Mainframe Day 2022 -The NRB Group - the best partner of your z-modernization.pdf

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→ INTRODUCTION
Pascal Laffineur - CEO NRB
→ INNOVATE WITH ZSYSTEMS
J. Mawet - Head of Innovation & Business Consulting NRB
B. Brandt - Information System Architect NRB
→ DEMYSTIFY REAL-TIME PROVISION OF ZDATA
P. Cheslet - Solution & Product Architect NRB
→ NRB JAVA FRAMEWORK & PL1/COBOL INTEROPERABILITY
S. Georis - Information System Architect NRB
→ CONNECTING MAINFRAME CI/CD TO THE OPEN WORLD
B. Ebner - Mainframe engineer NRB
→ NEW INTEGRATION ARCHITECTURE VALIDATED WITH OUR CUSTOMERS
→ HOW TO MODERNIZE FOR AI WITH THE IBM Z16
G. Arnould - Data & AI on IBM z Technical Sales – Client Engineering - EMEA
→ IT TRENDS & MAINFRAME
H. Gilabert - Expert of «Tendances de l’Informatique»
AGENDA

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INTRODUCTION
Pascal Laffineur - CEO NRB

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Introduction
NRB Mainframe business roadmap extending beyond 2035, fueled by:
❭ Continued investment in state-of-the-art hardware and software
❭ Continued investment in people and expertise, with in-house training at the « NRB zAcademy » and recruitment
❭ Innovation and application modernization, supported by the « NRB Software Factory »
❭ Sustained market growth for modernization of legacy Mainframe application
Business development
❭ Consolidation of NRB’s position as top Mainframe service provider in Belgium
❭ Footprint expansion in the French market, with early successes in 2021-2022, building up strong interest in NRB
Mainframe services
CSR and Green-IT
❭ Focus on energy efficiency and self-sufficiency (wind turbine, extension of the solar park)
❭ Inherent cost-efficiency of the Mainframe is key in this strategy

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INNOVATE WITH ZSYSTEMS
J. Mawet - Head of Innovation & Business Consulting NRB

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AGENDA
I n n o v a t e w i t h Z S y s t e m s
NRB Innovation Hub
introduction
Our co-creation
approach
They 6 key success
factors of innovation
Digital platform as an
innovation booster
Q&A

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The NRB innovation hub is part of NRB.
We scan today's behaviors & technologies to respond to new needs in a creative and innovative way.
We are a business booster
We help our customers move forward faster by providing optimal skills, processes and technologies.
About us
EXPLORE EXPLOIT

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O u r d e p a r t m e n t m i x e s t e c h n i c a l a n d b u s i n e s s k n o w l e d g e i n o r d e r t o c o v e r t h e e n t i r e v a l u e c h a i n
Digital Transformation and Innovation
Digital Transformation
& Innovation
William Poos
IT & Digital Strategy
Bertrand Josse
Integration Solutions
& IoT
Jean-Marc Herzet
Analytics
(Models +AI)
Leila Rebbouh
BI & Data
Management
Didier Brabant
Innovation
Business Consulting
Justine Mawet
Cloud Native
Application
Olivier Blanpain
Scrum Practice
Manon Filon
11 FTE 25 FTE 66 FTE 15 FTE
5 FTE
7 FTE
7 FTE

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T h i s o r g a n i z a t i o n i s a l s o r e i n f o r c e d b y c r o s s - f u n c t i o n a l s k i l l s t o e n s u r e m a x i m u m v a l u e
g e n e r a t i o n f o r o u r c u s t o m e r s
Digital Transformation and Innovation
Vincent Jassogne –
PreSales, EA and
Azure Architecture
Benjamin Brandt –
Innovation &
Cloud Architect
Fabian Delhaxhe –
Digital Marketer &
Squad Creator
Olivier
Lefèvre –
Mister Smart
Cities
Bruno Franki –
Innovation &
Cloud Architect
Olivier Fekenne
Architecture

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Our ecosystem

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Our mission & objectives
Our Mission
• To strengthen the innovation of the group and its customers, by mobilizing
key competencies and information, and by evolving our framework of actions
Our objectives
• To support external customers’ innovation
• Strengthen the service offer of the group's entities - reduce the Time2market
• Build a Go-2-market that enhances cross-sector synergies with subsidiaries and partner clients
• Guarantee the orchestration and/or participation of the group within the main ecosystems
• Generate new revenues through the creation of new integrated services / group digital platform – IP
• Create and support intra-group startups responsible for integrated services
• Evolve our culture (learning, collaboration, agile...)
• Attract young talent
• Be 25% profitable
explore
exploit

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Our co-creation model | TRL – TECHNOLOGY READINESS LEVEL
Observation
Concept
formulation
Experimental
evidence
Laboratory
Validation
Representative
environment
validation
Prototype
demonstration
in a
representative
environment
Demonstration
of a prototype
in an
operational
environment
Qualification of
the real system
in an
operational
environment
Real-world
system validation
in real
environment -
successful
operational
missions
1 2 3 4 5 6 7 8 9
Phases
Actors
Financing

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Observation
Concept
formulation
Experimental
evidence
Laboratory
Validation
Representative
environment
validation
Prototype
demonstration
in a
representative
environment
Demonstration
of a prototype
in an
operational
environment
Qualification of
the real system
in an
operational
environment
Real-world
system validation
in real
environment -
successful
operational
missions
1 2 3 4 5 6 7 8 9
Phases
Actors
Finan-cing
Basic and applied research
Universities/
Governments

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Observation
Concept
formulation
Experimental
evidence
Laboratory
Validation
Representative
environment
validation
Prototype
demonstration
in a
representative
environment
Demonstration
of a prototype
in an
operational
environment
Qualification of
the real system
in an
operational
environment
Real-world
system validation
in real
environment -
successful
operational
missions
1 2 3 4 5 6 7 8 9
Phases
Actors
Finan-cing
Advanced research and technology
demonstration
Universities/
Governments
Private/Public Partnerships
Co Creation
Co Financing
NRB/Customers

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Observation
Concept
formulation
Experimental
evidence
Laboratory
Validation
Representative
environment
validation
Prototype
demonstration
in a
representative
environment
Demonstration
of a prototype
in an
operational
environment
Qualification of
the real system
in an
operational
environment
Real-world
system validation
in real
environment -
successful
operational
missions
1 2 3 4 5 6 7 8 9
Phases
Actors
Finan-cing
Advanced research and technology
demonstration
Qualification and technological
operationality
Universities/
Governments
Private/Public Partnerships Private
Co Creation
Co Financing
NRB/Customers

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The 6 key success factors of innovation

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1| Business Strategy
The real innovation is not
technological, it aims at resolving
“real” business problems.

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▪ Sectorial vision – understanding challenges
▪ Understanding Customer Strategy - Think Tank
▪ Definition of business strategy and OKRs
▪ Business model transformation
▪ Value Proposition Design/Testing
▪ Customer segment
▪ Partnership and operating model
▪ Functional optimizations
▪ Revenues/costs
▪ Focus on real problems: Design Thinking
▪ Customer journey optimization
▪ Co-Creation on TRL < 7
▪ Study of ecosystems (participation, orchestration) – detection of
good partners
Business
Strategy

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2| Target Operating Model
We co-elevate and cross the
finish line all together.

