ICP for Data- Enterprise platform for AI, ML and Data Science

IBM Cloud Private for Data
An Enterprise Platform for AI, ML and Data
Science
—
Karan Sachdeva
Big Data Sales Leader
IBM Analytics, Asia Pacific
karan@sg.ibm.com

Agenda
a) Challenges banks face with AI, ML and
Data Science adoption
b) ICP for Data Overview
c) Demo – 10 Minutes
d) Business and IT use cases supported
e) Justifying value/investment- ROI
f) ICP for Data Community and resources
g) Data science Elite team
h) Services offers to get you started

3
Artificial Intelligence…
“Over the next decade, AI
won’t replace managers,
but managers who use AI will
replace those who don’t.”
“Business of Artificial Intelligence” by Harvard Business Review
Source: https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence

IBM Cloud / © 2018 IBM Corporation
Barriers to implementing AI
You need a Data
Platform-
a) Brings skill barrier
down
b) Is secure and
governed
c) Collaborates across
organizational
boundaries

Barriers to successful AI transformations
There is no Artificial Intelligence (AI) without Information Architecture (IA)
Data Ecosystem
• Data in silos
• Difficult to access
• No lineage
Analytics Tools
• Discrete tools
• Different preferences
• Difficult to manage
Workflow
• Not integrated
• Not governed
• Lack dev/prod parity
Culture
• Not collaborative
• Slow provisioning
• Lack trust in AI

FS Industry has big problem of all data in silos
5
Information is the Primary Resource of Financial Services
Mapping and Consolidation Are the Tip of the Iceberg for Big Data
Retail Banking
• Bank Transactions
• Customer Data
• ATM Activity
• Online Activity
• Mobile Activity
• Demographic / Census
Data
• Marketing / CRM
• Social / Sentiment
Credit Cards &
Payments
• Card Transactions
• Customer Data
• Online Activity
Data
• Marketing / CRM
• Integration with
Retailers / Loyalty
• Social / Sentiment
Investment
Banking
• Trade / Tick Data
• Customer Data
• Web Logs
• Research /
Publications
• Market Data
• Communications /
Documentation
Insurance
• Claims / Policy Data
• Customer Data
Data
• Weather Data
• Vehicle Telemetry
• Video / Surveillance
• Sensors
• Internet of Things
Services & SROs
• Trade Data
• Communications /
Documentation
• Market Data
• Research / Publications
• Surveys
Cost SavingsCompliance
Customer
Insight
Competitive
Advantage

Cloud Agility is critical component for AI
driven culture World is moving to a multi-cloud future
7
Traditional
IT
Public Cloud
Virtual
Private
Cloud
Private
Cloud
Portable Applications

Matching the right cloud
to the right workload
Maintain
& Evolve
Private
Cloud
Public
Cloud
Highly customized
applications
Not yet virtualized
applications
Applications with
complex processes
and transactions
Mature workloads
Workloads needing
low latency to back ends
Existing database
workloads
Applications with
sensitive data
Regulation-intensive
applications
Information-intensive
applications
Batch processing
Backup & archive
ERP
Big data & analytics
Front office/desktop
Disaster recovery
Risk & compliance
services
Web applications/ e-commerce
Digital experience solutions
Customer service
Enterprise social solutions
Third-party applications
Mobile applications
Non-core business processes
Development and test
workloads
Enterprise transformation
required for cloud adoption
DevOps | Governance | Integration
Security | Architecture | Culture
* IBM Institute for Business Value study,
“Tailoring Hybrid Cloud” August 2016

IBM Cloud Private for Data- An End to End Integrated
Analytics Platform
Collect Organize
360 degree view of
all data
Shop for Data
Automated/
Collaborative Data
Science
Analyze

10
Right Platform + Right IA = Effective AI
§ What if you had a way to quickly
improve your Information
Architecture?
§ What if you could modernize your
Information Architecture – operate
in a hybrid environment and get
ready for Cloud?

