The document outlines the capabilities and advantages of IBM's Cloud Pak for Data, emphasizing its forward-looking approach to data analytics and AI integration. It describes the platform's ability to collect, organize, and analyze diverse data types in a unified environment while leveraging advanced machine learning and compliance features. Key elements include microservices architecture, ease of use across multiple clouds, and tools for promoting data governance and operationalizing AI.

1. Hamza A. Ali
August 7, 2019
Cloud Pak for Data

2. 2
Disclaimer
Certain comments made in this presentation may be characterized as forward looking.
Forward looking statements are based on our current assumptions and understanding
regarding future business and offerings. Those statements by their nature address
matters that are unsure. Those statements involve a number of factors that could
cause actual results to differ materially. Any forward looking statement made during
this presentation speaks only as of the date on which it is made and is stated with
some degree of uncertainty. We assume no obligation to update or revise any forward
looking statements. These slides and the associated remarks and comments are
integrally related, and are intended to be presented and understood together.

3. 3
Introductions
• Hamza Ali
• NA Cloud Pak for Data Sales Leader
Hamza Ali
hamza.ali1@ibm.com

4. Su b t it le if n e e d e d
Agenda
- Why Change?
- Cloud Pak for Data
- Micro-Services
- Collect
- Organize
- Analyze
- Infuse

5. Why Change
Carson Masterson

6. IBM Data & AI / © 2019 IBM Corporation
IBM Data & AI / © 2019 IBM Corporation
COLLECT - Make data simple and accessible
ORGANIZE - Create a trusted analytics foundation
ANALYZE - Scale insights with AI everywhere
Data of every type,
regardless of where it lives
MODERNIZE
your data estate for an
AI and multicloud world
INFUSE – Operationalize AI with trust and transparency
The AI Ladder
A prescriptive, proven approach to accelerating the journey to AI
6
AI

7. Data
Analytic
s
90%
Low Maturity
Value

8. Data
Analytic
s
90%
Low Maturity
Value
Centralized
Data
Rework
Manual &
Slow

9. Data
Analytic
s
90%
Low Maturity
Value
Centralized
Data
Rework
Manual &
Slow
Data
Virtualization
Reuse
Analytics Success
Automated
& Fast

10. Cloud Pak for Data
Carson Masterson

11. Hybrid
Data Management
Unified Governance
& Integration
Data Science &
Business Analytics
• Collect all types of data, structured
and unstructured
• Includes all open sources of data
• Leverages a single platform with a
common application layer
• Write once and deploy anywhere
• Satisfy all matters of finding,
cataloging and masking data
• Integrates fluid data sets
• Delivers built-in compliance
• Leverages advanced machine
learning capabilities
• Delivers descriptive, prescriptive
and predictive insights across all
types of data
• Empowers all your teams and their
unique use cases
• Enables advanced analytics and
data science methods
Organize Analyze
• Db2 & Db2 Warehouse
• Db2 Event Store
• Integrated Analytics System
• Big SQL & Hortonworks Hadoop
• Information Server
• Data Replication
• Master Data Management
• Optim & StoredIQ
Lead with
Capabilities:
Lead with
Capabilities:
Lead with
Capabilities:
• SPSS & DSX
• Cognos & Watson Analytics
• Watson Explorer
• Planning Analytics
Collect
IBM Data & Analytics Portfolio
11
11

12. Today, we offer
individual products
to Collect, Organize
& Analyze data
IBM Cloud Private for Data
DATA
ONE
Information
Architecture
Hybrid Data
Management
Data Science &
Business Analytics
Unified
Governance
& Integration
Cloud Agility
Easily build data-driven apps, Pre-assembled personalized
experiences, Extensible with open APIs
Lightning Fast
Provision users in minutes, Fast Data ingest speeds,
Fast track projects with industry models
AI-ready
Machine Learning everywhere, Makes your data ready for AI
Now simplified into a “single offering” on private cloud
12

13. Cloud Pak for Data

14. Think 2018 / DOC ID / Month XX, 2018 / © 2018 IBM Corporation
Cloud Pak for
Data
Unified platform of foundational
Data & AI cloud services
Collect Organize Analyze
Db2 Mongo Cognos
Db2 ES OS
ISV
DSP Custom
Cloud Pak*
On-Prem
PICK YOUR KUB
*included with Cloud Pakd
PICK YOUR CLOUD
Private or Public
PICK YOUR ADD-ON
Containerized Services
AI INSIGHT PLATFORM
#1 Ranked by Forrester