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▪ Perimeter - When what needs to be done or how it is done is vague
and unpredictable
▪ Identify multidisciplinary teams of 7 to 10 people supporting the
strategy by assigning them OKRs
▪ Manage dependencies between teams via scrum of scrum
▪ Turning middle managers into entrepreneurs
▪ Transform « direct and control » culture to « empower and support »
▪ Deploy iteratively within the organization
▪ Building a Transformation Team to Address Irritants - CoE
▪ Review HR process - training, assessment and recruitment processes
Business
Strategy
TOM

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3| Information Management
Information to fuel innovation

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▪ Vision: Create a centralized data environment and analytics
capability in a secure and documented manner that provides data
consumers with reliable insights and insights
▪ Priorities: one version of reality, governed trusted data, adapted
visualization tools
▪ Projects : Data catalog, Data Lake et company wide DWH, Data
Masking / GDPR, Visualisation
▪ Transformation: Using cloud levers to
• Integrate new data sources –internal and external
• Accelerate consolidation load times
• Accelerate decision-making
• Supporter les uses case « near real time »
• Faster/Bigger/Cheaper
Business
Strategy
TOM
Information

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4| Technology
Multi Cloud Digital Platform
zSystem at the heart of the
digital platform

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▪ Incremental, agile construction
▪ Growth at the rate of value generated
▪ Multitech service set – ux, twins, microservices, AI, IOT, ...
• UX - language, text, image, ...
• Ecosystem and Open API
• Third Party Development Platform
• Microservices - Kubernetes, azure functions, lambda
• DATA/AI – Data Lake, Machine Learning
• Legacy Systems Integration
▪ Reusable component library
• Infrastructure component (security, identity & access management, event processing, … )
• Reusable business capabilities/component (billing, twins, …)
• Specific components specific to a particular offer
• Cross-sector digital offer
Technology
Business
Strategy
TOM
Information

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5| Culture and communication
Everyone is an innovator

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▪ Inclusive approach - gamification
‒ Bronze: participation in a scrum training, ideation, ...
‒ Silver: participation in a sprint of definition of value proposal / realization of a POC
‒ Gold: achievement of a successful MVP
▪ Showcase your innovation - services, skills, method, sector issues,
implementation
▪ Sparking the idea – Id8or by providing collective intelligence tool
▪ Innovation functions / career Management: innovation manager,
product owner, agile coach, …
▪ Setting up creative spaces
▪ Organize innovation sprints with partners and internal challenges
Culture
Communication
Technology
Business
Strategy
TOM
Information

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6| Compliance
Compliant by design

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▪ Early integration of all company functions
‒ DPO , Communication, Legal, Marketing, Risk Officer
▪ Definition of Explore / Exploit criteria – Insure smooth transition
‒ Accountability
‒ SLA
‒ Environments
‒ Architecture
Culture
Communication
Technology
Business
Strategy
TOM
Information
Compliance
SUCCESS

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Digital platform as a booster to create a
real added value by capitalizing on the core
business

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Mainframe Day
2016
A (short) look to the past

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Z Systems
in the Digital Platform
Stability in an agile world

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ZSystems in the Digital Platform
▪ Stable
▪ Robust
▪ Reliable
▪ Predictable
▪ Fast
▪ Agile
▪ Connected
▪ Enjoyable
Core System on Z Digital Platform

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ZSystems in the Digital Platform
Core is accessible and opened
Core leverage other technologies to innovate
Core System on Z Digital Platform

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Digital Platform
- What’s in it ?
An ecosystem of technologies

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Digital Platform components
▪ Mobile Applications
‒ Native (IoS / Android)
‒ Flutter
▪ Web sites / applications
‒ Angular
‒ React
▪ End-user devices
‒ Assistants
‒ Virtual Reality headsets
▪ Data Lake
‒ Store
‒ Purpose-built DB
‒ SQL DBs
‒ Object Storage
‒ GraphDB
‒ …
▪ Analytics
‒ Exploit & understand
‒ AI /ML
‒ BI
‒ Streaming Analytics
‒ …

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▪ Microservices Apps
‒ Serverless
‒ AWS Lambda
‒ Azure Functions
‒ Containers
‒ Kubernetes
‒ Java Spring Boot
‒ .net core
‒ NodeJS
‒ Python
▪ Core systems
‒ Packaged
‒ SAP, GuideWrire, …
‒ ESB
‒ SAG, Mule, Talend, …
‒ Mainframe
‒ Z systems

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▪ Internet of Things (IoT)
‒ For Home
‒ Lights, Smoke detector, camera,…
‒ For Business
‒ Self Driving truck, harvester, …
▪ Ecosystems
‒ Expose services
‒ Consume external services
‒ Monetization
‒ Co-creation
‒ Open Data
‒ Networking connectivity
‒ Rules engine
‒ Fleet management

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▪ Identity Provider
‒ Essential building block
‒ Build applications faster
‒ Standard protocol
‒ Open Id Connect (OIDC)
‒ Connect with social medias (Facebook, Google, Tweeter, …)
▪ Transversal Services
‒ Monitoring
‒ Alerting
‒ CI/CD
‒ Compliance

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Digital Platform
- Where to build it ?
All things distributed

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Digital Platform is distributed.
It span across your on-premises, public cloud and
partner’s systems.
Choose the right tool for the job.

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Digital Platform – why the cloud
Powerful
Security
Reliability
Tools
World-wide footprint
Scalability
Pay only for what you
use.
Adapt resources
automatically, If need be.
Enjoy the power of image
recognition, user
management, centralized log
systems and many others.
Use those tools to leverage
your business.
Thanks to isolated and
replicated solutions all over
the world, you can build high
quality disaster recovery
systems.
With the global footprint,
you deploy applications
closest to your customers.
To keep their digital journey
at its best shape
The security OF the cloud is
guaranteed by the providers.
Plus, they provide you with
up-to-date tools so you can
guarantee the security IN the
cloud to your customers
Use the amount of
resources you need for
your business.
Use the right resource for
the right job.

