DataAquitaine February 2022

Yves Caseau - Exponential Information Systems towards Data-Driven Digital Transformation – 2022 1/26
Exponential Information Systems
to support a Data-Driven
Digital Transformation
Yves Caseau
Group CDIO, Michelin
NATF (National Academy of Technologies of France)
http://informationsystemsbiology.blogspot.com/
https://twitter.com/ycaseau DATAQUITAINE
February 10th, 2022 – v0.2

 Part 1: Digital Transformation
Driving the software revolution to adapt constantly to the customer’s environment
 Part 2: Exponential Information Systems
Software excellence matters – Build your foundations
 Part 3: Data-Driven Ambition
Enterprise-wide flows, Customer-time freshness, Future-proof unified semantics
 Part 4: Exponential Technologies Ambition
Artificial intelligence and machine learning to drive more value from data
Outline

Information Systems as Core Digital Capabilities
 Digital transformation is a
business transformation, across
the value chain
 IS as a backbone (Part 2)
 Shared “digital core” … but each
digital world has its own
ecosystems: (IT ≠ Digital)
 Digital continuity creates value
Alibaba & Amazon example:
 Digital Supply Chain Meets
Demand Management
 AI to grow new knowledge from
end-to-end processes
Digital Employee
Support Systems:
Infrastructure, Data, Identity & security, orchestration, API, ….
Product
Development
Supply
Chain
Manu-
facturing
Services
&
Solutions
Sales &
CRM
Digital continuity

4
Product Development and Knowledge Engineering
AI as a tool to capture, share and scale process and product
knowledge
Hybrid AI, from DeepMind (cf. Part 4) –
to ML-augmented finite element simulation
AI and generative ML techniques to re-invent product expertise

Industry 4.0 : Digital Manufacturing, Digital Twin & Digital Workspaces
 AI in Manufacturing to
absorb complexity
 cope with variability
 cope with manufacturing
process complexity
 Augmented humans and
augmented environments
 machine vision &
sensors for enhanced
perception
 End to End
process
optimization
Merck Example
Middleware / HA/ Containers
Shared
Datalake
Shared
Expert
Services
Infrastructucture / Security
Digital Core (App Server / CICD)
Digital Twin
Digital Twin
Digital Twin
Information
Systems
PLM
ERP
MPM

Digital Customer Journeys
 Events, Insights and Context
 Co-operation Agents/ Digital is built on a virtuous loop
 Insights are fed by events, events fed by conversations, conversations fed by content
Daily
Yearly
Life
Time
Customer
Journey
(Sales / Service ) Agent
CDP
3rd Party
Data
Process
Data
Engagement
DMP
Advice /
Recommendation
Service /
Assistance
Content
Insights
Mining
Reactive
care
Conversations
feedback
events
requests
Web/
Mobile/
Social
02.04.2021 Retain for: 90d
v. Croc / t. fraudet / a. LemblÉ / t. signarbieux
Steering committee api transformation < N° >
d3
Group Martech Stack at Michelin
Apostrophe
MediaMath
BlueConic
inRiver
Wedia
????
Salesforce
Didomi
?????
?????
WordPress
Salesforce
Marketing
Cloud
Rul.ai
Sprinklr
Pixel
MediaMath??
Excel Sheet
Qualtrics
Pixlee??
CloudImage??
Flutter
Apostrophe
Apostrophe

7
Support Hybrid Way of Working : remote collaboration
DIGITAL WORKPLACE
Augmented collaboration : “Kolmogorov compression”
The smarter the AI, the more succinct the context synchronization
Cognitive Agents: From ontologies to “GPT-3 + semantics”
knowledge management and augmentation

Part II
Exponential
Information
Systems
8

Yves Caseau - Lean Software Factories and Digital Transformation – 2021 9/17
Entreprise 3.0
“Holomorphism”
Open
Platforms
Antifragile
Customer
Homeostasis
Recognition &
Response
Network
of Teams
Massively
Transformational
Purpose (MTP)
Orientation
Client
Scalability
On-demand
Algorithms
& Automation
Continuous
Learning
Experimentation
Interfaces
Autonomy
Agility
Short steps
Communities
& Crowds
E3.0
Customer
Orientation

Exponential Information Systems Principles
 Digital Homeostasis
 Outside-in
 Reactive
 Open frontiers
 Accelerate Takt Time
 Automation
 Short release cycles
 Living System
 In/out breathing
 Continuous architecture
(emergence)
Outside-In
Customer focus
EDA
Data-Driven
Multimodal
CICD
Elastic
Resources
Algorithms
Continuous
Refresh / Refactor
Sustainable
Growth
SRE
AI4Ops
API

