量資料方法
  • 零售分析
  • 分析的零售的利用案例
  • 全方位流通管道的平台
  • 客戶中心分析

第3章 資料管理及零售的數位轉換

  • 從多通路到全方位流通管道
  • 同日發送
  • 策略的實行
  • 展示廳現象(Showrooming)
  • SoLoMoMe (社群+當地+行動+個性化)
  • 預測分析
  • 全方位流通管道客戶經驗
  • 巨量資料分析
  • 全方位流通管道體驗的利用案例
  • 全方位流通管道的預測分析
  • 客戶行為分析
  • 全方位流通管道策略的開發

第4章 零售的巨量資料:技術、解決方案、方法

  • 對零售來說巨量資料意味著什麼?
  • 零售分析方法的變異
  • 零售的資料管理及巨量資料應用
  • 對零售業者來說的巨量資料的優點
  • 零售業者的行動
  • 零售商務上巨量資料的4個V
  • 零售商務上4個V的影響
  • 巨量資料技術
  • 零售的巨量資料所扮演的角色、重要性
  • 零售的巨量資料分析所扮演的角色、重要性

第5章 零售的巨量資料市場分析

  • 目前市場趨勢
  • 巨量資料及預測的零售推動成長因素
  • 巨量資料市場課題
  • 網路購物市場課題
  • 巨量資料的風險
  • 引進障礙
  • 市場機會
  • 市場投資機會

第6章 零售的巨量資料的生態系統

  • 巨量資料的相關利益者
  • 經營模式

第7章 零售的巨量資料的案例研究

  • 家電
  • 家用
  • 包括食品的一般消費品
  • 奢侈品、含運動的服飾
  • 對日常生活的影響

第8章 零售的巨量資料供應商

  • 個性化
  • 動態價格設定
  • 欺詐管理
  • 供應鏈的能見度
  • 預測分析
  • 主要企業

第9章 零售的巨量資料市場預測

  • 零售市場上巨量資料收益
  • 零售市場上巨量資料收益:各類型
  • 零售市場上巨量資料收益:各子類型
  • HADOOP型巨量資料解決方案收益
  • 零售市場上巨量資料收益:各地區
  • 零售市場上巨量資料收益:各國
  • 前五名領導者的巨量資料收益
  • 資料的擴大

第10章 結論、建議

  • 一般的建議
  • 對巨量資料供應商的建議
  • 對零售業者的建議

保險產業上巨量資料

第1章 摘要整理

第2章 簡介

第3章 保險產業上的巨量資料及分析

第4章 有高ROI可能性的領域

第5章 巨量資料影響的領域

第6章 保險的巨量資料趨勢

第7章 結論、建議

圖表

醫療的巨量資料

第1章 摘要整理

第2章 簡介

第3章 醫療的巨量資料

第4章 趨勢的影響

第5章 巨量資料的醫療保健解決方案

第6章 未來展望

第7章 結論

圖表

政府、防禦及國防安全保障的巨量資料

摘要整理

簡介

巨量資料趨勢

巨量資料、政府及國家國防

國防安全保障的巨量資料

結論

製造的巨量資料:主要趨勢、機會及市場預測

第1章 簡介

第2章 摘要

第3章 概要

第4章 製造中的巨量資料解決方案

第5章 全球市場、預測

第6章 企業簡介

圖表

目錄

While Big Data and Analytics is rapidly integrating with virtually every industry vertical, there are certain sectors that are early adopters and also expected 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. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