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Digital Platform – Azure Services

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Digital Platform – AWS Services

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Build your blocks
Combine services
& reduce time-to-market

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Digital Transformation &
Innovation
Justine.Mawet@nrb.be
Head of Innovation & Business Consulting
Benjamin.Brandt@nrb.be
Information System Architect
William.Poos@nrb.be
Head of Digital Transformation & Innovation

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DEMYSTIFY REAL-TIME
PROVISION OF ZDATA
P. Cheslet - Solution & Product Architect NRB

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▪Access from Distributed / Cloud Apps to Core Data on z/OS
Hybrid Data Integration Need
On-Premises
Dedicated Local
Mainframe
Traditional IT
Private
Hybrid Cloud
Off-Premises
Multi-Cloud
Public
NECS 4

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Hybrid Data Integration Patterns
Access from Distributed / Cloud Apps to Core Data on z/OS
READ-ONLY
DB Duplication &
Transformation
including changes
Hybrid Integration
Data Lake
Near real time
copy of data
Near Real Time
Second to minutes old
depending on change
apply
PUSH
Data change
Batch copy of
data
READ-ONLY
DB Duplication &
Transformation
Static data
Data Warehouse
Data Lake
One day or
more old
PUSH
Data
Near Real Time
Second to minutes old
depending on event
subscription
PUSH
Apps event
Event-based
Architecture
for
Integration
Pub/sub of events
Data
synchronization
thru Apps Events
Real Time
PULL
Data
Synchronous
Integration with
zApps
No data duplication
API Inbound
Real Time
PULL
Data
Hybrid DB
access using
SQL
No data duplication
Data
virtualization
& federation
Data
Latency
Use Case
& Techno

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▪ Pull versus Push
For real time data access and updates, data is pulled from IMS Applications
For IMS DB or Db2
For access without « real time » need, event publication thru « PUSH » mechanism
Replication of IMS Databases to relational with IBM Change Data Capture (CDC)
Creation « Application Event » for Pub/Sub Kafka solution
Access to aggregated data with datawarehouse technologies
NRB – zData Access Best Practices
Method Data Access Level Access Type Data validity Technology
PULL Real Time
Read
IMS Transactionality for IMS DB, Db2, MQ
ressources
API on top of new IMS PLI/Java Data Service
transaction
Write
IMS Transactionality for IMS DB, Db2, MQ
ressources
API on top of Business Service (New framework)
or existing applications
PUSH
Near Real Time Read Thru apply of updates
Data Event with IBM CDC
Use case: IMS DB to SQL based format
Near Real Time Read Thru subscription of event & apply updates Application Event with IBM MQ & Kafka
5 Minutes Read Thru apply of updates and transformation
Data Event with IBM CDC & post processor to
build « enterprise canonical view » in ODS
Previous Day Read Static aggregated data Data Warehouse

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▪PULL – zApps & API
Access to 100% of z/OS data based on
business need
On demand creation of API with new IMS Apps
to give access to IMS data without reusing
existing business logic
▪PUSH "Data Event” – with IBM CDC
Replication in Db2 on z/OS, or Oracle on
distributed
Access limited to the Dbs managed by CDC
▪PUSH "Application Event" - with
Confluent Kafka
Event publication when some IMS DB are
updated
MQ message integrated in two phase commit
Gateway between MQ & Kafka
NRB – zData Access Best Practices …
Distributed Apps
Including
home made apps,
SalesForce, Guidewire, …
CDC /
Apply
Raw Data
(Oracle)
POST-
Process
Conformed
Data (Oracle)
Kafka Subscription
SQL Queries
Kafka
IMS
IMS DB
Db2
subset
CDC /
Capture
Db2 Raw
Data
CDC /
Apply
Db2 SP
DLI SQL
MQI SQL
Service
API

57
New “Query Services” in IMS PLI ou JAVA
▪ Read-Only Access to “Real
time” IMS data NOT replicated
with CDC
▪ Components
New “data services” to answer
quickly to customer data need
API Creation oriented “Query IMS”
for a specific business need
IMS Transaction creation reusing
existing data access components
Read-Only access to traditional IMS
databases with DLI Calls
▪ Remark: Update access are
still done with legacy IMS
apps.
IMS
Data
Service
(New)

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▪Read-Only Access to “Near real
time” IMS data replicated in Db2
z/OS without leaving z
▪Components
IBM CDC
Db2 Native SP
SQL only
zIIP support – low cost ;)
API managed by z/OS Connect to call Db2
SP
New API & Db2 Native Stored Proc

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Teasing for NRB Mainframe Day 2023
▪In 2022 - Hybrid Data Integration Need:
Access from Distributed / Cloud Apps to Core Data on z/OS
▪In 2023 - Hybrid Data Integration Need:
Access from Core IT Apps on z/OS to Distributed / Cloud Data
On-Premises
Dedicated Local
Mainframe
Traditional IT
Private
Hybrid Cloud
Off-Premises
Multi-Cloud
Public

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NRB JAVA FRAMEWORK &
PL1/COBOL INTEROPERABILITY

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Agenda
1. NRB’s Application Architecture Evolution
2. NRB’s development framework overview
3. NRB’s development Java framework with interoperability

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‘Historical’ Model
To-be
Domain 1
As-Is
Domain 2
Domain 2
Domain Driven Design Model
Transformation
From historical to Domain Driven Design service-oriented architecture
zApplications NRB’s Application Architecture Evolution

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zApplications NRB’s Application Architecture Evolution

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Agenda

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zApplications NRB’s development framework overview
NRB’s has build a framework the ease, standardize,
accelerate the development of applications with a high level
of reusability and avoid code duplication.
Based on a common services models supporting all the
service’s types of the zApps’ Evolved Architecture :
IMS transactions, CICS programs, Business Services, Business
Objects Services, Business Rules Services, Data Access
Services and Utility Services.
Abstract layer & services for all the aspect such as :
▪ Applicative context initialisation
▪ Services and operation metadatas
▪ Data Communication : IMS, CICS, MQ, Java Native
Interface (interoperability)
▪ ODM ruleset execution
▪ Error handling
▪ Application audit & monitoring
▪ …
The framework is available for Cobol, PL/1 and Java

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Agenda

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zApplications NRB’s development framework with Java interoperability

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Performances test conditions
▪ Invoking mirrored applications written in PL/1 and in Java
▪ Running scenarii simulating realistic business behaviours
▪ Gradual increase of the number of users and number of
API calls
▪ Running in a development environment with limited
capacity
▪ z/OS Platform up to date (z15 / z/OS 2.3 / uncapped zIIP
processors) 0
50
100
150
200
250
300
350
400
z/OS Connect average
response time (ms)
IMS average response
time (ms)
Total CPU time (sec) % CP Processor usage % zIIP Processor usage
Frameworks performance tests
PL/1 Framework Java Framework
Framework # API Calls z/OS Connect
average
response time
IMS average
response time
Total CPU time % CP Processor usage % zIIP Processor usage
PL/1 5444 336 ms 196 ms 73,06 secs 100 0
Java 5443 253 ms 65 ms 84,64 secs 18 82
Java framework performances test

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Need or Concern Answer
Enable the development of Java applications on the IBM z platform Java framework under IMS or CICS
Support to be-architecture patterns & architectural concepts Alignment on the architectural patterns & concepts
Enable interoperability between different languages Interoperability using Java Native Interface
Benefit from existing transactionality and security on the platform ▪ Transactionality : Java runs under the authority of IMS or
CICS
▪ Security : via RACF orTop Secret
Reduction of run costs through the usage of zIIP type processors 82 % of workload is zIIP processors eligible
Increase developments speed and reduce time to market Some aspects should no longer be managed by developers and
they can only focus on the business code to be developed +
DEVOPS.
Adequate performances z15 hardware
z/OS 2.3
Java Framework performance is 3X faster than PL/1
Java framework : Answers to needs & concerns

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CONNECTING MAINFRAME
CI/CD TO THE OPEN WORLD
B. Ebner - Mainframe engineer NRB

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Agenda
1. How to integrate Java deployment on mainframe?
2. How to control and pilot production deployment?
3. How to implement a quality gate?