Enterprise Data Catalog
11
Private
Cloud
– Data integration
– Data cataloging
– Governance
– Data lifecycle mgmt
IBM Cloud Private
Cloud-native Data Micro Services
Instant Provisioning - Dynamic Dashboards
– Customized DBs
– Data warehousing
– Fast Data Ingest
– Federated query
– Predictive Modeling
– Machine Learning
– Mathematical models
– Dashboards, reporting
New
Make your data ready for AI with a seamlessly
integrated and governed platform
• Access, govern and analyze all data
irrespective of the type of data source and
where it is hosted
• Modernize data infrastructure: Cloud native,
Micro services based behind the firewall
• Enhance workforce productivity: Provision and
scale in minutes
• Reduce costs of managing multiple product
stacks
• Lightning fast ingest (250 billion+ events/day)
• Automation and governance infused throughout

Apps + insights
Social
LOB
Graph
IoT
Image
CRM INGEST STORE PREP & TRAIN MODEL &
SERVE
Data orchestration
and monitoring
Data lake
and storage
Hadoop/Spark/SQL
and ML
.
IoT
T H E A I D E V E L O P M E N T L I F E C Y C L E

13
Increases workforce productivity across the analytics lifecycle – governed seamlessly
Private
Cloud
Cloud-native Data Micro Services
Instant Provisioning - Dynamic Dashboards
Architects data pipelines and
ensures operability
Gets deep into the data to draw
insights for the business
Works with data to apply insights
to business strategy
Plugs into analysis and code to
build apps
Access
data
Transform:
cleanse
Create
and build
model
Evaluate
Deliver and
deploy model
Communicate
results
Understand
problem and
domain
Explore
and
understand
data
Transform:
shape
DEPLOY
ANALYZE
COLLECT Data Engineer
Data Scientist
Business Analyst
App Developer
ORGANIZE
Governs data and ensures
regulatory compliance
Data Steward
CXO
Sys
Admin

© 2016 IBM Corporation
IBMInstituteforBusinessValue
14
Data scientists work best in open, collaborative
based on strong governance processes
Encourage a disciplined approach to deliver greater value
Page 14
Data scientist Business-driven
governance committee
Guided by:
Analytic strategy
Prioritization process
Project funding process
Source: Mastering the art of data science. IBM Institute for Business Value.
© 2016 IBM. ibm.biz/artofdata

15
Lead data
scientists
works with a
team of
supporting
specialists
on each part
of the team’s
methodology
Create data science teams with varied backgrounds and skills
Page 15 Source: Mastering the art of data science. IBM Institute for Business Value.

16
Data science is data-driven, and too often organizations devote
insufficient time and resources to the data to achieve objectives
Create data science teams with varied backgrounds and skills
Page 16 Source: Mastering the art of data science. IBM Institute for Business Value.

Model Lifecycle Management
Machine Learning Runtimes Deep Learning Runtimes
Authoring Tools
IBM Cloud Private Infrastructure
• Best-of-breed tooling from open ecosystem
• Authoring tools
• Machine learning, deep learning, optimization
• Roll-your-own environments
• Coding and visual modeling options
• Cloud private infrastructure
• Container-based resource management
• Scale with distributed and GPU support
• Model Lifecycle Management
• Dev -> Test -> Staging -> Prod
• Versioning, release, SLAs, rolling upgrades
The best of Open Source and IBM for training, deploying, and managing AI

Value Drivers - Stakeholder Pain Points
The motivation behind IBM Cloud Private for Data
• IT expense model
• Project risk-adjusted return
• Cloud architecture
• Security, governance, and compliance
• Day-to-day project management
• Integrating outputs from working teams
• Keeping systems operational
VP Analytics/IT
VP Line of Business
Financial Sponsors
CFO
VP Finance
VP
CxO
Chief Information Officer
Chief Data Officer
Responsible for data governance
• Enforcing business policies
• Compliance with regulation
• Tagging data assets
• Annotating data assets
• Validating data quality
• Validating data lineage
• Tracking and protect data usage
Data Stewards
Responsible for systems & infrastructure
• Provisioning capabilities
• Configuring, upgrading, and automating
• Allocating and managing resources
• Monitoring system health
• Ensuring uptime and performance
• Maintaining system security
• Maintaining multiple product stacks
• Ensuring parity between dev, test, and
prod environments
System Administrators
Responsible for capturing the value of AI
• Cross-functional collaboration & skills
• Data ingest
• Data access and federation
• Data quality, lineage, policies
• Reproducible data pipelines
• Machine learning & optimization
• Analytic asset sharing and cataloging
• Model management and SLAs
• Model trust, quality, and retraining
Analytics Teams: Data Scientists,
Data Engineers, App Developers

