The disruptive business models in spatial analytics
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Transcript of The disruptive business models in spatial analytics
The disruptive business modelsin spatial analytics
Bhoopathi RapoluHead of Analytics – EMEA | CYIENT Europe Ltd.
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Driven by Big Data & IoT
GEOSPATIAL WORLD FORUM 2016 Rotterdam, The Netherlands
agenda• Location Intelligence
– the new frontiers
• 5 Disruptive Forces
• Disruptive Business Models
• CYIENT - Leading the journey
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Location intelligence
The New Frontiers
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Location Intelligence – the new frontiersC
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of corporate data has
spatial significance
BI provides WHAT, WHEN &
WHO…
Spatial provides WHERE and
makes better sense of HOW &
WHY
RELEVANCE
RESPONSE
REVENUE
RESULTS
Extending the gamut of LocationC
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Every “thing” of economic value is spatially relevant…
SOLOCO
Socialise Localise Commercialise
5 disruptive trends
The New Opportunities
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Sharing Economy
5 Disruptive trendsC
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Location
Intelligence
Mobile
IoT
Big Data
Autonomous Agents
1. Widespread adoption of smartphonesC
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Source:* Ericsson
**Pew Research Internet project, 9/2013
Global Smartphone traffic to increase by 8X by 2020• Location is ubiquitous
with cell triangulation
• Consumers opting to share their location voluntarily
• >74% of consumers with smartphones use location based services**
• App proliferation
17 EB
2.1 EB
3.5 GB
900 MB6.1 B
2.7 B
Smartphonesubscriptions
Monthly trafficPer active subscription*
Total monthly Smartphone traffic
2014 2020
2. Ability to connect things affordablyC
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Most of the IoT applications have spatial significance
• Vehicles, cell phones, assets and even pets
Location and Time play pivotal role in building spatial intelligence• Show me vehicles passed by location X in
the past hour• Where has my car been over the last few
hours
3. Ability to process big data in real-time and offlineC
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If there is location, there is location intelligence, almost instantly.
• 28 to 32 year old, middle class single male. • Spends lunch hours at the gym. • Frequents nightlife hotspots on Fridays. • Visits mountains on weekends. • Air travel 20% of the time. • Outdoor, active lifestyle pursuits & interests.
Human Smartphone Location Relevant data Context Analytics Applications
Location intelligence is underutilized in most BI applications
3. Ability to process big data in real-time and offlineC
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• Speech recognition• Natural language processing• Face detection• Medical diagnosis• Customer profiling• Fraud detection• Spam detection• Shape detectionBusiness Data
ERP CRM OLAP
Complementary DataDemographics Business
statistics
Spatial DataPoI people things
Sensor DataGPS RFID Other sensors
Spatial Database
Spatial data ETL Spatial data integration
Spatial AnalyticsTraditional Analytics
Spatial Display Traditional DisplayVisualization
Analytics
Common capabilities of Machine Learning
4. Autonomous Agents and ThingsC
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ObviousUnobtrusive
Physical
Virtual
Robots
Drones
Driverless vehicles
Speech to Text
Security Operations
EnterpriseApps
Sensors
Smart Appliances
Cognitive Services
Smart Advisors
Virtual Assistants
Smart Tools
Autonomous agents and things sense their environment and produce rich data.
They get richer context with location intelligence when they interact with their environment
5. Sharing EconomyC
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Communication Web 2.0Social Media &
Sharing Economy
Location IntelligenceWeb ServicesInternet
Disruptive Business Models
Where is the Money?
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The Disruptive Business ModelsC
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Accumulate context to create untapped business value
Location
Intelligence
Mobile
IoT
Big Data
Autonomous Agents
• Resolve data sources across multiple disruptive forces
• Integrate new forces with existing ones
Sharing Economy
• Spatial Reasoning
• Temporal Reasoning
• Nodal Reasoning
• Create possibilities
• Predict Outcomes
• Alert opportunities
Tap Multiple Forces
Accumulate Context
Determine Relevance
Create Business Value
Observation space for Location IntelligenceC
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LBS Interplay
Business Data
Dynamic Data
• Weather• Traffic• Risk & Hazards• Asset condition
• Business Demographics
• Consumer Demographics
Map Info
• Navigation• Elevation & terrain• Point of Interests
Third Party Data
• Transportation• Comms. networks• Utilities
• Agriculture• Healthcare
Lack of robust revenue model is the primary reason for failure of most of the LBS providers
The money is with:• Rich spatial data• Attractive platform for advertisers • Compelling applications• Cross functional applicability
Cyient
Leading the journey of Spatial Analytics
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Thank You
Bhoopathi RapoluHead of Analytics – EMEA
Email: [email protected]: @bhoopathi
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