In this webinar, experts from VoltDB and Teradata discussed top-of-mind topics to help you gain immediate traction in your IoT architectures. Cheryl Wiebe, Partner, Analytics of Things, Think Big, A Teradata Company and Dennis Duckworth, Dir. Product Marketing, VoltDB will discuss the most impactful IoT use cases and business models, and key technology considerations to ensure effective analytics against IoT data. Finally they will outline real world IoT customer case studies showcasing break-through value. To view the entire webinar, click here: http://learn.voltdb.com/WRTeradataIoT.html

1. page
IOT TOP BUSINESS MODELS AND USE
CASES FOR 2016

2. © 2016 VoltDB
OUR SPEAKERS
Cheryl Wiebe, Partner,
Analytics of Things,
Teradata
Dennis Duckworth,
Dir. Product Marketing,
VoltDB

3. 3
Internet of Things now
worth 14.4 trillion dollar up
from 35 billion dollar.
Is amazing Compound
Hype Cycle Growth Rate
(CHCGR) of 41143%
Big Data Borat ‫‏‬@BigDataBorat 18 Mar

4. 4
• Smart Meters
• Digital Oil
Field
• Delivery
• Refinement
• Wind/Solar
Management
• Lights Out
Production
• Smart
Warehouses
• Smart Cities
• Waste
Management
• City Works
• Sowing and
Harvesting
• Yield
Prediction
• Plant Disease
Diagnostics
• Delivery
Efficiency
• Driver Safety
• Truck
Maintenance
• Insurance
Based on
Driving
• Market
Predictions
Based on
These Other
Markets
• Smart
Hospitals
• Smart Home +
Healthcare
Possibilities for IoT in Your Industry
Gartner: Internet of Things: The Foundation of the Digital Business, Jan 6, 2016“Analytics are essential to the success of IoT systems. They are arguably the
main point of the IoT as they support the decision-making process in operations
that are created in business transformation and digital business programs.”
Roy Schulte and Rita Sallam,
Three Best Practices for Internet of Things Analytics, October 23, 2015

5. 5
Two Major Subsystems
Operations of Things Analytics of Things
Things
Gateway
The Edge
Networks
Analytics
Data Center
Local | Cloud | Hybrid

6. 6
IoT data introduces new complexities
Data management and Analytics must adapt
Key Issues:
• The velocity and volume of the data may be huge
• In some cases, most of the data is unimportant, or redundant
• In some cases there will be a need to increase/decrease collection
• Some data is clearly erroneous
• May need intelligent processing at the edge

7. 7
Two Types of IoT users
Asset Maker / Seller Owner/ operators
Focus
External…the customers
using their products
Internal…their own
operation
Examples
Large Equipment, Auto,
Aerospace
Utilities, Hospitals, Plants, Oil
Exploration / Refining, Cities
Goal
R&D, warranty,
Product as Service
Improve own operations,
product
Analytics at Edge
Data Compression;
Anomaly detection
Fleet coordination; policy
enforcement

8. 8
Use cases for the Asset Maker / Seller of Heavy Equipment:
Condition Based Monitoring & Maintenance
Improving field services for some of the
most complex, transportation and
heavy asset companies out there

9. 9
• Data Collaboration
– Engines
– Aircraft
– Operator
• Improved Maintenance
– Condition-Based
– Predictive
Product as a Service
• Power by the Hour
– Pay for usage
– Known cost projection
• Pay when it works, not breaks
– Supplier/Operator incentives
aligned
• IoT enables model in lower cost
businesses
Commercial Airline
Services

10. 10
GOAL: Deploy scalable
fault probability calculations
– Define remaining useful life of all trucks
– Eliminate downtime and opportunity costs
– Extend new analytics to the edge
Anything less than high performance =
high cost and low customer satisfaction
Post-operation manual
analysis was slow and
unsatisfactory
• Engine sensor data
• Vehicle master &
engineering data
Single system failure detection
required time-intensive
computation for cross-fleet
prediction
© 2016 Teradata

11. 11
Semi Conductor
Owner/Operator of the Manufacturing Site or Campus
Operations Performance Optimization
Helping high tech, discrete and process manufacturers reduce
equipment downtime, plant throughput, and available-to-promise
Paper / Cellulose Processed Food
Consumer Elec.
Contract Mfg

12. 12
Need to analyze a
constantly changing
set up >20,000
attributes to identify
yield loss factors
required flexible
schema AND large
matrix math
Data prep
• Ingest raw data
using JSON
• UnPivot
Sensor Data
• From Semi-
conductor Probes
(>20,000
variables)
• Principal components
analysis
• Linear Regression
• Embedded Visualization

13. 13
Parse machine logs, to
identify significant
machine events.
Pattern matching helps
predicts machine failure
Affinity analysis predicts
likely replacement parts
• Npath Pattern
detection
• Affinity analysis
• Visualize
Process Automation Equipment
• Line equipment; Sensor logs

