The document discusses industrialized analytics and artificial intelligence. It outlines the need for a better way to manage data, analytics pipelines, and machine learning models at scale. The presentation covers topics like AI business trends, challenges of data management, and a vision for operationalizing, integrating, managing and monetizing AI using concepts like DataOps. Implementing industrialized AI through DataOps can help deliver analytics solutions with continuous delivery, quality outputs, flexibility and speed to insight and value.

1. July 14, 2020
Industrialized Analytics and
ArtificiaI Intelligence
Pedro Martins | DXC Technology
pedro.martins@dxc.com

2. July 14, 2020
Industrialized Analytics and
ArtificiaI Intelligence
Pedro Martins | DXC Technology
pedro.martins@dxc.com

3. July 14, 2020 3
Agenda
• AI Business & Market Trends
• Good and Bad Data Management
• Industrializing AI
• The Vision
• The Method
• Q&A

4. July 14, 2020 4
What is
Industrialized Analytics & AI?

5. July 14, 2020 5July 14, 2020
Today, the way we manage Data,
Data Pipelines, Analytics Pipelines,
Machine Learning models is messy
and doesn’t scale

6. July 14, 2020 6July 14, 2020
What if there was a better way?

7. July 14, 2020 7July 14, 2020 7
AI Business & Market Trends
of CEOs are planning
organic growth initiatives
80%
Organizations adopting AI expect
to see a 39% average increase
in revenue and 37% average
decrease in cost by 2020
76% of business decision
makers see AI as pivotal to the
future success of the
organization
Most industries have captured
less than 40% of the potential
value in their data
71% of business decision
makers believe the rise of AI is
inevitable and positive for
business prospects

8. July 14, 2020 8
What are the challenges?
90% of
companies
report
challenges to the
adoption of AI
40% of
companies say
they lack
understanding of
the benefits and
intended uses of
AI
40% believe
that time to
implement and
integration with
existing systems
is a major barrier
to adopting AI
Only 10% of
companies that
have deployed
AI believe they
are getting the
full benefit

9. July 14, 2020 9
What are the needs?
Quickly develop,
deploy, and test
AI experiments
Quickly turn AI
experiments into
fully-functioning
enterprise
applications
Turn unused
data into
potential sources
of business
insight
Identify AI
applications with
the highest
potential
business value

10. July 14, 2020 10
What Does Bad Data Management
Look Like?

11. July 14, 2020 11
Bad Data Management (I)
• Fraud
• Criminal Activities
• Money Laundering
• Tax Evasion

12. July 14, 2020 12
Bad Data Management (II)
• Data Loss
• Data Inconsistencies
• Data Incorrections

13. July 14, 2020 13
Bad Data Management (III)
• Poor Customer Experience
• Customer Loss
• Revenue Loss
Source: Gartner, 2018
https://www.gartner.com/smarterwithgartn
er/how-to-create-a-business-case-for-
data-quality-improvement/

14. July 14, 2020 14
What Does Good Data Management
Look Like?

15. July 14, 2020 15
Source:
1 “Gauging investment in self-driving cars,” Cameron Kerry and Jack Karsten, The Brookings Institution, Oct 2017
2 “Top 10 Worldwide Connected Vehicle 2019 Predictions”. IDC, Nov 29, 2018
3 https://www.marketwatch.com › Industries. Huston, May 27, 2017
$80B
had been poured into AD development
through June 2017 by automakers and
other parties. 1
5%
will replace dedicated vehicle usage with
mobility-as-a-service by 2022. 2
of urban households
$285B
Ride-hailing industry could achieve an
eightfold increase to
by 2030. 3
50MB
By 2020, level 4 & 5 autonomy fleets will require
of cellular data per hour
for safe vehicle operation and consistent
rider satisfaction. 2
$80B
had been poured into AD development
through June 2017 by automakers and
other parties. 1
5%will replace dedicated vehicle usage with
mobility-as-a-service by 2022. 2
of urban households
$285B
Ride-hailing industry could achieve an
eightfold increase to
by 2030. 3
50MB
By 2020, level 4 & 5 autonomy fleets will require
of cellular data per hour
for safe vehicle operation and consistent
rider satisfaction. 2
Autonomous driving will redefine mobility

16. July 14, 2020 16July 14, 2020 16
Key challenges of AD development and testing
16
Autonomous
Vehicles (AV)
regulations
Speed and
accuracy
Reliable, safe
vehicles
Test vehicle
data in PB scale
Standardization
Data Volume
and Security
Artificial Intelligence (AI)
as New Discipline
Political and Legal
Implications
Public Support
and Safety
Mass-Produce AV
at Scale
Simplify and expedite
data collection,
storage and
management, securely
>100 million
lines of code and >15
times more complex
than aircraft software
2.2 to 30.4 PB
of sensor data
generated by Waymo
test fleet per day
1000s
decisions made for
every mile traveled
New field of case law
and automobile
insurance
By 2021, 20%
of major cities will
support AV pilots,
transit innovation,
improve road safety
Transparency in
testing and
verification
By 2024, 20%
of AV owners will
network vehicle digital
twins into their
ecosystems
Global data and
compute distribution
Open ecosystem for
exchanging data and
algorithms for
verification and
validation