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How to integrate Java deployment
on mainframe?

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C o n s t r a i n s a n d g o a l s
Context
H o w t o i n t e g r a t e J a v a d e p l o y m e n t o n m a i n f r a m e ?
• We develop a new framework: PL1 – Java
• We want to keep the Java source workflow in the “normal Java way” (Git, Jenkins, …)
• The deployment on the mainframe need to be seamless for Java developers
• We want to use the same CI/CD pipeline for other mainframe related objects (zOS Connect, ODM, …)
• We want to benefit of the NRB private cloud (NECS)

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1 T h e J a v a d e v u s e t h e i r p r e f e r r e d e d i t o r a n d s t o r e t h e c o d e i n a G i t l a b i n s t a n c e
Flow overview
NECS
IntelliJ
Netbeans
VSCode
Gitlab

76
2 T h e J A R g e n e r a t e d a r e s t o r e d i n N e x u s
Flow overview
NECS
IntelliJ
Netbeans
VSCode
Gitlab Nexus

77
3 E v e r y t h i n g i s p i l o t e d b y J e n k i n s
Flow overview
NECS
IntelliJ
Netbeans
VSCode
Gitlab Nexus
Jenkins

78
4 J e n k i n s i n i t i a t e t h e d e p l o y o n t h e m a i n f r a m e l p a r
Flow overview
NECS
IntelliJ
Netbeans
VSCode
Gitlab Nexus
Jenkins
Mainframe
ISPW
rest API
(ISPW Jenkins plugin)

79
J e n k i n s f i l e : L a u n c h a d e p l o y m e n t o f a n a s s i g n m e n t
Flow overview

80
How to control and pilot production
deployment?

81
C o n s t r a i n s a n d g o a l s
Context
H o w t o c o n t r o l a n d p i l o t p r o d u c t i o n d e p l o y m e n t ?
• We want to have a full control on MEP
• Check if all the element is “promotable”
• If we have a problem to promote one element of a change, return to previous state for this change
• If something failed, warn the duty
• Communicate the result of MEP to all the people involved
• Release management
• System team
• Developer team
• Operator team
• The trigger for the promote is Control/M

82
P y t h o n
What tool do we use
• Works seamlessly on mainframe
• More capabilities than REXX
• RESTapi call
• All Python plugins work on mainframe
• Debug online
• Call Python with shell script in BPXBATCH
Z O A U ( I B M Z O p e n A u t o m a t i o n U t i l i t i e s )
• Add MVS function on USS Shell command line, Python and Java.
• Execute MVS command (normal or authorized)
• Dataset manipulation
• JES utilities (submit, cancel, list…)
• Console, operator utility
• …
• Use here to call IBM System Automation and send WTO.
P y t h o n + Z O A U c a n r e p l a c e a p a r t o f y o u r J C L , R E X X a n d e x t e n d m a i n f r a m e c a p a b i l i t y

83
1 C o n t r o l - M s u b m i t a B P X B A T C H t o t r i g g e r t h e M E P p r o c e s s
Flow overview
CTRL/M Python
BPXBATCH

84
2 P y t h o n w o r k w i t h I S P W / R e s t A P I t o d o t h e M E P
Flow overview
CTRL/M Python
ISPW
BPXBATCH
rest API

85
E x a m p l e : G e t a l l t h e s o u r c e s r e a d y t o p r o m o t e t o p r o d u c t i o n
Flow overview

86
3 P y t h o n u s e Z O A U t o w o r k w i t h I S A t o s e n d t h e s t a t u s t o t h e o p e r a t o r
Flow overview
CTRL/M Python
ISPW
BPXBATCH
rest API
ISA
ZOAU

87
S a m p l e o f a I B M S y s t e m A u t o m a t i o n c a l l
Flow overview

88
4 A l l s t a t u s e m a i l i s s e n d
Flow overview
CTRL/M Python
ISPW
BPXBATCH
rest API
ISA
ZOAU
Email
email
report

89
Flow overview
E m a i l s a m p l e : p r o m o t i o n a n a l y s i s

90
How to implement a quality gate?

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W h a t w e t r y t o i m p l e m e n t ?
Context
H o w t o i m p l e m e n t a q u a l i t y g a t e ?
• Before push a source to acceptance level we want to be sure:
• The quality control is done
• The change is linked to a correct demand
• Maximize automatization of the process
• If no problem detected ➔ don’t block the promotion
• Otherwise: use Teams to warn the Quality Control Team

92
1 c a l l P o w e r A u t o m a t e w i t h a W e b h o o k
Flow overview
Mainframe
ISPW
Microsoft Power
Automate
Office 365 cloud
Webhook

93
W e b h o o k p a n e l
Flow overview

94
2 c h e c k i f t h e e l e m e n t i s a l r e a d y v a l i d a t e d
Flow overview
SharePoint
Microsoft Power
Automate
Mainframe
ISPW
Office 365 cloud
Webhook

95
3 c h e c k i f t h e c h a n g e i s c o v e r e d b y a v a l i d t i c k e t
Flow overview
ITSM
Jira
SharePoint
Microsoft Power
Automate
Mainframe
ISPW
Office 365 cloud
Webhook

96
4 I f s o m e t h i n g m i s s i n g , p u s h a m e s s a g e i n a T e a m s g r o u p
Flow overview
ITSM
Jira
SharePoint
Microsoft Power
Automate
Teams
Mainframe
ISPW
Office 365 cloud
Webhook

97
S a m p l e o f a T e a m s i n t e r r a c t i o n
Flow overview

98
5 u s e I S P W r e s t A P I t o r e l e a s e o r c a n c e l t h e p r o m o t i o n
Flow overview
ITSM
Jira
SharePoint
Microsoft Power
Automate
Teams
Mainframe
ISPW
Office 365 cloud
Webhook
rest API

99
Modern mainframe development can be
connected to open environments.
You have all in your hand to unlock it!

100
NEW INTEGRATION
ARCHITECTURE VALIDATED
WITH OUR CUSTOMERS

101
z/OS Connect EE
The Mainframe intergration’s
corner stone

102
Truly RESTful APIs to
and from your
mainframe
DevOps using z/OS
Connect EE
IMS
CICS
DB2
MQ
…
PL/1 zAPP
Cobol zAPP
Basic of z/OS Connect EE

103
API Provider
➢ zAssets expositions: IMS transaction, CICS
programs, MQ, Db2Services, …
➢ Exposition of real REST resources aligned with
the enterprise data model
➢ Authorization using JWT token
API Requester
➢ PL1 or Cobol applications calling external API
from the digital platform, partners or government
➢ Secured connection using JWT token for
authorisation
Common
➢ Exploitation of SMF records 123 v2 for auditing & monitoring
➢ Usage of Omegamon for JVM for system monitoring
➢ Secured using z/OS Address Space protection (RACF, TSS) , certificates, IP Stack and NetAccess TCP/IP
TLS Secured Connection, Usage of Policy Agent, …
z/OS Connect EE usage @NRB