14. © 2016 VoltDB
VOLTDB OVERVIEW
Mike Stonebraker
FAST
World Record Cloud Benchmark:
YCSB 2.4 millon tps
Other Stonebraker Companies
Customers
Technology
• In-Memory RDBMS with durability
• Scale-Out shared-nothing architecture
• Reliability and fault tolerance
• Super fast ingest and export
• SQL + Java with full ACID transactions
• Hadoop and data warehouse integration
• Open source and commercially licensed (24x7)
• Cloud Ready
Co-Founded by winner of the 2014 ACM Turing Award
Forrester
Wave for In-Memory DB

15. © 2016 VoltDB
VOLTDB: WE DON’T MAKE THE APPS, WE MAKE THE APPS…
15
• Real-time intelligence and context for richer interactions
• Make different decisions on each individual event or person
• Analyze and act on streaming data
• More efficient use of hardware
• 100X faster than traditional databases
• World record performance in the cloud (YCSB)
• Millisecond response time
• High-speed data ingestion
• Simpler apps, easier to test and maintain
• Easier to program with SQL + Java
• Seamless ecosystem integration
• Data is always consistent and correct, never lost
Smarter
Faster
Simpler
1/10
of the Resources Needed
100X
Traditional DB
100%
Consistent, Correct

16. © 2016 VoltDB
Low Complexity
Rich, Smart
Value of Individual Data Item Aggregate Data Value
DataValue
Data
Warehouse
Hadoop, etc.NoSQL
Interactive
Real-time
Analytics
Record Lookup
Historical
Analytics
Exploratory
Analytics
Data in Motion Data at Rest
Fast Data Big Data
Feeds, Collectors
CEP
CEP + DB
VoltDB
DataInteraction
THE FAST DATA + BIG DATA UNIVERSE:
VOLTDB OWNS A UNIQUE MARKET SEGMENT
Teradata

17. © 2016 VoltDB
Streaming
Analytics
- Filtering
- Windowing
- Aggregation
- Enrichment
- Correlations
Deep
Analytics
- Statistical correlations
- Multi-dimensional
analysis
- Predictive/
Prescriptive analytics
Operational
Interactions /
Transactions
- Context-aware
- Personal
- Real-time
+
Big Data
FAST DATA APPLICATION REQUIREMENTS
Fast Data

18. © 2016 VoltDB
VOLTDB FAST DATA USE CASES AND INDUSTRIES
Use Cases
Personalization
Precision resource counting, billing
Real-time policy enforcement
Operations and manufacturing
IoT sensor data processing
Industries
Mobile/Telco
IoT
Media & Entertainment
Financial Services
Retail

19. © 2016 VoltDB
IOT DEPLOYMENTS WITH VOLTDB*: SMART METERS
* More than 60 million meters under management
Leader in the Gartner
Magic Quadrant
Announced Utility Customers
• UK Smart Meter
• ShikoKu Electric Power
• Hokkaido Electric Power

20. © 2016 VoltDB
IOT IS A DEEP STACK: OUR FOCUS IS DATA
Device
Security & Policy
Communication
Edge Compute
More Networks
Centralized Compute
Data Services
Cloud Applications
Fast (in motion)
Streaming Analytics:
real time summary
aggregation, modeling
Transaction
Processing:
per-event decisions
using context + history
Big (at rest)
Exploration:
data science,
investigation of large
data sets
Reporting:
recommendation
matrixes, search
indexes, trend and BI

21. © 2016 VoltDB
IoT WILL REQUIRE FAST DATA OPERATIONAL SYSTEMS
4. Optimized Opera&onal
3. Automated Opera&onal
2. Interactions Opera&onal
1. Monitoring Streaming analy&cs
IoTApplication
Evolution

22. © 2016 VoltDB
VOLTDB CUSTOMERS ARE DOING IT TODAY
Platforms IoT Applications
Huawei
Openet
Nokia
Hewlett Packard Enterprise
+
Smart Energy GB
Mitsubishi’s Smart Metering
Wearables
Home Security / Automation

23. © 2016 VoltDB 23
FAST
DATA
BIG
DATA
Devices
Security/Authorization
Device Management
…
Application1
Application2
Application3
Models/
Historical Insights
Horizontal
Capabilities
Specific
Applications
DATA IN IoT PLATFORMS
…

24. © 2016 VoltDB
Ingest sensor readings from
millions of smart meters
Energy Validation process.
Determine id billing threshold has
been exceeded.
Initiate message to user of
dynamic rate change.
Categorize and filter by meter
type and rate table prior to export
to billing application and other
apps connected through Teradata
Update Operational models and
processes
Events VoltDB
Triggered
Alerting
Teradata
1
3
2
1
2
3
4
4
Rule
Engine
SAMPLE SMART METER SYSTEM ARCHITECTURE
5
5

25. page© 2016 VoltDB
THANK YOU