Big Data in Leading Industry Verticals: Retail, Insurance, Healthcare, Government, and Manufacturing 2015 - 2020
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:
Target Audience:
- Retail
- Insurance
- Healthcare
- Government
- Manufacturing
- Financial Services
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
1. EXECUTIVE SUMMARY
2. 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. 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. 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
4.9.1 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. 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. BIG DATA ECOSYSTEM IN RETAIL
6.1 BIG DATA STAKEHOLDERS
6.2 BUSINESS MODELS
7. 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. 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. 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. CONCLUSIONS AND RECOMMENDATIONS
10.1 GENERAL RECOMMENDATIONS
10.2 RECOMMENDATIONS TO BIG DATA VENDORS
10.3 RECOMMENDATION TO RETAILERS
2. 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. 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. 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
4.9.1 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. 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. BIG DATA ECOSYSTEM IN RETAIL
6.1 BIG DATA STAKEHOLDERS
6.2 BUSINESS MODELS
7. 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. 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. 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. CONCLUSIONS AND RECOMMENDATIONS
10.1 GENERAL RECOMMENDATIONS
10.2 RECOMMENDATIONS TO BIG DATA VENDORS
10.3 RECOMMENDATION TO RETAILERS
LIST OF 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
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
LIST OF 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. EXECUTIVE SUMMARY
2. 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. 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
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. 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. 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. 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. CONCLUSIONS AND RECOMMENDATIONS
LIST OF 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. EXECUTIVE SUMMARY
2. 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. 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. 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. 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. 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. CONCLUSIONS
LIST OF 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
LIST OF 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
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
LIST OF 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
LIST OF 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
Big Data in Financial Services Industry: Market Analysis and Forecasts 2015 - 2020
1. EXECUTIVE SUMMARY
2. BIG DATA IN FINANCIAL SERVICES
2.1 FINANCIAL SERVICES INDUSTRY
2.2 FINANCIAL SERVICES TRANSFORMS WITH BIG DATA
2.3 MACRO DRIVERS FOR BIG DATA IN FINANCIAL SERVICE
2.3.1 CASHLESS SOCIETY
2.3.1 BIG DATA IN TRADING AND INVESTING COMPLIANCE AND BEHAVIOR LEARNING
2.3.2 THE GOVERNMENT AND BIG DATA IN FINANCIAL SERVICES
2.4 ROLE OF BIG DATA IN FINANCIAL SERVICES
2.5 BIG DATA TO BECOME ESSENTIAL COMPONENT FOR FINANCIAL SERVICE SECTOR
2.6 A THREE-WAY BIG DATA APPROACH TOWARDS FINANCIAL SERVICES
2.6.1 INFORMATION BASED SORTING
2.6.2 INFORMATION BASED BROKERING
2.6.3 INFORMATION BASED DELIVERY
2.7 STEPS FOR BIG DATA FUNCTIONING IN FINANCIAL SERVICES
2.7.1 DATA ACQUISITION, COLLECTION, AND DETECTION
2.7.2 DATA MANAGEMENT AND INTEGRATION
2.7.3 DATA ANALYSIS
2.8 BIG DATA AS COMPETITIVE DIFFERENTIATOR FOR FINANCIAL SERVICES
2.9 FINANCIAL BIG DATA MANAGEMENT: REFERENCE DATA
2.10 FUTURE OF BIG DATA IN FINANCIAL SECTOR