Architect for Change : Multimodal Architecture
 Four zones illustration:
different rates of change,
different software ecosystems
 Extension of bi-modal IT
pattern (but everything must
change)
 Edge is the software domain
that is not controlled by IT but
where it must project its
services
 The supporting integration
capabilities is a key enabler
for digital transformation
Enterprise Integration Capabilities
Business capabilities
• Records
• Transactions
• Business
Intelligence
CORE
Renewal
every 10 years
Engagement capabilities
• Mash-up
• Contextual
• Conversations
• Personalization
MATRIX
Renewal
every 5 years
Edge capabilities
• Smartphones
• Social Platforms
• Connected
Objects
• Etc.
EDGE
Fast & imposed
Renewal
Imported
Capabilities
• AI/ ML
• NLP/Semantics
• IOT
Exponential Cloud
Renewal
every 3 years
API
API
API

Lean Software Factories
 Lean & Agile: short-term delivery
of small value increments, long-
term iterative learning
 Lean thinking : continuous
management of technical debt to
develop « situation potential »
(tomorrow’s agility)
 Lean practices : right on the first
time, continuous learning through
kaizen
 Product mode : short-term
delivery of small value increments,
long-term iterative learning
Agile Manifesto
• Sprints
• Focus on user
• Cross-functional
teams
• Coevolution of
code/design
SCRUM
• Rites
• Retrospectives
Lean
• Kanban
• Kaizen
• 5S and
waste
removal
CICD
Continuous
Integration
Extreme Prog.
Continuous
Delivery
DEVOPS
Continuous Testing
Infrastructure
as Code
Dev & Ops
cross-functional
teams
Product
Lifecycle
• Test-driven
• Code is valuable

Software Craftmanship – Embedded Agility
 “Show & Share” : Develop and value software excellence
through peer reviews
 Make Code Reviews more pleasurable and more efficient:
Coding standards and Pair-programming
“Love your code” : code elegance as a support for business
agility (code that one likes to modify)
13

Part III
Data-Driven
Company
14

What we need to build this ambition:
• Digital continuity & Unified data models, to break siloes and
create enterprise-wide flows
• Data lakes & APIs, to foster open innovation and data
democratization
• AI-ready software stacks, to leverage the outside innovation
flow, and access to large and elastic computing resources.
Data -Driven Innovation
Define &
Produce Data
Store &
Forward
Data
Analytics
Data
Services
Data Products
Creating
Value
from
Data
Data strategy
Internal data
External data
Datalakes
Data Fabric
See
Understand
Predict
Service
exposure
Virtual Goods
Data API
Exchange
Platform
Empower our customers
Data
Intelligence
Adapt
Automation
Data
Collection
Architecture
Infrastructure
Create value for Michelin processes Create value for our customers
Turn data into assets that enable us to make better decisions, to deliver better operations and
to offer better solutions to customers and partners
Mindset: distributed and emergent innovation
Data collection/ training sets
AI-friendly software environments
Lab Culture (Data Science)
Perseverance
Constant
flow of
software
It takes
time to
build
skills
Data-Driven Innovation

Yves Caseau - Event-Driven Architecture for Smart Systems – 2020 16/19
Data Infrastructure
Collection Flow Infrastructure
Storage
Infrastructure
Referentials
Datalake(s)
« Hot »
Analytics
Platforms
« Cold »
Analytics
Platforms
Events
External
Sources
IOT / Video
Integration
Service
Platforms
AI & ML
Services
Classification
Forecast
NLP / Semantics
Planning
Optimization
Distributed
Data Ledgers
Sharing
Distribution
Synchronisation

Data Infrastructure Principles
 CAP Theorem : it is a new world 
 Eventual consistency
synchronized with Business Processes
 Right-time architecture (events)
High Availabilty & Scale
 Data Quality emerges from QoS &
Synchronized Process Design
 Quality of User Experience matters
 Excellence requires focus and
perseverance
 Break data siloes with federated
models and pivot objects
 Shared semantics (AI ready)
 Rosetta stone for standards and
platform strategy
Pivot Business Objects Data Quality & Processes

Event-Driven Enterprise Architecture
 Hot & Cold Interplay
 Cf. LSTM architecture
 Bio-mimicry : combine cortex with
reflexes
 Smart routing of events as
distributed system control
 Where we plug the AI toolbox
 Reactive (reflexes) and Reflective
(learning from event patterns)
 Hierarchical event model
 Events and Business Process
duality
 Outside-In thinking to design
companies as platforms
Event Model Complex Event Processing Beyond Lambda