17. July 14, 2020 17July 14, 2020 17
BMW Autonomous Driving Platform
Logical Components
Benefits
Proven Platform
with comprehensive
Services
Allows access to the
data ➔ ignore the
volume
Use state of the art
technology ➔ result
in seconds
Integrate in legacy
development
process
Reduced need to
invest in car;
shorten loop time
Faster scale to ad
level 3; supporting
safety
Scientific support
and extended AD
workbench
Test/Road approvalRecomputeSimulate
Fusion/
Motion control
Perception &
location
Manage, find &
analyze
Collect/Ingest/Store
What the OEM’s want
to do
Time, Drive Time, Analyze Evolve & decide Street Approved
Why it Manage data in volume | Data science challenge on a petabyte scale | State of the art AI on the right data
Robotic
Drive
Toolkit
Cluster
In car
Robotic Drive
Optimizer
Instant Access
Robotic Drive Actor
Functional Testing & Process
Control
Geo-Distributed Data Lake
End Users
Access Rights
Radar other xxx
control data
Image Lieder
Analyze & learn to
know your data
FPGA
Simulate
Simulate new
scenarios
How many wrong
decisions ~10,000
0.001
Hil
Sil
Recompute
algorithms
at scale
Enrich
sensor
data
On Target
Disengagement
<0.001
Robotic Drive Trainer
Robotic Drive Analzyer
Robotic Drive Ingestor

18. July 14, 2020 18July 14, 2020 18
Data Centers
3 Robotic Drive Trainer
BMW Autonomous Driving Platform
The Big Picture
7 Instant Access
Remote Work
Platform
R&D Engineers
OEMs Suppliers Consumers
3 Robotic Drive Trainer
Annotation
Teams
Data Scientists
Data Analysts
1
Geographic Distributed
Data Lake
2 Robotic Drive Analyzer
4 Robotic Drive Ingestor
5
Robotic Drive
Optimizer
6 Robotic Drive Actor
Car
ClusterBuilding blocks in
car
3 Robotic Drive Trainer

19. July 14, 2020 19
What is the road to
Industrialized Analytics & AI?

20. July 14, 2020 20
Monitor and predict passenger booking
Improve booking
• Reduce booking errors and issues
• Improve handling delays and cancellations
• Predict booking fraud
Predict service needs
Improve service quality
• Improve meal and on-board logistics
• Improve target group(s) experience
• Improve lounge experience
Augment operations
Improve operational efficiency
• Optimize revenue from bookings
• Improve delays and cancellations response
• Increase utilization of crew and fleet
Automate the customer journey
Distinguish from competition
• Introduce efficient, timely, and personalized
service to customers
• Provide unmatched performance with minimal
cost using as much AI as possible
Monitor and predict booking fraud
percent potential increase in revenue10
Automate resolution of booking
issues
percent improvement in operating
margin due to better booking
management and less support calls
25
Services supply
percent potential decrease in
supply expenses
30
Predict operational issues
percent potential increase in
productivity and fly more customers
20
Automated logistics and
disruption recovery
percent potential cost savings from
less penalties, delays, and overtime
10
Augment booking experience
percent potential increase in
bookings and ultimately profits
23
AI Starts with a Vision
Predict booking issues
Forecast On-board
Services
Fraud Detection
Lounge Demand
Forecasting
Predict Target
Customers Behavior
Optimize
Booking
Real-time, Automated
Customer Satisfaction Offers
Real-time Supply and Demand
Matching
Overall
percent estimated boost in
customer satisfaction resulting in
increased travel and loyalty
25
Schedule Planning
Optimization
Crew Planning
Optimization
Re-routing
Optimization
Fleet
Optimization
Predictive Rebooking & Re-routing
Monitor and predict fleet and crew
utilization
percent potential increase in on-time
flights and better issue response
15

21. July 14, 2020 21
AI
AI: …

22. July 14, 2020 22
AI AI
AIAI
AI
AI
Operationalize AI
AI
AI: Operationalized

23. July 14, 2020 23
AI AI
API
API
AIAI
AI
AI API
• Configurable (customized) solutions
• Microservices
• API-based integration
API-based orchestration allows for decentralized coordination
Integrate AIOperationalize AI
AI
AI: Operationalized + Integrated

24. July 14, 2020 24
AI AI
API
API
AIAI
AI
AI API
• Configurable (customized) solutions
• Microservices
• API-based integration
API-based orchestration allows for decentralized coordination
Integrate AIOperationalize AIManage AI
AI
AI: Operationalized + Integrated + Managed
Managed
Model
Lifecycle
Managed
Infrastructure
Managed
Security
Managed
Data
Pipelines