Tactic 1 – Lead with the Use Cases. Top ICP for Data Use Cases
1 Top Use Cases 2 Value Prop 3 Top Partners 4 Top Offers 5 Resources 6 Evidence
• Finding the data you need is half the battle.
That’s why you need to have 360 degree
view of all your data.
• Enable governed and secure access to your
data – structure and unstructured,
regardless of where it lives
• Cross-organizational platform that breaks
down the traditional data silos, freeing the
data so all users have access.
• Scalable Data
Tech
• Solusi
• Optus
• Certus
• Certiware
• ICP for Data Cloud
Native Edition 48
VPC offer
• Free 90 days
sandbox offer
• One year
subscription offer for
48 VPC Cloud Native
Edition
5 minute Demo
Reference architecture
• Self-service data users can “shop” for the
data they need in the data catalog and find
trustworthy data quickly
• . Providing critical context to enterprise data
in order to increase trust and understanding
of the data
• Life-and-death decision-making that
mandates the data used meets the highest
levels of accuracy, integrity, precision, and
clarity
• TDGlobal
• Alpharythm
Native Edition 48 VPC
offer
• Free 90 days sandbox
offer
• One year subscription
offer for 48 VPC Cloud
Native Edition
5 minute Demo
• Collaboration – between business
stakeholders, data stewards, data
scientists, subject matter experts, and data
professionals – enabling everyone in an
organization to collaborate and benefit from
the AI and Data Science.
• Leveraging open source tools like
Tensorflow, Spark etc to productionise the
business impacting AI and data science use
cases impacting revenues like enhanced
customer experience, personalized
marketing offers, next best action etc.
• Cartesian
Consulting
• DXC
Native Edition 48 VPC
offer- USD 100K
• Free 90 days sandbox
offer
• One year subscription
offer for 48 VPC Cloud
Native Edition
5 minute Demo
360 degree view of all
your data
Shop for Data
Collaborative data
science/AI

Banking AI Use Cases
Investment
Bank
Risk
Trade Reporting
Trader Surveillance
Tick Storage
Compliance
AML
IT / Corp
Functions
Log Management
Cyber Security
ETL Processing
Active Archive
Compliance
DW Optimization
Retail
Banking /
Cards
Data Products
Fraud Detection
Customer 360
degree
AML
Digital
Asset
Management
Investment Research
Fraud Detection
Recommendation
Engine
Trader Surveillance

Industry Use Cases
AI Use Cases enabled by data science: Burning Business Problems More Robustly Addressed
Financial
Services
Insurance
••Credit Scoring
••Fraud Analytics
••Compliance- GDPR,
PDPA etc
••Risk Analytics
••Operational Data Store
••Predictive credit card
churn analytics
Telco
Media
Utilities
••Customer Insights
••Network Optimization
••Data Monetization
••EDW Augmentation
••Predictive maintenance
Retail
Ecommerce
••Loyalty programs
••Upsell/Cross sell
••Agile Supply Chain
••Next Best Offer/Action
••Connected Store
••Operational Data Store
••IoT – Stores
Manufacturing
Industrial
Automotive
••Connected: Car, Plane,
Equipment
••Agile Supply Chain
••Predictive Maintenance
••IoT Data enabled
“Smart Services”
Government
Public Sector
••Border Control
••Public Safety /
Intelligence
••360 Tax payer
••Tax Optimization
••Cyber Threat
••Citizen Self Service
••Social Services Fraud
Enterprise data warehouse(EDW) Modernization | EDW Offload | Teradata/Oracle TCO savings
Enriched Customer 360 | Citizen 360 | Product 360
Recommendation Engine | Next Best Action
Hyper Personalization