104
Z in the
Digital Platform
The Mainframe intergration’s
corner stone

105
• Client Layer
➢ for customers, partners, employees, …
• Integration Layer
➢ Public & Private Cloud
➢ System API Gateway
• A single gateway for all z/OS Assets
➢ Inbound & Outbound
➢ Secured
Integration End-to-End View

106
API Layers
of the Digital Platform
Connect the world

108
API Layers
▪ Channel Layer
‒ Specific to a consumer
▪ Experience Layer
‒ Specific to a product
▪ Capability Layer
‒ Generic APIs
▪ System Layer
‒ Contains Business logic

109
Integration is key
Sebastien.Georis@nrb.be
Mainframe Modernization
Benjamin.Brandt@nrb.be
Digital Transformation & Innovation

110
HOW TO MODERNIZE FOR AI
WITH THE IBM Z16
G. Arnould - Data & AI on IBM z Technical Sales
Client Engineering - EMEA

Data and AI on IBM z
How to modernize for AI with
the IBM z16
Guillaume Arnould
Data & AI on IBM Z - Expert IT Specialist
IBM Client Engineering for Systems | EMEA
November 22nd, 2022 | NRB Mainframe Days

La Banque Postale | Juin 2002
Agenda
How to modernize for AI with the IBM z16 ?
❑ AI powered by IBM z16
❑ Exploiting Integrated Accelerator for AI software
stack
Questions
112

https://newsroom.ibm.com/2021-08-23-IBM-Unveils-On-Chip-Accelerated-
Artificial-Intelligence-Processor
IBM Z AI & Analytics / IBM / © 2022 IBM Corporation
114

IBM z16 Value statement
Real-time Business insights at
Scale
Enhanced Data Security &
Resiliency
Hybrid Cloud with Intelligent
Infrastructure
Enable clients to infuse AI into every
business transaction by seamlessly
leveraging the IBM z16 hardware AI
engine to accelerate inference on Z
Leverage data and transactional gravity
on Z to drive real-time AI infused insights
in business-critical workloads, while
meeting even the most stringent SLAs
High throughput, low latency AI, in-
transaction decision making before the
opportunity has passed.
Enhanced cost savings by prevent fraud
and mitigate risk with greater accuracy by
leveraging deep learning.
Safely use personal, sensitive data
for analytics and AI in-place within
the security-rich IBM Z perimeter –
with 100% encryption of all data
Apply transactional system-level
performance and availability to your
analytics and AI workloads to deliver
actionable, real-time insights
Train anywhere and inference on Z
capability enables customers to bring their
existing AI investments along
Flexibility for practitioners to leverage the
tools they are accustomed to, and deploy
on Z when beneficial
Data federation capabilities through
virtualization
Improve Security, Data Privacy, IT
Operations with AI
Deploy advanced, explainable AI across the
ITOps toolchain
116

IBM Z: Fully enabled platform for business intelligence
Build and train anywhere
Deploy on IBM Z
Deploy on IBM Z and seamlessly exploit
innovations across the stack to infuse AI
in every single transaction.
Train anywhere
Public clouds, private clouds, on-premises,
and hyperconverged systems.
Organize Data
Import data from different
applications and sources
Import Data
Model & Data
Prep
Model Training Deploy Predict
Business Applications
117

Exploiting
Integrated
Accelerator for AI
software stack
118

A comprehensive technology stack designed for AI
119
CPU
SIMD
Hardware &
Facilities
Operating Systems,
Container Env.
Math Libraries,
Compilers,
Optimizations
IBM DLC
AI Frameworks and
Runtime
Optimizations
IBM SnapML
IBM Solutions for
Data and AI
Watson Machine
Learning for z/OS
Db2 AI for z/OS
z/CX
z/OS
• Data and AI platform
modernizes your data estate
• ONNX/DLC enables choice
with multiple frameworks
• Deep Learning for granular
and low latency insight
• Support for inference
containers
• Optimizations of AI inference
and pipeline execution
AIOps
IZOA Z APM Connect
zAIU
DB2 v13
SQL Data
Insights

120
IBM Z
IBM Z Integrated Accelerated for AI
zDNN Library
Tensor
Flow
Snap ML Deep Learning
Compiler (ONNX)
zADE library
Watson Machine Learning
for z/OS
SQL Data
Insights
Cloud Pak for Data
Db2 for
z/OS
IBM Z
Anomaly
Analytics
Watson AIOps
Db2 AI
for z/OS
Exploiter of IBM Z Integrated
Accelerated for AI 120
How offerings leverage the IBM Z Integrated Accelerated for AI
IBM developed accelerator library.
Building block used by compiler and framework
developers – not generally by clients.
Open source and/or community freely available
software. Except for zADE, these are often used
directly by end users and other open-source sw.
Full enterprise AI lifecycle solutions. Leverages
the below building blocks but bring huge
additional value.
Products that embed AI solutions to provide
insights used in the offering.

TensorFlow on IBM z16
• Popular AI open source framework with a
broad ecosystem.
• Widespread industry adoption.
• Highly popular
• Develop, train and inference of deep neural
networks.
• Available on today’s IBM zSystems!
• IBM is enhancing TensorFlow to exploit the z16
Integrated Accelerator for AI
• Will feature transparent acceleration with
no model changes.
• Planned to be available initially through
open beta late 2Q 2022.
121
✓ Available for Linux environments
✓ z/OS Container Extensions (zCX) helps
integrate Linux on Z applications with z/OS
✓ Run TensorFlow Docker images directly on
zCX in proximity to z/OS workloads
✓ Available via IBM Container repository for
trusted images.
✓ Manage model serving instances on IBM Z
using popular AI frameworks

122
122
The IBM DLC
(Deep Learning Compiler),
optimized for performance
and new libraries, generates
a program from the model for
execution on z/OS or Linux®
on IBM z16
Use ONNX, an opensource tool for
framework interoperability
Models are converted
to the ONNX interchange format
Leverage zCX and run on
zIIP engines
Build and train model
in any popular
framework on any
platform of
your choice
IBM Deep
Learning
Compiler
Generated
inference program
ONNX interchange
format
Deploy on
IBM z16 and
IBM LinuxONE
• Bring machine learning & deep learning models to IBM z16 with ONNX/DLC
• Exploit IBM Integrated Accelerator for AI for best inference performance.
• Repeatable practice for different vendors to leverage IBM z16 and Integrated Accelerator for AI
Deploy on IBM z16
and IBM LinuxONE
and infuse model into
workload application
Build and train anywhere – Deploy on IBM Z

Deep Learning Compiler for
Linux on Z environments
• Stand-alone compiler docker image.
• To be listed as ONNX-MLIR, the open-
source partnership the DLC builds on.
• Targeted at open-source or do-it-yourself
pairings.
• No packaged serving environment.
• Pair easily with BentoML, FastAPI, etc.!
• Exploit the z16 Integrated Accelerator for AI.
• Supports C++, Java, Python APIs.
• Code examples to be made available.
• Will be available on the IBM Z and LinuxONE
Container Registry.
• Target for GA is May 31st.
123