3. BIG DATA INITIATIVES OF FINANCIAL SERVICES PROVIDERS
3.1 CURRENT STAGE OF THE BIG DATA IMPLEMENTATION IN FINANCIAL SERVICES
3.1.1 FINANCIAL SERVICE PROVIDER BIG DATA INITIATIVES
3.2 TOP BIG DATA INITIATIVES IN FINANCIAL SERVICES SECTOR
3.2.1 PROVIDE REAL-TIME RESPONSE TO CONSUMER QUERIES
3.2.2 ASSESS CUSTOMER BEHAVIORAL AND TENDENCY DATA USING PREDICTIVE ANALYTICS
3.2.3 MEASURE CUSTOMER SENTIMENTS AND TAKE APPROPRIATE ACTION
3.2.4 MASS CUSTOMIZATION DATA REMODELING
3.2.5 BIG DATA FOR BIG REVENUE
3.2.6 BIG DATA FOR PREDICTING FRAUD AND OTHER FINANCIAL CRIMES
4. BIG DATA IN FINANCIAL SERVICES: GLOBAL MARKET 2015 - 2020
4.1 THE GLOBAL BIG DATA MARKET
4.1.1 THE UNSTRUCTURED DATA MARKET
4.1.2 THE THIRD PLATFORM PERSPECTIVE
4.1.3 DATA PROCESS MAGNITUDE
4.1.4 TOWARDS THE ZETTABYTES MARKET
4.1.5 GLOBAL MARKETS FOR BIG DATA 2015 - 2020
4.1.6 DATA ANALYTICS IS THE BATTLEGROUND FOR COMPETITION
4.2 LEARNING FROM BIG DATA IN FINANCIAL SERVICES SECTOR
4.3 GLOBAL MARKET FOR BIG DATA IN FINANCIAL SECTOR 2015 - 2020
4.4 FOCUS AREAS FOR FINANCIAL SERVICES SECTOR INVESTMENT 2015 - 2020
5. COMPANIES AND SOLUTIONS
6.1 BIG DATA FINANCIAL MANAGEMENT SOLUTIONS
6.2 COMPANIES AND SOLUTIONS
6.2.1 1010DATA
6.2.2 10GEN
6.2.3 ACTIAN
6.2.4 ALTERYX
6.2.5 AMAZON
6.2.6 ATTIVIO
6.2.7 BOOZ ALLEN HAMILTON
6.2.8 CAPGEMINI
6.2.9 CISCO SYSTEMS
6.2.10 CLOUDERA
6.2.11 CSC
6.2.12 DELL
6.2.13 EMC
6.2.14 FUSION-IO
6.2.15 GOODDATA
6.2.16 GOOGLE
6.2.17 GUAVUS
6.2.18 HP
6.2.19 HITACHI
6.2.20 IBM
6.2.21 INFORMATICA
6.2.22 INTEL
6.2.23 MARKLOGIC
6.2.24 MICROSOFT
6.2.25 MU SIGMA
6.2.26 NETAPP
6.2.27 OPERA SOLUTIONS
6.2.28 ORACLE
6.2.29 PARACCEL
6.2.30 QLIKTECH
6.2.31 SAP
6.2.32 SGI
6.2.33 SPLUNK
6.2.34 TERADATA
6.2.35 TIBCO SOFTWARE
6.2.36 VMWARE
7. CONCLUSIONS AND RECOMMENDATIONS
LIST OF FIGURES
Figure 1: Big Data Approaches for Financial Services
Figure 2: Big Data Functional Levels
Figure 3: Big Data as Competitive Differentiator for Financial Services
Figure 4: Financial Big Data Management Paradigm
Figure 5: Big Data for Predicting Financial Crimes
Figure 6: Big Data Paradigm
Figure 7: Migration Process of Platform Technology
Figure 8: Data Universe Zettabytes Generation 2013 - 2020
Figure 9: Global Big Data Market Forecast 2015 – 2020
Figure 10: Global BD Market by H/W, S/W, and Services 2015 – 2020
Figure 11: Big Data in Financial Services by Components 2015 - 2020
Figure 12: Big Data Revenue Share by Vendor Solutions
Figure 13: Hadoop and NoSQL Vendor Revenue Share
LIST OF TABLES
Table 1: Global Big Data Market 2015 – 2020
Table 2: Global Big Data Markets by H/W, S/W, and Services 2015 - 2020
Table 3: Global Markets for Big Data in Financial Sector
Table 4: 1010data Big Data Financial Management Solutions
Table 5: 10gen Big Data Financial Management Solutions
Table 6: Actian Big Data Financial Management Solutions
Table 7: Alteryx Big Data Financial Management Solutions
Table 8: Amazon Big Data Financial Management Solutions
Table 9: Attiivio Big Data Financial Management Solutions
Table: 10 Booz Allen Hamilton Big Data Financial Management Solutions
Table 11: Capgemini Big Data Financial Management Solutions
Table 12: Cisco Big Data Financial Management Solutions
Table 13: Cloudera Big Data Financial Management Solutions
Table 14: CSC Big Data Financial Management Solutions
Table 15: Dell Big Data Financial Management Solutions
Table 16: EMC Big Data Financial Management Solutions
Table 17: Fusion-IO Big Data Financial Management Solutions
Table 18: GoodData Big Data Financial Management Solutions
Table 19: Google Big Data Financial Management Solutions
Table 20: Guavus Big Data Financial Management Solutions
Table 21: HP Big Data Financial Management Solutions
Table 22: Hitachi Big Data Financial Management Solutions
Table 23: IBM Big Data Financial Management Solutions
Table 24: Informatica Big Data Financial Management Solutions