Data
Metadata
ML Models
IBM Cloud Private for Data + IBM Watson Studio
Interoperability for hybrid cloud use cases
Prepare data once and access anywhere
• Create enterprise data catalogs on enterprise data, imported into IBM Watson Studio and vice-versa
• Easily share machine learning metadata across the enterprise, and on-premises and public clouds
Build once and run anywhere
• Create and train machine learning models behind firewall on enterprise data, deploy them to IBM Watson Studio
• Use IBM Watson Studio for creating resource intensive deep learning models and deploy them on ICP for Data
Ensure secure data inter-operability
• Securely access & share data dynamically, migrating data quickly and seamlessly between on-premises and cloud
IBM Cloud
Private for Data
(IBM Cloud Private)
IBM Watson
Studio
(IBM Cloud Public)

IBM Cloud Private for Data – Packaging
Community Edition*
Build apps and manage data
in non-production
ü Try for free
ü Hosted Trials available
Make your data ready for AI – Cloud Agility, Lightning Fast & AI-ready
ICP Foundation = ICP restricted version to use for Data Analytics Services. If client
wants ICP for other services outside data, will need to purchase ICP separate
Cloud Native Edition*
Collect, Organize & Analyze data in
production
Entry level with 1 Click install with capacity
and scalability restrictions
Enterprise Edition
Full function deployments with enterprise QoS, add-on
modules & AI ready
Full function deployments with enterprise QoS
Basic Support Basic & Premium Support
* On roadmap for 2H 2018
includes “ICP foundation”

• Core operational services
including logging,
monitoring, security
• Flexibility to integrate
with existing tools and
processes
• Integration capabilities to
unlock and connect
• Secure access to
public cloud services (AI,
Blockchain)
• Consistent experience
across private/public
• Containerized versions of
IBM Middleware
• Prescriptive guidance to
optimize workloads
• Work with existing apps,
data, skills, infrastructure
• Open Kubernetes-based
container platform
• Cloud Foundry for app dev
and deployment
• DevOps toolchain
integration
Rapid Innovation Hybrid Integration Investment Leverage Management & Compliance
IBM Cloud Private brings cloud
native to the enterprise

IBM Data Science Elite Team
IBM’s Data Science Elite team
co-engineer prototypes with
clients to succeed and lead in AI
via Data Science.
What do we offer?
ü Free onsite engagement
ü Identify use case(s) & Minimal
Viable Products via discovery &
design workshops
ü Collaboratively build & evaluate
data science and machine learning
models
ü Mentor & enable client teams
hands-on
Write to me at- karan@sg.ibm.com
Read more at- http://www.ibmbigdatahub.com/blog/author/karan-sachdeva

IBM Analytics Services
ICP FOR DATA OFFERINGS
The Solution Initiation Workshop will use our expertise to:
• Integrate ICP for Data into the Client’s environment
• Demonstrate the full lifecycle capability of ICP for Data from initial data organization and exploration
through to maintaining analytical models with collaboration features built-in
• Guide you around adoption of diverse open source and value added tools and libraries for metadata,
governance and machine learning
The MVP Build has been designed to provide:
• Quick validation of the Client use case developed in your Solution Initiation Workshop
• Easy adoption of diverse open source, value added tools and libraries for metadata, governance and
machine learning
• Expert knowledge by leveraging IBM expertise across multiple industry domains
Frame and accelerate your success around ICP for Data with
IBM Lab Services

IBM Cloud / DOC ID / Month XX, 2017 / © 2017 IBM
Corporation
30
Artificial Intelligence, impact on business…
A single use case in large US based Insurance Company,
Built a simple ML model that cost about 150K,
But in production it saved the company 15 Million annually..
“We cannot solve our problems
with the same thinking we used
when we created them.” – Albert
Einstein

ICP for Data- Enterprise platform for AI, ML and Data Science

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