Watson Machine Learning for
z/OS Online Scoring
Community Edition
• Community edition (free) scoring service for
ONNX models, featuring IBM Deep Learning
Compiler.
• Rapid PoC capability – setup and deploy in 15
minutes!
• Deploy models to z/OS Container Extensions.
• Exploit the z16 Integrated Accelerator for AI.
• Updates for z16 generally available on May 31st
• Available under “trial code” here:
https://ibm.biz/WMLzOSCE
124
z/OS System
z/ OS Container Extensions
WMLz Online
ScoringService
DLC Compiled
Model
Core Services (Model and
Deployment Management)
WMLz
Base Core Services
Deploy &
Manage
Inference
REST
endpoint

Watson Machine Learning for
z/OS 2.4
CICS COBOL and WMLz online scoring using ALNSCORE
• Native deployment on ONNX models on z/OS.
• zIIP eligible for inference.
• Simplified AI integration for CICS® COBOL
applications.
• CICS COBOL applications can invoke ONNX
models using standard CICS commands.
• Provides features for optimal exploitation of
IBM z16 Integrated Accelerator for AI.
• Embeds IBM Deep Learning Compiler
• Server-side micro-batching.
• Numerous other models supported; provides
model lifecycle management.
• V2.4 generally available on May 31st
COBOL
Application
z/OS System
CICS Region
COBOL
Application
Liberty JVM Server
Program
ALNSCORE
WMLz Online
Scoring
Service
DLC Inference
Program
Core Services (Model
and Deployment
Management)
Watson Machine Learning
for z/OS
Deploy
• PUT CONTAINER(…)
CHANNEL(CHAN1)
FROM (…)
• LINK PROGRAM(ALNSCORE)
CHANNEL(CHAN1)
• GET CONTAINER(…)
CHANNEL(CHAN1)
• FROM (…)
125

126
Choose your own deployment path for Deep Learning
models…
When to choose TensorFlow on Z
• Direct support of TensorFlow
assets (models, pipelines).
• Desire a consistent TensorFlow
ecosystem experience.
• Configure serving infrastructure to
scale.
• REST API overhead is acceptable.
When to choose ONNX and IBM DLC
• Optimized inference for many
model types (e.g., PyTorch).
• Enterprise scalability and support.
• Embed inference tightly in-
transaction.
• Minimal application changes for
native z/OS applications.
Foundational Technologies
● SIMD Architecture ● Optimized Libraries ● Built on IBM Z
IBM Deep Learning
Compiler

128
AI on IBM Z Use cases

Announced April 5, 2022
129
Available May 31, 2022
IBM z16
IBM Db2 13 for z/OS
+
Better together!
TM

AI agility:
Business insights without data
science skills
• Power any Db2 for IBM z16/OS
application with AI enhanced SQL
• Uncover and monetize hidden
insights within your data
• Identify similarities, dissimilarities
and correlations
• Apply a single model across
multiple questions
• Minimize AI deployment
complexity
• No data science skills needed
Trillions of transactions per
day go through
IBM z16 and that data is
stored in our Db2 for IBM
z16/OS engine.
Assess whether a customer
will churn.
Clients can use built-in AI models
to understand underlying
semantics of the data
Learn patterns in
that data to identify
fraud before the
transaction closes.
Mine data to
determine whether
to extend a loan to
a customer.
“Out-of-the box” AI can
be exploited through Db2
for IBM z16/OS
IBM z16 supports the most popular machine learning algorithms, providing
our clients an AI cloak to help them improve processes and drive greater
business value from the existing investment they have made.
The IBM z16 platform empowers clients to
mine their most valuable enterprise data
130

131
131
SQL Data Insights - Enabling Self-service AI
Additional Value:
• Provides interpretability
• Exploits AIU acceleration
• Operations on encrypted data
– Provides hidden relationships and inferred meaning from data in your database
– Reduces need for deep data science skills
SELECT X.accountID, X.FirstName, X.LastName,
X.openedDate, X.RewardPoints,
ai_semanticCluster(X.accountID, ‘1234ABCD’,
‘4567EFGH’,’6789IJKL’) AS RiskScore
FROM Data_Table X
WHERE ai_semanticCluster(X.accountID, ‘1234ADCB’
4567EFGH’,’6789IJKL’) > 0.0
ORDER BY RiskScore DESC

135
Semantic SQL Functions
First set of AI Built-In Functions available in Db2 13
Cognitive
Intelligence
Query
Functional
Classification
Functional Description Db2 functions
semantic similarity
and dissimilarities
Entity Matching
Recommendation
• Matching rows/entities based on overall meaning
(similarity/dissimilarity)
• Suggest choices for incorrect or missing entities
AI_SIMILARITY
semantic
Clustering
Recommendation • Find entities/rows based on relationships between
attributes in a given set
• Example: Find animals similar to (lion, tiger, panther)
AI_SEMANTIC_CLUSTER
Reasoning Analogy Recommendation • Find entities/rows based on relationships between
attributes
• Example: Moon : Satellite :: Earth; ?
AI_ANALOGY

AI on IBM z16 :
Designed for business insights and intelligent infrastructure
136
Enable a leading
AI portfolio &
ecosystem Watson Machine Learning
for z/OS
IBM Cloud Pak for Data
Deploy advanced,
explainable AI across
the ITOps toolchain
Enhance database
performance with
machine learning
Data Privacy for
Diagnostics
Leverage machine
learning to detect
and redact PII from
diagnostic dumps
REAL TIME BUSINESS INSIGHTS
Infuse AI in Real-time into Every
Business Transaction
INTELLIGENT INFRASTRUCTURE
Improve Security, Data Privacy,
IT Operations with AI
Watson AIOps &
IBM Z Anomaly Analytics
Db2 AI
for z/OS
Watson® Machine
Learning for z/OS
Unprecedented AI
inferencing performance for
every transaction while
meeting SLAs
Db2 for z/OS® with
SQL Data Insights
Uncover hidden
insights in
Db2 for z/OS data
Db2 Analytics Accelerator
for z/OS
Db2®
Db2 Analytics
Accelerator for z/OS
Real-time insight
from data at the
point of origin

IBM Z – An industry leader in optimized inferencing
Business Analytics use cases
- Provide consulting and services to Line of Business
Db2 z/OS SQL Data Insight
- Provides hidden relationships and inferred meaning
from data in Db2 or other IBM Z data via DVM
- Reduces need for deep data science skills
- Minimizes complexity of infrastructure and tooling to
deploy AI for your applications
ML Performance
- Library enhancements for ML performance
- Optimization of AI inference and pipeline execution
Software enablement for DL acceleration
- zDNN is an AIU-accelerated library of primitives for
deep neural networks.
- ONNX/DLC enables multiple DL frameworks
- TensorFlow enablement delivers acceleration in an
industry-standard serving environment
On-Chip engine for Deep Learning
- Industry-first low latency in-transaction inferencing
137
IBM Cloud Pak
for Data
IBM Open Data Analytics for z/OS
Optimized Data Layer
Z Core (CPU)
Watson Machine
Learning for z/OS
Libraries-Eigen,
Open BLAS, etc.
Z AI Unit (AIU)
Neural Network
Library - zDNN
Db2 13 for z/OS
SQL Data Insight
Business Analytics use cases
& IBM
Deep Learning
Compiler

