1. The Internet of Things (IoT) and Analytics
Class 4: Examples of Big Data Analysis
March 10, 2016 Louis W. Giokas

2. This Week’s Agenda
Monday The Different Things of the IoT Tuesday A Look at Communications and Devices Wednesday Cloud Storage and Formats in the IoT Thursday Examples of Big Data Analysis Friday Machine Learning & Analysis Techniques

3. Course Description The IoT generates a vast amount of data.
This data can be used for many purposes, from product design, service and support, marketing, and control.
There are three levels of devices: the things, communications infrastructure and storage.
Tying it all together are analytic techniques.
In this course, we will build from the bottom up and then look at how the analytics infrastructure can be used in applications.

4. Today’s Agenda
IoT Analytics Benefits
Use Cases
Platforms

5. IoT Analytics Benefits
Business Transformation
Efficiency and savings
Internal analysis of operations
Growth opportunities
Plant utilization
Supply chain efficiencies
Market opportunities
New business models
New revenue streams

6. IoT Analytics Benefits
Process automation
Remote monitoring
Visibility into asset health and maintenance
Responsive asset management
Predictive Maintenance/Asset Management
Industrial
Public infrastructure
Better budgeting
Responsive manufacturing
Automated planning

7. IoT Analytics Benefits
Business Responsiveness
Respond to:
Competition
Supply chain changes
Customer demand
Market changes
Process change
In response to the above
Automate analysis

8. Use Cases Product development
Understand how existing, similar products are used
Track issues with current version
Actual product use data from your own data
Service calls
Collected data (devices self reporting)
Social media reaction
External factors
Combine the data to plan future versions and enhancements

9. Use Cases Product marketing Detect industry trends External factors
Weather
Competitors
Social media
What is “trending”
Current product performance

10. Use Cases Product Lifecycle Management (PLM)
A growing area of product development and design
Encompasses many of the previous use cases
Integration of many data sources with CAE, CIM and CAD systems
Drive product decision with data
Release schedules
Pricing

11. Use Cases Predictive maintenance Find trends and predict failure times
Proactive vs. reactive
Schedule maintenance and upgrades
Merge IoT data with other schedule information
Customer requirements
Software upgrades
Many products with embedded processors can be made more efficient with a software change
Simulate to predict improvements
Test against real data.

12. Use Cases Predictive Maintenance
Benefits
Identifies key prediction factors
Determines likelihood of predicted outcomes
Optimizes decision making
Systematically apply institutional knowledge
Extending asset life
Uncover root causes
Determine optimum correction actions
Enhance diagnostic capabilities

13. Use Cases Predictive Maintenance
Data Dimensions
Structured
Industrial control systems (e.g., SCADA)
ERP
CRM
Financial
Unstructured s
Operator logs
Social media
Streaming
PLCs
Telemetry
Weather

14. Use Cases Predictive Maintenance
Analytic Techniques Used
Data Mining
Anomaly Detection
Clustering
Classification
Regression
Text Mining
Machine Learning
Learn from the data
Simulation

15. Platforms Many automation vendors are offering platforms and solutions
General Electric
Siemens
Software vendors are also creating platforms for IoT analytics, integrating the various data sources
Ansys
IBM

16. Platforms GE: Predix.io

17. Platforms GE: Predix.io
Framework for developing Industrial IoT Analytic applications
Industry specific packages
Brilliant Factory
Digital Power Plant
Many more…
Lots of partners

18. Platforms Siemens PLM
A set of software technologies geared toward product design and development
Centered around Product Data Management (PDM)
Other components include
CAD
CAM
CAE (including simulation)
FEA
MOM (Manufacturing Operations Management)
Testing
Digital manufacturing

19. Platforms Microsoft
Software based, general purpose analytics infrastructure for IoT Analytics
Brings together existing software products
Cloud based (Azure)
This is a toolkit with specific analytic tools targeted to the IoT
Azure HDInsight
Azure Machine Learning
Azure Data Factory

20. Platforms Microsoft

21. Platforms IBM
Another software platform utilizing existing tools with IoT specific applications and architecture
IBM Bluemix cloud platform, or other cloud platforms
Watson for deep learning analytics

22. Platforms IBM

23. Summary and Preview
Today we have discussed three aspects of IoT Analytics
Benefits
Some use cases
Platforms
Tomorrow we will look at:
Machine Learning
Analysis Techniques
Statistical Methods