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

Table of Contents

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

Figures

  • Figure 1: Big Data in SMAC Ecosystem
  • Figure 2: Big Data Sources
  • Figure 3: Four V Framework of Big Data
  • Figure 4: Big Data for Analytics: Sources, Projections and Contribution
  • Figure 5: Customer Journey in In-Store Analytics Framework
  • Figure 6: Use Case Framework for Retail Analytics
  • Figure 7: Omni-channel Customers and New Retail IT Model
  • Figure 8: Customer Centric Analytics Framework
  • Figure 9: Consumer Goods Value Chain
  • Figure 10 Transformation of Age from Manufacturing to Customer
  • Figure 11: Customer Experience Framework
  • Figure 12: Omni-Channel Micro Strategy
  • Figure 13: Customer Intelligence Appliance
  • Figure 14: In-Store Customers in Big Data Retail Framework
  • Figure 15: Big Data Situation in Retail Industry
  • Figure 16: Big Data Retail Analytics Variant and Actions
  • Figure 17: Goals of Using Big Data Application in Retail
  • Figure 18: Big Data Customer Insight Framework for Time Engagement
  • Figure 19: Big Data Technology Stack
  • Figure 20: Value Generation of Big Data Analytics
  • Figure 21: Big Data Analytic Value Chain
  • Figure 22: Nordstrom Using Like2Buy Button on Instagram
  • Figure 23: Walgreens Gamified Health Activities in Retail
  • Figure 24: Birchbox Ecommerce to Offline Shop
  • Figure 25: Bird on a Wire on Mobi2Go Solution
  • Figure 26: Big Data Business Model: Information Based Framework
  • Figure 27: Lily Interactive Big Data Framework
  • Figure 28: Big Data in Retail Market Revenue $ Billion 2015 - 2020
  • Figure 29: Data Growth in Zettabytes 2008 - 2020
  • Figure 30: Big Data Implementation Framework
  • Figure 31: Big Data Implementation Steps

Tables

  • Table 1: New Online Shopping Dynamics for Retail Marchant
  • Table 2: Big Data Technology and Services Vendors to Watch
  • Table 3: Big Data in Retail Revenue by H/W vs. S/W vs. Services 2015 - 2020
  • Table 4: BD in Retail Rev by Database, Analytics, Services, Cloud 2015 - 2020
  • Table 5: Hadoop Based BD Retail Revenue 2015 - 2020
  • Table 6: Big Data in Retail Market Revenue by Region 2015 - 2020
  • Table 7: Big Data in Retail Revenue in Top 4 Countries 2015 - 2020
  • Table 8: BD Retail Revenue by Country as % of Total BD Market by Region
  • Table 9: Big Data Revenue of Top Five Leaders 2013 - 2014

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 AND 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

Figures

  • Figure 1: Global Data 2009 -2020 (ZB)
  • Figure 2: Cost of Data Management per GB 2005 - 2015 (USD)
  • Figure 3: Global Spending on Big Data 2014 - 2019 (USD $B)
  • Figure 4: BI, Big Data, and Analytics
  • Figure 5: Risk, Customers, and Finance

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 BIG 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

Tables

  • Table 1: Personal Health Care Expenditures by Source of Funds 2015 - 2020
  • Table 2: Government Spending on Healthcare in United States 2010 - 2020
  • Table 3: US Medical Health Care Allocation in 2015
  • Table 4: Platforms for Big Data in Healthcare
  • Table 5: Due North Analytics
  • Table 6: Explorys
  • Table 7: Humedica
  • Table 8: InterSystems
  • Table 9: Pervasive
  • Table 10: Clinical Query
  • Table 11: GNS Healthcare
  • Table 12: OmedaRX
  • Table 13: TRUVEN Health Analytics
  • Table 14: Sogeti Healthcare

Figures

  • Figure 1: US Healthcare Spending 2010 - 2020
  • Figure 2: US Healthcare Budget Allocation 2015
  • Figure 3: Conceptual Framework of Big Data in Healthcare Analytics
  • Figure 4: Healthcare and Big Data Leverage 2015 - 2020
  • Figure 5: Healthcare Big Data Sources
  • Figure 6: Healthcare versus Fraud 2015 - 2020
  • Figure 7: Healthcare Fraud 2015 - 2020
  • Figure 8: Overdose Deaths from Select Prescription and Illicit Drugs 2010 - 2020
  • Figure 9: Overdose Death Mitigation via Big Data 2015 - 2020

Big Data in Government, Defense and Homeland Security 2015 - 2020

EXECUTIVE SUMMARY

INTRODUCTION

BIG DATA TRENDS

  • 1.1 MANAGING UNSTRUCTURED (BIG) DATA
  • 1.2 A FUNCTIONAL PERSPECTIVE FOR BIG DATA IN DEFENSE AND HOMELAND SECURITY
  • 1.3 DATA ACQUISITION, COLLECTION AND DETECTION
  • 1.4 DATA MANAGEMENT, INTEGRATION AND ANALYSIS
  • 1.5 IMPACT ANALYSIS
  • 1.6 PROSPECTS