Data Meshes
 Distributed Agility
 Scalable Event-Driven
 Think of data as evolving flows
 “Lambda architecture” is built-in 
(data lakes as nodes – temporal decoupling)
 Modularity / Federation
 “Architectural Quanta” based on “distributed
domain driven architecture”
 More an art (experience) than a science –
CAP vs transactions.
 “Data as product”
 Align people (governance) and systems
(flows)
 Change management as #1 priority
 Discoverable, self-describing, inter-operable,
trustworthy

Part IV
Artificial Intelligence
and
Machine Learning
20

Exponential Tech: Software Engineering Matters
 Data Engineering (flows) and
platforms
 Lessons from Google, Criteo,
Amadeus, etc.
 Ecosystems / rate of change /
integration
 Software engineering
because of integration and
speed of change
 Future data is better than past
 Continuous learning /
enrichment cycle /
 Speed of cycle is critical
Iterative
Developement of
AI Practice
Speed of learning
depends on
computing power
Smart
Algorithms
Smart
Engineering Smart
Services
Service
Usage
Growing
Large
Datasets
Distributed Software
Engineering Practices
Management Vision
& Grit
Ease to collect Trust &
Acceptability
21

Leveraging the Diversity of the AI Toolbox
 Todai Robot Example
 Systems of systems brings
 Resilience (biomimicry)
 Multi-scale
 Explainability
 From AlphaGo to AlphaFolds
 Large-scale Intelligent Agents
communities
 Game theory to reason about
competition and cooperation
 Reinforcement learning
 Transfer learning, GAN,
recurrent networks
 Generative Approaches,
Randomization (MCTS)
Meta-Heuristics Hybrid Machine Learning Systems of Systems

Designing Systems of (Smart) Systems
 Individual and collective
learning
 Hybrid AI required
 Time horizons (reflexes to LTP)
 Explainable / certifiable / black-box
 “Prediction is the essence of
intelligence”. Yann Le Cun
 Requires a “model of the world”
 Deep Learning for perception
 Anticipation requires system
engineering and adaptive
learning
Event-Driven Architecture
Vision
Perception
Communication
Neighbors
Robots
Information
System
Autonomous
Robot
React
Reflexes
Plan
Execute
Think
Decisions
Sensors
Goals
Individual
Memory
Forecast
Learn
adapt
Collective
Memory
Behaviors
Rules
Valuation
Patterns
Analysis
Machine
Learning
Behaviors
Rules
Valuation
Patterns
Human
worker
Cloud
hosting

TRAINING SETS
 “Data is the new code ?” (P. Haren & H. Verdier @ NATF)
… with training protocols 
 Importance of benchmarking ( Netflix, Allstate, UPS, …)
Learning curve / communities /
nested guild structure at Michelin
Training sets as a KPI for advanced data capabilities

Yves Caseau - Exponential Information Systems for Digital Transformation – 2019 25/23
AI @ Michelin : Some Examples
 Quantitative Identification
of aggressiveness of mine
roads using computer vision technology
 Stone density and size identification
 Plotting over the mine area map with heat
signatures
MVP
Productionized
 Predict the volumes of the
replacement market for the
next 5-year horizon
Productionized
Product to scan tyres using
tyre image, extract
dimensions and other details (SI, LI,
etc.) in order to recommend the right
Michelin tyre for online consumers to
increase sales
MVP
Product to use AI to recommend
tyres to e-retail customers
 Blackcircles POC: Model performance is promising, and
areas of improvement identified.
 Consumer segments generated from the model along with
the recommendations were new and useful insights
POC results
validated
Generative Adversarial
Network
 ''Application of GAN for Reducing Data
Imbalance under Limited Dataset'' accepted
for VISAPP 2022 conference authored by
Gaurav Adke
Technology
Enablers
Predict raw material prices
for next 1 year horizon
Deployed the product for 7 raw materials
(6PPD, TMQ, CBS, TBBS, ZNO, Insoluble
Sulphur, COCL2 )
Productionized

 Digital transformation is homeostasis :
Perpetual change to leverage best the possibilities of “exponential tech” in order
to match the expectations of a fast-changing world
 Data Science & Systems Engineering have never been so exciting …
Exponential revolution is happening now 
 The main risk is not to create too little value with data,
It is to leave the field of disruption to some (possibly unknown) competitors
Conclusion

DataAquitaine February 2022

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