- Real-Time Big Data & Artificial Intelligence in a Sensor-Rich World

1. Confidential
Real-Time Big Data &
Artificial Intelligence in a
Sensor-Rich World
November 2017

2. 2 Confidential
10 Years Before Big Data

3. 3 Confidential
The Data-Insight-Action cycle
Execute action
Sensor
Environment
Capture new data
Analysis
& insight
ExploratoryAutomated
Execute
action
Correlate & consolidateDashboards
& monitoring

4. 4 Confidential
Real-Time Big Data Needs A New Architecture
DATA VARIETIES
ACROSS SILOS
STREAMING DATA
VELOCITY & VOLUME
TIMELY INSIGHTS &
ACTIONS
DISTRIBUTED DATA
SOURCES
Time and Money
Big Data
Challenges for
Sensor-Rich
Industries CARDINALITY &
DIMENSIONALITY
QUERYTRANSPORT STORE, INDEX & REPLICATE
Time
Traditional Approach: Store-First, Analyze Later
Centralized, Batch Architecture Reactive, Rear-view Analysis
Timely actions infeasible because data needs to be
stored & indexed prior to analysis
Proactive, Forward-looking Prescriptions
Streaming analytics enables real-time action
Streaming Analytics
Distributed, Continuous Analytics, Timely Actions
QUERY &
ANALYZE
TRANS-
PORT
ACT & ARCHIVE
Time
Cost & Time Savings

5. 5 Confidential
Streaming Sensor Data Needs Artificial Intelligence

6. 6 Confidential
Real-time Big Data: Today & Tomorrow
Today: Self-Assembly The Need

7. 7 Confidential
Autonomously
Recognize
Autonomously
Reason
Autonomously
Respond
Artificial Intelligence: Today & Tomorrow

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The Emerging Decisoning Fabric for a Sensor-Rich World
▪ HOLISTIC, SCALABLE: Built on streaming architecture for real-time actions
▪ INTELLIGENT: Recognizes patterns, Reasons about anomalies and Responds with a prescription
▪ SECURE: Secured access, Open Models, and Expert-in-the-loop automation
▪ FAST TIME TO VALUE: Agile, use-case led, app-store approach to analytics
Security
Sensors & Connectivity Big Data &
Artificial Intelligence Automation

9. 9 Confidential
Example: Predict & Respond to Failures to Reduce Service Costs
The Challenges Guavus solution The Results
$70M in
savings in the
first year
• Difficult to diagnose failures
• Expensive truck rolls ($80 per roll) were
being needlessly sent to customer sites
to resolve plant issues
• Diagnosed root cause of customer
complaints without going on site to
avoid sending expensive tech for plant-
based issues
• Automated triggers to customer IVR
• Reduced service costs related to
unnecessary truck rolls by $70M in
the first year
The ResultsThe SolutionThe Challenges

10. 10 Confidential
Example: Detect & Stop Fraud to Improve Customer Experience
The Challenges Guavus solution The Results
$40M in
annual ROI
• A small number of customers abusing
the network with huge amounts of data
while concealing their tethering usage
(sometimes up to 2 TBs/month)
• Detected illegal tethering usage
• Identified heavy users and understood
the tethering traffic
• Increased revenue by $12M+ from
upsell in the first year
• Saved $28M+ in the first year
• Improved customer experience by
reducing traffic congestion issues
The ResultsThe SolutionThe Challenges

11. 11 Confidential
Full Context Correlation
Any Data In Motion Any Data at Rest
Sensors, Systems & Networks Information Systems
Recognize & Reason
Highly Available | Virtualized | Horizontally Scalable | Real-time & Forensic | Public & Private Cloud | Distributed | Modular | Flexible Data Models | Integration Friendly
Guavus & Thales: The Vision

12. 12 Confidential
Automated Response.
(Operator-Assisted) Timely & Targeted Response
Continuously Analyze.Continuously Measure. Continuous
Analytics &
Dynamic
Response
Towards Secure, Intelligent, Automated Decision Systems

13. Confidential
Thank you
November 2017