2018 Women in Analytics Conference https://www.womeninanalytics.org/ Cheryl will talk about her consulting practice in Industrial Solutions, Analytic solutions for industrial IoT-enabled businesses, including connected factory, connected supply chain, smart mobility, connected assets. Her path to this practice has bounced between hands on systems development, IT strategy, business process reengineering, supply chain analytics, manufacturing quality analytics, and now Industrial IoT analytics. She spent time working in industry as a developer, as a management consultant, started and sold a company, before settling in to pursue this topic as a career analytics consultant. Cheryl will shed light on what's happening in industrial companies struggling to make the transition to digital, what that means, and what barriers they're challenged with. She'll touch on how/where artificial intelligence, deep learning, and machine learning technologies are being used most effectively in industrial companies, and what are the unique challenges they are facing. Reflecting on what's changed over the years, and her journey to witness this, Cheryl will pose what she considers important ideas to consider for women (and men) in pursuing an analytics career successfully and meaningfully.

1. Cheryl Wiebe, Industrial Intelligence,
Think Big Analytics, Teradata
Getting Value from Industrial Analytics
© 2018 Teradata

2. 2
• Industrial IoT and
the Analytics of Big Things
• What is stopping us?
• And what are we doing about
it?
© 2018 Teradata
Getting Value out of
Industrial Analytics

3. 3
Consumer IoT vs Industrial IoT
Cool New Applications
Industrial Transformation
© 2018 Teradata

4. 4
Really BIG things!
© 2018 Teradata

5. 5
Really BIG things!
© 2018 Teradata

6. 6
Really
AUTONOMOUS
things!
© 2018 Teradata

7. 7
Paint a digital picture of a
machine in “real time”
Combining multiple data
domains
Machine learning, soon AI
Digital Twin: Predicting machine behavior
Predict machine behavior
Suggest best actions
© 2018 Teradata

8. 8
Haul Truck Engine: Failure Prediction and Monitoring
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
Challenging to integrate
and analyze
• Engine sensor data
• Vehicle master &
engineering data
Single system failure detection
required time-intensive
computation for cross-fleet
prediction
© 2018 Teradata

9. 9
Smart Mobility Analytics:
First mile / last mile. Find/predict a City’s worst problems
Resident Geo-locations and Movement
Data can show us where people live versus where they work, and the routes they
travel between home and work site
Individuals leave accessible data trails that yield valuable detail on travel routes
2. Example of one individual’s journey from zip code 43229
Inbound
Outbound
Evening
Using multi-domain data: individual movement + public transit+ POI
Data Aggregation Shows Visit Patterns
Knowledge of when people visit different
locations within the city highlights mobility need
4. Leads to aggregate pathing trace showing volume of
travelers and routes taken through the city and environs
1. Using available smartphone app data, we can
determine home zip code of downtown workers
0 SS M T W T F 24
3. Visitors to Zip 43215 (by hour & day of week)
© 2018 Teradata

10. 10
Smart Mobility Analytics:
First mile / last mile. Find/predict the worst commutability problems
Using multi-domain data: individual movement + public transit+ POI
© 2018 Teradata

11. 11
Road objects,
traffic and
accident events
are manually
reported or not at
all
Real-time
identification
Automated object
detection and scene
labeling system from car
camera feed to improve
navigation and traffic
Darknet
Caffe
TensorFlow
Deep learning: object detection
Real-Time
Streaming
Streaming
Results
Traffic Data Service
Navigation Update
Darknet/Darkflow –
Object Detection
TensorFlow – Scene
Labeling
Cloud GPU
Based
Training
TF Serving
Cloud GPU
Based
Inference
Model
Updates
Please hover over the image to PLAY VIDEO
© 2018 Teradata

12. 12
Demo
© 2017 Teradata
© 2018 Teradata

13. 13
Now, AI and computer vision
Factory of the Future
© 2018 Teradata

14. 14
© 2017 Teradata
The possibilities for value are tremendous!
1st Pass Yield
Productivity
Morale
Safety
Quality
SG&A
-$104M
COGS
-$22M
Revenue
+$17M
% Rework
RCR
LWDCR
Labor Loss
Production
Rate
Final Assy
Flow
Unit Hours
Stores
Plant XYZ
Plant ABC
Ratio Analysis
Xxx Program
yyy Program
zzz Program
Program performance
© 2018 Teradata

