1. Jay Mason, Associate Partner, Big Data & IoT M&S Consulting
Industrial IoT Platform Services for Analytics and Machine Learning [843708]
Jay Mason, Associate Partner, Big Data & IoT M&S Consulting
Oracle BIWA Summit 2017

2. Speaker Bio - Jay Mason
20+ years working with Oracle Business Intelligence and reporting solutions
Worked with machine data (SCADA PLCs) since the 1980s
Completed multiple successful IoT implementations from POC to Production
Leader of Big Data, IoT and IDM practices at M&S Consulting

3. Agenda Overview of IoT IoT Use Cases in Elite Sports
Industrial IoT Platforms
Industrial IoT Analytics
Industrial IoT Deployment

4. How This IoT Presentation is Different
No predictions on the size of the IoT market
No Gartner Hype Cycle

5. Challenges of Industrial IoT

6. IoT Analytics Predictions for 2017
New Predictions for 2017
The “edge” will become a huge growth market
DDoS attacks from IoT devices will continue
AI/Deep Learning will become a reality
And the common predictions from prior years will be repeated
Self-Service BI will become the new normal
More disparate data sources and formats
More actionable, automated decision capability

7. USA Cycling IoT Use Case
Times automatically fed to IoT Platform using MQTT protocol
Integration with other data feeds (power, heart rate, etc.) for advanced analytics
Coaches and athletes have information right after each practice run

8. USA Cycling IoT Solution Architecture

9. US Speedskating Use Case
Re-used solution from USA Cycling with minimal modification
Challenge: Frequent connectivity issues and power outages
Recommendation: Store copy of original messages for validation when proving out solution

10. Common IoT Platform Capabilities
Security
Analytics
Hadoop scale data management and storage
(Near) Real-time data insights
Application Development & Integration
HTTP, MQTT, CoAP
SDKs / APIs
Device Features
Device commands, provisioning, device registries, libraries, and virtual devices
Workflow / Event Processing
Monetization / Marketplace

11. End-to-End IoT Security
Unique device identity
Authentication
Transport-level security
Device metadata and lifecycle management
Secure every layer from the device to the cloud

12. Industrial IoT Platform Comparison
mandsconsulting.com/industrial-iot-platform-comparison

13. Sample Industrial IoT Platforms
Amazon
AT&T
Autodesk
Ayla Networks
Blackberry
Bosch
Bright Wolf Strandz
C2M
C3 IoT
Carriots
Cisco Jasper
Concirrus
Connecthings
Connio
Cumulocity
Davra Networks
Device Insight
Echelon
Emerson
EVRYTHING
Exosite
FIWARE GE
GroveStreams HP
IBM
Intel
Itron
Kaa
Losant
M2Mi
MachineShop
Microsoft
Octoblu
Oracle
PLAT.ONE
Preva Systems
PTC
Salesforce
SAP
Sentilo
Siemens
T-Mobile
thethings.iO
ThingPlus
Verizon
Waylay
Wind River
Workpad IoT
Wot.io
Xively
Zebra Technologies

14. IoT Capabilities - Connect, Analyze, Integrate
Source: Extending Enterprise to the Edge | White Paper | Oracle and Wind River

15. IoT Platforms and Spark Integration
Amazon IoT and Apache Spark on Amazon EMR
IBM Watson IoT Platform and IBM Analytics for Apache Spark for Bluemix or IBM Streaming Analytics for Bluemix
Intel IoT Platform and Intel Data Analytics Acceleration Library (Intel DAAL)
Microsoft Azure IoT Suite and Apache Spark for Azure HDInsight
Oracle IoT Cloud Service and Oracle Big Data Cloud Service
SAP HANA Cloud Platform for IoT and SAP HANA Vora

16. IoT Platforms with Integrated ML
Amazon IoT and Amazon Machine Learning
GE Predix
IBM Watson IoT and IBM Watson Machine Learning (Coming Feb. 15th)
Intel IoT Platform
Kaa IoT Platform
Microsoft Azure IoT Suite (includes Azure ML)
SAP HANA Cloud Platform for IoT and SAP HANA Smart Data Streaming
thethings.iO
ThingWorx IoT Platform
Verizon ThingSpace IoT Platform

17. IoT PaaS Marketplaces AWS Marketplace Predix Catalog
IBM Cloud Marketplace
Microsoft Azure Marketplace
Oracle Cloud Marketplace
ThingWorx Marketplace
Salesforce AppExchange
SAP HANA App Center

18. Oracle IoT Cloud Services Analytics
Select raw data streams
Choose a data analysis pattern
Route analyzed streams to services/applications

19. With IoT, Context is King
Context defines the state of an environment (usually the user’s environment) in a certain place at a certain time.

20. IoT Integration and Workflow
(CBM = Condition Based Maintenance; PdM = Predictive Maintenance)

21. Oracle IoT Cloud Service Integration
Oracle Business Intelligence Cloud Service
Oracle Integration Cloud Service
Oracle Mobile Cloud Service
Enterprise Applications
Oracle E-Business Suite
Oracle Asset Tracking
Oracle JD Edwards EnterpriseOne
Oracle Transportation Management

22. Reference IoT Layered Architecture

23. Industrial IoT Analytics Architecture

24. Centralized IoT Data Store
Store data streams from many disparate data sources
Standardized data regardless of device protocols
Drives analytics for realizing the benefits of IoT
Centralized repositories are where big data and analytics can be applied to extract insights
(Preferably) Optimized for time series analysis

25. IoT Data Processing Options

26. Edge Computing/Analytics
Move the processing to where the action is
Minimize data transfer
Minimize network latency

27. Cloud and Fog

28. Digital Twins
Oracle IoT Cloud, GE Predix and other major IoT PaaS services offer implementation of the Digital Twin through:
Virtual Twin
Predictive Twin
Twin Projections

29. Twin Projection Purpose: Integration of insights
Triggering appropriate processes/workflows
Accessing data for decision support from business apps
Allowing business apps visibility into current and predicted machine states and environment

30. Predictive Modeling with Oracle IoT Cloud Service
Spectrum of complexity
Simple models typically based on trends and patterns
For these, Oracle Stream Explorer (included in the IoT Cloud) is sufficient
More complex models can be created using Apache Spark based analytics engine in the Oracle IoT Cloud
Typically developed using Oracle R Advanced Analytics for Hadoop (ORAAH)
These R models can then be executed in the IoT data pipeline
Oracle IoT Cloud offers libraries for working with Time Series data
Business users can use simpler interfaces provided by the Oracle Big Data Discovery product

31. Machine Learning Integration
Machine Learning capabilities integrated with Industrial IoT Platforms
Built-in data processors
Training models
Automation
Alerts / Monitoring
Tuning
Visualization tools

32. IDC MaturityScape IoT Maturity Model

33. IoT Phased Deployment from IDC
Connecting devices and assets
Applying real-time and predictive analytics and machine learning
Achieving service excellence

34. Key Takeaways Connect, Analyze, Integrate
Edge computing/analytics minimizes data transfer and latency
Context is King
Device data does not exist on an island → Integrate!