BIG DATA, THE GOVERNMENT AND NATIONAL DEFENSE

  • 1.7 US GOVERNMENT SPENDING FOR SECURITY AND MILITARY APPLICATIONS
  • 1.8 COST OF CYBERCRIMES AND GOVERNMENT INITIATIVES
  • 1.9 DEFENSE ADVANCED RESEARCH PROJECT AGENCY (DARPA) BIG DATA INITIATIVES
    • 1.9.1 XDATA PROGRAM
    • 1.9.2 BIG MECHANISM PROGRAM
    • 1.9.3 MUSE
    • 1.9.4 MEMEX PROGRAMS
    • 1.9.5 ADAMS
    • 1.9.6 RESILIENT CLOUDS PROGRAM
    • 1.9.7 VIDEO AND IMAGE RETRIEVAL AND ANALYSIS TOOL (VIRAT)
    • 1.9.8 NEXUS 7
    • 1.9.9 QUANTITATIVE GLOBAL ANALYTICS
    • 1.9.10 CYBERCOMPUTATIONAL INTELLIGENCE (CCI)
  • 1.10 DARPA BIG DATA APPLICATIONS BUDGET ANALYSIS
  • 1.11 IMPACT ANALYSIS
  • 1.12 PROSPECTS

BIG DATA IN HOMELAND SECURITY

  • 1.13 BIG DATA AND HOMELAND SECURITY CHALLENGES
  • 1.14 INTELLIGENCE DRIVEN SECURITY (IDS): THE NEXT GENERATION SECURITY FEATURES
    • 1.14.1 THE CASE OF MODUS OPERANDI
  • 1.15 BIG DATA AS CHANGING NORM FOR SECURITY APPROACHES
  • 1.16 STRUCTURING THE BIG DATA SECURITY PROGRAM FOR HOMELAND SECURITY
    • 1.16.1 CREATING "BIG DATA VIRTUALIZATION" FOR SECURITY APPLICATIONS
    • 1.16.2 PREDICTIVE ANALYTICS FOR DISASTER EVENTS AND COUNTERTERRORISM
    • 1.16.3 SPSS PREDICTIVE ANALYTICS
    • 1.16.4 BIG DATA PREDICTIVE POLICING
    • 1.16.5 THE BIRT SOLUTIONS
    • 1.16.6 TRANSVOYANT'S CONTINUOUS DECISION INTELLIGENCE (CDI)
    • 1.16.7 AGILEX - PHANERO SOLUTIONS
    • 1.16.8 BIG DATA AND CATASTROPHIC EVENTS
    • 1.16.9 DEPARTMENT OF HOMELAND SECURITY "THE WEATHER MAP"
    • 1.16.10 BIG DATA FOR IMPROVED AVIATION SECURITY
  • 1.17 IMPACT ANALYSIS
  • 1.18 PROSPECTS
    • 1.18.1 BIG DATA ANALYTIC TOOLS TO SEE BY 2020

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 Generate 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

Figures

  • Figure 1: Markets for Big Data in Manufacturing 2015 - 2020
  • Figure 2: Big Data and Analytics Framework for Manufacturing
  • Figure 3: IIoT Deployment in Manufacturing 2015 - 2020
  • Figure 4: Data Generation in Manufacturing 2013 - 2020
  • Figure 5: Total Big Data Market vs. Big Data in Manufacturing 2015 - 2020
  • Figure 6: Regional Markets for Big Data in Manufacturing 2015 - 2020
  • Figure 7: Big Data in Manufacturing by Products / Services 2015 - 2020

Tables

  • Table 1: Market for Big Data in Manufacturing 2015 - 2020
  • Table 2: Key Trends in Big Data in Manufacturing
  • Table 3: Tips for Manufacturers on Big Data Investments
  • Table 4: Servers and Data Computing Appliances offered by various Companies
  • Table 5: Data Integration Solutions offered by Various Companies
  • Table 6: Data Connectors offered by Various Companies
  • Table 7: Analytics Platform offered by Various Companies
  • Table 8: Big Data Security Software offered by Various Companies
  • Table 9: Big Data Managed Services offered by Various Companies
  • Table 10: IIoT Deployment in Manufacturing
  • Table 11: Data Generation in Manufacturing 2013 - 2020
  • Table 12: Global Markets for Big Data in Manufacturing 2015 - 2020
  • Table 13: Regional Markets for Big Data in Manufacturing 2015 - 2020
  • Table 14: Big Data in Manufacturing by Products / Services 2015 - 2020
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