15. 15 © 2014 Teradata
What stops us from getting all the value from analytics?
Industrial settings intensify these issues
…Technology differences …chasm between
discovery and
operationalizing
…Access to data
Physical challenges in
gaining access to data in
the wild
Multi-generation systems
span many technologies
OT vs IT divide:
Technology and Culture
differences.
…Domain silos
Multiple domains need
multiple skill sets
…Domain silos
Multiple domains need
multiple skill sets

16. 16
By themselves, sensor data are of only limited value
© 2018 Teradata

17. 17
SENSOR MASTER OPERATIONS
REPAIR &
MAINTENANCE
SUPPLY
CHAIN
HR FINANCE
Probability that component will fail? ✓ ✓ ✓
Probable time to failure? ✓ ✓ ✓
Where is the train now – and next few hours? ✓ ✓
Probable issue and resolution? ✓ ✓ ✓
Availability of required spare parts? ✓ ✓
Availability of suitably qualified Engineer? ✓ ✓
Replacement train available? ✓
What is the impact of cancellation of service? ✓ ✓
By themselves, sensor data are of only limited value
© 2018 Teradata

18. 18 © 2018 Teradata

19. 5
USERS
LAYERED DATA ARCHITECTURE
DATALAB
Virtual Sandboxes
& Prototypes
PRESENTATION
Application Specific Views
AGGREGATIONAL
BU Specific Rollups
CALCULATION
Key Performance Indicators
INTEGRATION
Integrated Model at
Lowest Granularity
STAGING
1:1 Source Systems
4
3
2
1
0
BUSINESS
ANALYSTS
POWER
USERS
DATA
SCIENTISTS
USER
OWNED
ATOMIC
DATA
BUSINESS
RULES
Copyright Teradata
TERADATA CONFIDENTIAL
DO NOT COPY OR DISTRIBUTE WITHOUT THE EXPRESS WRITTEN CONSENT OF TERADATA

20. 20 © 2014 Teradata
What stops us from getting all the value from analytics?
Industrial settings intensify these issues
…Technology differences …chasm between
discovery and
operationalizing
…Access to data
Physical challenges in
gaining access to data in
the wild
Multi-generation systems
span many technologies
OT vs IT divide:
Technology and Culture
differences.
…Domain silos
Multiple domains need
multiple skill sets
…chasm between
discovery and
operationalizing

21. 21
• Monitoring and alerting
based on operational model,
• May require real-time
• SLAs and IT standards are
enforced
• Well-defined workflow, high
performance, high volumes
production
• Iterative, creative
• Hypothesis generation /
validation of theories
• Exploration of new data,
relations, drivers, and
predictors
• Ad hoc workflow, lower
performance needs
Discovery vs Production
discovery deployment
• Model conversion
• Port, optimize, re-
architect for
performance at scale
• Compliance with IT
standards and tools
• Policy/procedure fit
• Process integration
© 2018 Teradata

22. 22
Analytic
Opsexternal data
iteration
data-mining
statistics
value-driven
flexibility
exploration
discovery
modelling
blue-sky ideation
DISCOVERY ANALYTICS
OPERATIONS ANALYTICS
security
governance
compliance
curation
deployment
maintenance
integration
testing
engineering
process-driven
© 2018 Teradata
Bridge the divide between data science, software engineering and DevOps

23. 23
Analytic Ops is DevOps for Data Science
Development
Batch Data
Model Business Process
Configuration
(Scheduling, Reports & Approvals)
Models Pipeline via templates
(Training, Test,
Champion/Challenger)
Data Wrangling & Feature
Engineering
Check-in model via Git “pull
request’ to trigger CI
CI Integration (e.g. via Docker)
(package model dependencies)
Automate
Store model metadata &
artefacts (PMML/POJO)
Run unit tests & model
pipelines
Scoring performance tests
Champion/Challenger
model evaluation
Models & Business reports
creation
Consume
Build & deploy scoring engine
with new models artefacts
Log outputs for continuous
monitoring & improvement
Apply A/B or gradual
deployment strategies for
new models artefacts
Build & deploy scoring
pipelines & RT queues for
new models artefacts
Real-Time Batch Data
Mainframe, real-time streams, batch,
business solutions (e.g. Financial Crime)
Value
© 2018 Teradata

24. 24 © 2018 Teradata

25. © 2018 Teradata