25. July 14, 2020 25
AI AI
API
API
AIAI
AI
AI API
• Configurable (customized) solutions
• Microservices
• API-based integration
API-based orchestration allows for decentralized coordination
Integrate AIOperationalize AIManage AI
AI
AI: Operationalized + Integrated + Managed+
Monetized
Managed
Model
Lifecycle
Managed
Infrastructure
Managed
Security
Managed
Data
Pipelines
Corporate
Industrialized AIBusiness Unit Function
AI Project AI Project
Enterprise Analytics Enterprise Analytics
Corporate-wide innovation initiatives are defined
within central AI utility services and implemented/
executed by local business units and corporate
functions.
Monetized AI

26. July 14, 2020 26
AI
AI: …

27. July 14, 2020 27
AI AI
API
API
AIAI
AI
AI API
• Configurable (customized) solutions
• Microservices
• API-based integration
API-based orchestration allows for decentralized coordination
Integrate AIOperationalize AIManage AI
AI
Industrialized AI
Managed
Model
Lifecycle
Managed
Infrastructure
Managed
Security
Managed
Data
Pipelines
Corporate
Industrialized AIBusiness Unit Function
AI Project AI Project
Enterprise Analytics Enterprise Analytics
Corporate-wide innovation initiatives are defined
within central AI utility services and implemented/
executed by local business units and corporate
functions.
Monetized AI

28. July 14, 2020 28
How to implement Industrialized AI
with DataOps

29. July 14, 2020 29
Why DataOps
Source: DataKitchen Blog

30. July 14, 2020 30
DataOps Building Blocks

31. July 14, 2020 31
DataOps Transformation
Continuous Delivery of Analytics
3
STEP
2Speed to insight &
Value
High quality outputs
with effective
governance
1 4Flexibility to add
features to match the
velocity of business
needs
Automate, orchestrate a
complex environment of
people & technology
The Benefits of DataOps

32. July 14, 2020 32
3
DataOps: Core Principles & Activities
1 STEP
2
Add data and logic tests, and use
a version control system to
effectively leverage your data
holdings with a strong data
backbone and hub that inspires
confidence in data governance,
acquisition, and ethics
Branch and merge, use multiple
environments, reuse and
containerise, and parameterise
your processing to establish data
democratisation and provide an
easy-to-use experience
Work without fear or heroism with
an optimised user experience by
increasing data literacy and
bringing them on the value journey
Develop while
protecting
production
Reusing &
Branching
Maintain central
control while
encouraging
self-service
Experiment
while delivering
reproducibility
Encourage group
share while
allowing
individual control
Core Principles
Core Activities
We understand the challenges of trying to deliver innovative insights at the pace of business needs, whilst not compromising on data quality, and juggling
resources to test, deploy, and leverage new innovative technologies. We believe adherence to the following core principles will underpin addressing thse
challenges:

33. July 14, 2020 33
Delivering Analytics with DataOps
All aspects of building data analytics solutions are affected
Non-technical aspects
• Work models
• Organizational changes
• Compliance
• Security
• Skills
Technical aspects
• CI/CD automation
– Reference architectures & tools
– Version control of code and data
– Automated testing of data analytics solutions (Unit tests,
regression tests, bias tests, smoke tests, …)
– Deployment support for data analytics solutions (including
model management)
– Operations and monitoring for data analytics solutions in
production
• Managed services
DataOps work model
• Building solutions
• Agile
• CI/CD automation
• Operations support
DataOps Environment
• Sandboxes, Prod
• Automation
DataOps
Team
DataOps
Engineer

34. July 14, 2020 34
DataOps | Agile, Incremental approach
Stakeholder
commitment
Hypothesis
Get data
Write algorithmsGenerate
evidence
Decide on
hypothesis credibility
✓
Take an action
Data science
Small 4-6 week sprints
Scale globally across
the enterprise
and adapt
to fluctuating
enterprise demand
Map to standard
concepts and
make insights
repeatableUse experiments to
produce reliable
measurable results
Produce insights
that can be
distributed and
used throughout
the enterprise

35. July 14, 2020 35
Data
DataOps | Turning Data Into Value
Revenue
Efficiency
Strategy
Actions OutcomesIntelligence
Trust Thrive
Data Engineering
Trusted and
secure
Actionable insights
Automation
Embedded into
business processes
Enterprise scale
security scalability speed agilityvisibility
IoT
Distributed
Unstructured
Siloed
Transform

36. July 14, 2020 36July 14, 2020 36
Q&A
Learn more | www.dxc.technology/analytics

37. July 14, 2020
Industrialized Analytics and
ArtificiaI Intelligence
Pedro Martins | DXC Technology
pedro.martins@dxc.com