138
AI on IBM Z Resources
Soon updated!

Thank YOU
139

Trademarks
© 2022 IBM Corporation 140
Notes:
Performance is in Internal Throughput Rate (ITR) ratio based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput that any user will experience will vary depending
upon considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will
achieve throughput improvements equivalent to the performance ratios stated here.
IBM hardware products are manufactured from new parts, or new and serviceable used parts. Regardless, our warranty terms apply.
All client examples cited or described in this presentation are presented as illustrations of the manner in which some clients have used IBM products and the results they may have achieved. Actual environmental costs and
performance characteristics will vary depending on individual client configurations and conditions.
This publication was produced in the United States. IBM may not offer the products, services or features discussed in this document in other countries, and the information may be subject to change without notice. Consult your local
IBM business contact for information on the product or services available in your area.
All statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.
Information about non-IBM products is obtained from the manufacturers of those products or their published announcements. IBM has not tested those products and cannot confirm the performance, compatibility, or any other claims
related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products.
Prices subject to change without notice. Contact your IBM representative or Business Partner for the most current pricing in your geography.
This information provides only general descriptions of the types and portions of workloads that are eligible for execution on Specialty Engines (e.g, zIIPs, zAAPs, and IFLs) ("SEs"). IBM authorizes clients to use IBM SE only to
execute the processing of Eligible Workloads of specific Programs expressly authorized by IBM as specified in the “Authorized Use Table for IBM Machines” provided at
www.ibm.com/systems/support/machine_warranties/machine_code/aut.html (“AUT”). No other workload processing is authorized for execution on an SE. IBM offers SE at a lower price than General Processors/Central Processors
because clients are authorized to use SEs only to process certain types and/or amounts of workloads as specified by IBM in the AUT.
* Registered trademarks of IBM Corporation
Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries.
Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom.
IT Infrastructure Library is a Registered Trademark of AXELOS Limited.
ITIL is a Registered Trademark of AXELOS Limited.
Linear Tape-Open, LTO, the LTO Logo, Ultrium, and the Ultrium logo are trademarks of HP, IBM Corp. and Quantum in the U.S. and other countries.
Intel, Intel logo, Intel Inside, Intel Inside logo, Intel Centrino, Intel Centrino logo, Celeron, Intel Xeon, Intel SpeedStep, Itanium, and Pentium are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the
United States and other countries.
The registered trademark Linux® is used pursuant to a sublicense from the Linux Foundation, the exclusive licensee of Linus Torvalds, owner of the mark on a worldwide basis.
Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates.
Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both.
OpenStack is a trademark of OpenStack LLC. The OpenStack trademark policy is available on the OpenStack website.
Red Hat®, JBoss®, OpenShift®, Fedora®, Hibernate®, Ansible®, CloudForms®, RHCA®, RHCE®, RHCSA®, Ceph®, and Gluster® are trademarks or registered trademarks of Red Hat, Inc. or its subsidiaries in the United States
and other countries.
RStudio®, the RStudio logo and Shiny® are registered trademarks of RStudio, Inc.
UNIX is a registered trademark of The Open Group in the United States and other countries.
VMware, the VMware logo, VMware Cloud Foundation, VMware Cloud Foundation Service, VMware vCenter Server, and VMware vSphere are registered trademarks or trademarks of VMware, Inc. or its subsidiaries in the United
States and/or other jurisdictions.
Zowe™, the Zowe™ logo and the Open Mainframe Project™ are trademarks of The Linux Foundation.
Other product and service names might be trademarks of IBM or other companies.
CICS*
Db2*
Telum
IBM *
IBM Cloud Paks
ibm.com*
the IBM logo*
z16
z/OS*

141

142
IT TRENDS & MAINFRAME
H. Gilabert - Expert of «Tendances de l’Informatique»

© Copyright 2022
IT professional and recognized expert, I have
accumulated diversified skills with
manufacturers, IT service companies, businesses,
as well as analysis and synthesis skills acquired in
the field of strategic consulting. I am also a
recognized speaker and lecturer.
Henri GILABERT
▪ «Système»
▪ Amdahl
▪ IBM
▪ Capgemini
▪ Compass
▪ Consultant
▪ SLA
▪ Synthèse Informatique
Henri GILABERT
Henri.gilabert@sla.lu
6 rue Joan DI
66110 Amélie-les-Bains
06.74.23.84.11
144

• More comfort;
• More quality;
• Low price.
New customer
habits
• Loyalty;
• Laws;
• Geography.
Less
effective
barriers to
entry
• More players;
• More value;
• Price pressure.
More
competition
More innovation in
• Products & services
• Production processes
• Commercialization
• Organization
Digital technologies © Copyright 2022
OECD, Oslo Manual

© Copyright 2022
✓ Three groups of trends.
✓ what about mainframes in all this?
✓ IT department, a value-added
reseller (VAR).
Knowing that...
"Prediction is difficult, especially when it
comes to the future"
Niels Bohr, Danish physicist
1992 Nobel Prize in physics
The complementarity principle was introduced
by Niels Bohr following the Heisenberg's
indeterminacy principle, as a philosophical
approach to the seemingly contradictory
phenomena of quantum physics.

Based on breakthrough technologies, such as millimeter
waves, NOMA (Non Orthogonal Multiple Access), MEC
(Mobile Edge Computing), massive MIMO (Multiple
Input Multiple Output), small Cells and Beamforming.
The first 5G networks will use carrier aggregation,
massive MIMO or NFV (Network Function
Virtualization).
Three major types of uses:
✓ mMTC – Massive Machine Type Communications:
communications between a large quantity and diversity of
objects with varied quality of service needs;
✓ eMBB – Enhanced Mobile Broadband: ultra-high speed
connection outdoors and indoors with uniform quality of
service, even at the edge of the cell;
✓ uRLLC – Ultra-reliable and Low Latency Communications:
ultra-reliable communications for mission-critical and very
low latency needs.
Public evidence is lacking to demonstrate
that Huawei would cooperate with Chinese
intelligence. But the equipment
manufacturer fails to demonstrate that it
poses no risk to the national security of the
States in which it equips network operators.
[…] The most worrying is the 5-year intrusion
into the computer systems of the African
Union headquarters The Huawei law is voted in France
Source : F. Launay Univ. Poitier

Urban area
Management of urban lighting, buildings, water,
heating, transport, pollution and municipal
governance (Barcelona Smart City).
Waste and bin management (Plastic Omnium),
Management of parking spaces and traffic by video
counting people & vehicles.
Home automation
Thermostats (Nest),
Switches, household
appliances, intrusion-
fire safety, weather,
flower pot (Parrot).
Business
Object location terminals
(SenseIOT).
Technical objects
integrated into the
product: label (tracking),
electronics (equipment
management).
Industrialized
"consumer" objects
(connected lock).
Health
Measurement of diabetes,
blood pressure, electro-
cardiogram, stress, rest, UV
index, toothbrush (Kolibree),
“Quantified self”
(measurement of personal
data) and Fitness (Adidas,
Fitbit)…
Personal
Glasses (SmartEyeGlass),
Smartwatch (iWatch),
Forks (Hapifork),
Fundawear (Durex), Child
monitoring (Buddy),
Elderly people
(UnaliWare), dogs-cats
(Pet-Remote)…
Vehicles
V2X (Vehicle to X
detection), V2V
(collision
avoidance).
Bridging objects
Routers / gateways
(Smart TV Box,
Connected car,
smartphone, tablet)
Triggers (Proximity)