Table 25: Intel Big Data Financial Management Solutions
Table 26: MarkLogic Big Data Financial Management Solutions
Table 27: Microsoft Big Data Financial Management Solutions
Table 28: Mu Sigma Big Data Platforms
Table 29: MuSigma Big Data Financial Management Solutions
Table 30: NetApp Big Data Financial Management Solutions
Table 31: Opera Solutions Big Data Financial Management Solutions
Table 32: Oracle Big Data Financial Management Solutions
Table 33: ParAccel Big Data Financial Management Solutions
Table 34: Qlick Tech Big Data Financial Management Solutions
Table 35: SAP Big Data Financial Management Solutions
Table 36: SGI Big Data Financial Management Solutions
Table 37: Splunk Big Data Financial Management Solutions
Table 38: Teradata Big Data Financial Management Solutions
Table 39: Tibco Software Big Data Financial Management Solutions
Table 40: VMware Big Data Financial Management Solutions
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. EXECUTIVE SUMMARY
2. 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. 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
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. 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. 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. 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. CONCLUSIONS AND RECOMMENDATIONS
LIST OF 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. EXECUTIVE SUMMARY
2. 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. 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. 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. 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. 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. CONCLUSIONS
LIST OF 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
LIST OF 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
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
LIST OF 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
LIST OF 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
Big Data in Financial Services Industry: Market Analysis and Forecasts 2015 - 2020
1. EXECUTIVE SUMMARY
2. BIG DATA IN FINANCIAL SERVICES
2.1 FINANCIAL SERVICES INDUSTRY
2.2 FINANCIAL SERVICES TRANSFORMS WITH BIG DATA
2.3 MACRO DRIVERS FOR BIG DATA IN FINANCIAL SERVICE
2.3.1 CASHLESS SOCIETY
2.3.1 BIG DATA IN TRADING AND INVESTING COMPLIANCE AND BEHAVIOR LEARNING
2.3.2 THE GOVERNMENT AND BIG DATA IN FINANCIAL SERVICES
2.4 ROLE OF BIG DATA IN FINANCIAL SERVICES
2.5 BIG DATA TO BECOME ESSENTIAL COMPONENT FOR FINANCIAL SERVICE SECTOR
2.6 A THREE-WAY BIG DATA APPROACH TOWARDS FINANCIAL SERVICES
2.6.1 INFORMATION BASED SORTING
2.6.2 INFORMATION BASED BROKERING
2.6.3 INFORMATION BASED DELIVERY
2.7 STEPS FOR BIG DATA FUNCTIONING IN FINANCIAL SERVICES
2.7.1 DATA ACQUISITION, COLLECTION, AND DETECTION
2.7.2 DATA MANAGEMENT AND INTEGRATION
2.7.3 DATA ANALYSIS
2.8 BIG DATA AS COMPETITIVE DIFFERENTIATOR FOR FINANCIAL SERVICES
2.9 FINANCIAL BIG DATA MANAGEMENT: REFERENCE DATA
2.10 FUTURE OF BIG DATA IN FINANCIAL SECTOR
3. BIG DATA INITIATIVES OF FINANCIAL SERVICES PROVIDERS
3.1 CURRENT STAGE OF THE BIG DATA IMPLEMENTATION IN FINANCIAL SERVICES
3.1.1 FINANCIAL SERVICE PROVIDER BIG DATA INITIATIVES
3.2 TOP BIG DATA INITIATIVES IN FINANCIAL SERVICES SECTOR
3.2.1 PROVIDE REAL-TIME RESPONSE TO CONSUMER QUERIES
3.2.2 ASSESS CUSTOMER BEHAVIORAL AND TENDENCY DATA USING PREDICTIVE ANALYTICS
3.2.3 MEASURE CUSTOMER SENTIMENTS AND TAKE APPROPRIATE ACTION
3.2.4 MASS CUSTOMIZATION DATA REMODELING
3.2.5 BIG DATA FOR BIG REVENUE
3.2.6 BIG DATA FOR PREDICTING FRAUD AND OTHER FINANCIAL CRIMES
4. BIG DATA IN FINANCIAL SERVICES: GLOBAL MARKET 2015 - 2020
4.1 THE GLOBAL BIG DATA MARKET
4.1.1 THE UNSTRUCTURED DATA MARKET
4.1.2 THE THIRD PLATFORM PERSPECTIVE
4.1.3 DATA PROCESS MAGNITUDE
4.1.4 TOWARDS THE ZETTABYTES MARKET
4.1.5 GLOBAL MARKETS FOR BIG DATA 2015 - 2020
4.1.6 DATA ANALYTICS IS THE BATTLEGROUND FOR COMPETITION
4.2 LEARNING FROM BIG DATA IN FINANCIAL SERVICES SECTOR
4.3 GLOBAL MARKET FOR BIG DATA IN FINANCIAL SECTOR 2015 - 2020
4.4 FOCUS AREAS FOR FINANCIAL SERVICES SECTOR INVESTMENT 2015 - 2020
5. COMPANIES AND SOLUTIONS
6.1 BIG DATA FINANCIAL MANAGEMENT SOLUTIONS
6.2 COMPANIES AND SOLUTIONS
6.2.1 1010DATA
6.2.2 10GEN
6.2.3 ACTIAN
6.2.4 ALTERYX
6.2.5 AMAZON
6.2.6 ATTIVIO
6.2.7 BOOZ ALLEN HAMILTON
6.2.8 CAPGEMINI
6.2.9 CISCO SYSTEMS
6.2.10 CLOUDERA
6.2.11 CSC
6.2.12 DELL
6.2.13 EMC
6.2.14 FUSION-IO
6.2.15 GOODDATA
6.2.16 GOOGLE
6.2.17 GUAVUS
6.2.18 HP
6.2.19 HITACHI
6.2.20 IBM
6.2.21 INFORMATICA
6.2.22 INTEL
6.2.23 MARKLOGIC
6.2.24 MICROSOFT
6.2.25 MU SIGMA
6.2.26 NETAPP
6.2.27 OPERA SOLUTIONS
6.2.28 ORACLE
6.2.29 PARACCEL
6.2.30 QLIKTECH
6.2.31 SAP
6.2.32 SGI
6.2.33 SPLUNK
6.2.34 TERADATA
6.2.35 TIBCO SOFTWARE
6.2.36 VMWARE
7. CONCLUSIONS AND RECOMMENDATIONS
LIST OF FIGURES
Figure 1: Big Data Approaches for Financial Services
Figure 2: Big Data Functional Levels
Figure 3: Big Data as Competitive Differentiator for Financial Services
Figure 4: Financial Big Data Management Paradigm
Figure 5: Big Data for Predicting Financial Crimes
Figure 6: Big Data Paradigm
Figure 7: Migration Process of Platform Technology
Figure 8: Data Universe Zettabytes Generation 2013 - 2020
Figure 9: Global Big Data Market Forecast 2015 – 2020
Figure 10: Global BD Market by H/W, S/W, and Services 2015 – 2020
Figure 11: Big Data in Financial Services by Components 2015 - 2020
Figure 12: Big Data Revenue Share by Vendor Solutions
Figure 13: Hadoop and NoSQL Vendor Revenue Share
LIST OF TABLES
Table 1: Global Big Data Market 2015 – 2020
Table 2: Global Big Data Markets by H/W, S/W, and Services 2015 - 2020
Table 3: Global Markets for Big Data in Financial Sector
Table 4: 1010data Big Data Financial Management Solutions
Table 5: 10gen Big Data Financial Management Solutions
Table 6: Actian Big Data Financial Management Solutions
Table 7: Alteryx Big Data Financial Management Solutions
Table 8: Amazon Big Data Financial Management Solutions
Table 9: Attiivio Big Data Financial Management Solutions
Table: 10 Booz Allen Hamilton Big Data Financial Management Solutions
Table 11: Capgemini Big Data Financial Management Solutions
Table 12: Cisco Big Data Financial Management Solutions
Table 13: Cloudera Big Data Financial Management Solutions
Table 14: CSC Big Data Financial Management Solutions
Table 15: Dell Big Data Financial Management Solutions
Table 16: EMC Big Data Financial Management Solutions
Table 17: Fusion-IO Big Data Financial Management Solutions
Table 18: GoodData Big Data Financial Management Solutions
Table 19: Google Big Data Financial Management Solutions
Table 20: Guavus Big Data Financial Management Solutions
Table 21: HP Big Data Financial Management Solutions
Table 22: Hitachi Big Data Financial Management Solutions
Table 23: IBM Big Data Financial Management Solutions
Table 24: Informatica Big Data Financial Management Solutions
Table 25: Intel Big Data Financial Management Solutions
Table 26: MarkLogic Big Data Financial Management Solutions
Table 27: Microsoft Big Data Financial Management Solutions
Table 28: Mu Sigma Big Data Platforms
Table 29: MuSigma Big Data Financial Management Solutions
Table 30: NetApp Big Data Financial Management Solutions
Table 31: Opera Solutions Big Data Financial Management Solutions
Table 32: Oracle Big Data Financial Management Solutions
Table 33: ParAccel Big Data Financial Management Solutions
Table 34: Qlick Tech Big Data Financial Management Solutions
Table 35: SAP Big Data Financial Management Solutions
Table 36: SGI Big Data Financial Management Solutions
Table 37: Splunk Big Data Financial Management Solutions
Table 38: Teradata Big Data Financial Management Solutions
Table 39: Tibco Software Big Data Financial Management Solutions
Table 40: VMware Big Data Financial Management Solutions