© Copyright 2022
If the measurement/action couple is the basis of the service, the data collected
serves two distinct purposes:
✓ Hot data: Real-time data analysis feeds the feedback loop to control the
measurement/action pair as closely as possible (CEP).
✓ Cold data: Big Data analysis fuels deep understanding and strategy.
https://deepspace.jpl.nasa.gov/
https://www.confluent.io/kafka-summit-
san-francisco-2019/mission-critical-real-
time-fault-detection-for-nasas-deep-space-
network-using-apache-kafka

© Copyright 2022
Two different modes of operation:
Two ways to work;
Different cultures;
Who will want to work in "mod 1“*?
Transient or permanent cohabitation?
During the work, the store remains
open... and it’s likely to last!
IS agility the only criterion for all IS?
* (GG) Bimodal IT is the practice of managing two separate, coherent modes of IT delivery, one focused on
stability and the other on agility. Mode 1 is traditional and sequential, emphasizing safety and accuracy.
Mode 2 is exploratory and nonlinear, emphasizing agility and speed.

© Copyright 2022
Tools exist and the covid-19 pandemic has
been the biggest Poc (Proof of concept) in
history!
That said:
✓ Not all activities are suitable;
✓ Not all IS, applications and infrastructures
are ready;
✓ Security and privacy issues are increased;
✓ Psychological, organizational and working
conditions issues.
Real advantages for companies with
teleworking in terms of flexibility and cost.
Not to mention
LibreOffice Online,
Open365, OnlyOffice,
etc.

© Copyright 2022
Buying
mode
Description Examples in IT
On shelf Ready-to-use products that can easily
be obtained
Servers x86
Mass
customiza-
tion
Combines flexibility and customization
with low unit costs associated with
mass production
Packages,
cloud
computing
One of a
kind
Fully customized and unique solution
with the price that goes with
Specific
developments
✓ In the world of mass customization, the less you
customize the more is interesting, which is ideal
for back-office applications.
✓ Conversely, in the "front-office", customization is
essential to differentiate.
✓ The difficulty is to find the right balance.
"cloud computing is a mass customization market,
cloud vendors do their segmentation, they propose
you their offerings and, if we don't want it, you're
back in the One-of-a-kind and the price that goes
with it."

Data Mining
Email
Collaborative
Audio conferencing
videoconference
Development and test
environments in PaaS
mode Web hosting
Benefit
Ease of
implementation
ERP/CRM/SCM
For SMB
HPC &
Cloud AI
IoT and Complex
Event Processing
ERP/CRM/SCM
For large
companies
Traditional
transactional
applications
Workstation and
virtual prints
BPM
DevOps, Microservices
Distributed transactions
More or less easy to
implement with gains in
terms of cost and ubiquity.
Not very differentiating
Difficult to implement with
gains in terms of agility. Very
differentiating
BPM : Business Process Management
CEP : Complex Event Processing
CRM : Customer Relationship Management
ERP : Enterprise Resource Planing
HPC : High Performance Computing
SCM : Supply Chain Management
Everything As A Service
➢ Containers as a Service
(CaaS)
➢ Backend (BaaS) and
Mobile Backend (MBaaS)
for basic application
services
➢ Functions (FaaS) for a
ServerLess Cloud
➢ Platform integration
(iPaaS)
➢ Etc… But
An increasingly wide range
of services… of which the
most differentiating are the
most difficult to
implement.

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Sometimes imposed by regulations (eg PCI
DSS), it does not replace compliance with other
obligations (eg GDPR). Its role is to:
Stand above firewalls and other VPNs;
Track events and detect intrusions;
implement prediction rules.
SIEM (Security Information Management
System) for Information collection,
aggregation, normalization, log analysis,
correlations, detection of low-signals,
"replay" of events, ... (Microsoft, Splunk,
Exabeam, IBM, Securonix, etc.)

✓ Allows to enable disable,
encrypt, force company
policy
✓ The terminal "belongs" to
the company that entrusts
it to the user...
✓ The device "belongs" to the
user (BYOD) or the company
(COPE) who uses it
personally and for business
✓ Combination of MDM, MAM
and MIM
✓ Based on an app store
✓ Unified and consistent management of
all devices, OS and some IoT
✓ Management of configurations, profiles
and compliance.
✓ User-centric view.
Mobil Device Mgt, Enterprise Mobilty Mgt, Unified Endpoint Mgt
BYOD : Bring Your Own Device
COPE : Corporate Owned Personaly Enabled
MDM : Mobil Device Mgt
MAM : Mobil Application Mgt
MIM : Mobile Information Mgt

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Does Mainframe address these challenges?
✓ Yes, as well as all other platforms
✓ Even the more leading-edge concept like AIOps
With which advantages?
✓ Surely one of their best advantages, is their
ability to run legacy as well
With which weaknesses?
✓ Skills: despite a steady shift to a younger
workforce, mainframe is still perceived as
a legacy platform only…
✓ Mainframe is a high availability system,
but it is not built to fail*
* If you want to address this issue, you must run Parallel Sysplex or Dispersed Parallel Sysplex and be ready to pay the price that goes with

© Copyright 2022
Services can be provided internally or
externally.
The IT department has to decide where its
value-added for the company is the highest,
and then :
Outsource low value-added activities;
Insource services & activities with high added value
for the company.
Services and activities with high added value:
Ensure data confidentiality and security;
Collaborate with business lines in their transition
from applications to business processes;
Help business lines to take advantage of IT
innovations such as big data, social networks,
Internet of things, AI, design thinking, etc...
A value-added reseller is an
organization that enhances the value
of third-party products by adding
customized services for resale to its
customers.
Whatever the technology involved…

© Copyright 2022
Feeding customer’s
needs with the best
quality/price ratio
The IT department organizes its activities
according to the "value chain" concept.
Build Run
IT department management
Infrastructure
Gérer
la
relation
164
The value chain (1982)
Michael Porter

165
Moving from technology provider to service provider
Becoming business lines preferred VAR
Promoting ICT-based innovation
From OS to applications, having an Open Source strategy
Streamline infrastructure and IS:
For existing applications, it means making them as independent as possible of terminals
(RWD, RIA & RDA)
For new developments, it means making them as agile as possible (micro-services, agile
developments, DevOps, cloud-native)
For the infrastructure, it means “webizing” the workstation
Manage and redirect skills that will be less necessary (sharp technical specialists) towards
those that will be critical (cloud contract manager, data scientist, etc.)
Set up an organization able to meet the needs of reliability and agility (GG bi-modal IT
organization) (GG) Bimodal IT is the practice of managing two separate, coherent modes of IT delivery, one focused on stability and the other on agility.
Mode 1 is traditional and sequential, emphasizing safety and accuracy. Mode 2 is exploratory and nonlinear, emphasizing agility and speed.

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