15. •Current State Ideal Future State
• Containerized Platform
• Pre-Integrated & Governed
• Fast Provisioning
• Multiple Disjunct Stacks
• Siloed Data & Workflows
• Slow Provisioning

16. Micro-Services
Hamza Ali

17. Think 2018 / DOC ID / Month XX, 2018 / © 2018 IBM Corporation
Container 1
What’s a Microservice?
A microservice is a piece of an application that typically does 1
thing and does it well
What’s a Container?
A container allows a microservice to be portable
What’s Kubernetes?
Kubernetes orchestrates the use of containers to
optimize deployment and management of the
containers
Kubernetes
Container 2 Container 3 Container 4 Container 5
Reporting
Cognos Analytics
Dashboarding
MS1
Container
MS2 MS3 MS4 MS5

18. IBM Cloud / DOC ID / Month XX, 2017 / © 2017 IBM Corporation
RHOS + Cloud Pak for
Data
- Allows the Cloud Pak to
run anywhere
-Reduce complexity and
increase horizontal
scalability.

19. 19
The Ladder to AI
Cloud Pak for Data
Cloud Pak for Data
Foundational “out of the box” multicloud data & AI services
Powered by: Watson Studio open
source FW and Cognos
o Analytical visualization
o Machine learning learning
o Model build & deploy
o Model management
o Dashboards
Powered by: Infosphere,
Data Stage and IGC/WKC
o Discovery & search
o Data transformation
o Data cataloging
o Business glossary
o Policies, rules & privacy
Powered by: Db2 and Db2
Warehouse technologies
o Data virtualization
o Data warehousing
o Databases on-demand
o Data source ingestion
o Distributed processing
Collect Organize Analyze
Out of the Box iPhone

21. Hamza Ali
Collect Data

22. IBM Cloud / © 2018 IBM Corporation
A new approach to collecting data
Service
Node
Cluster
Constellation
Caching
Policy
Data Source
Node
Analytics
Application
Query anything, anywhere.
Query many heterogenous data sources
as one across cloud, on-premise and mobile
with advanced analytics using the most
popular languages and tools
Simplicity and scalability.
Automatically discover, and connect
few to many devices and data
stores into a single self balancing
constellation. Avoid the complexity of
centralized copies. Data only persists
at the source.
Execution speedup.
Many times acceleration using
the power of every device to
compute and aggregate results.
Security.
Fully secure and encrypted
communication and preservation
of data access rights at source.
1
2
3
4

23. What is
fundamentally
different?
IBM Cloud / © 2018 IBM Corporation
Classic Federation & Edge Computing
Query
coordinator
Query issued
against the
system
A coordinator receives the
request and fans the work
out to edge nodes
Edge nodes individually perform as much work as
they can based on their own data. Individual results
are sent back to the coordinator for final merging
and remaining analytics.
Coordinator receives intermediary
results from all edge nodes, merges
results, and performs remaining
analytics
Query Result
Query issued
against the
system
A coordinator receives the
request and fans the work
out to edge nodes
Edge nodes self organize into a constellation where
they can communicate with a small number of peers.
Nodes collaborate to perform almost all analytics, not
only analytics on their own data.
Coordinator receives mostly finalized
results from just a fraction of nodes.
Completes the final work for the query
result.
coordinator
New Computational Mesh
Query
Query Result

24. Hamza Ali
Organize Data

25. The path to governed data & analytics
Enabling governed self-service consumption through Cloud Pak for Data
Data Sources
Auto Data
Discovery
Profile: Data
Classify &
Term Assg
Data Curation
& Stewardship
Systems of
Record
3rd Party Data
Social Media
News
Systems of
Engagement
Other External
Weather
BI
Reporting
Dashboards
Regulatory &
Compliance
Reporting
Data Quality
Management
Machine Learning &
Self-Services
Analytics
Documents
Auto Data
Quality
Extract,
Transform,
Cleansing,
Standardize
Metadata
Catalog
Define Data
Governance
Objectives
Measure &
Monitor
Enforce
(Policies &
Rules)
Intelligent Metadata Catalog (infused with ML)
Shopping for
Information
The path to governed data

26. Hamza Ali
Analyze

27. 27
The Ladder to AI
IBM AI
Everything you need for Enterprise AI, on any cloud
Watson
Knowledge
Catalog
Watson
Studio
Watson
Machine
Learning
Watson AI
OpenScale
Build Deploy Manage
Interact with Pre-built AI Services
Watson Application Services
Catalog
Unify on a Multicloud Data Platform
IBM Cloud Private for Data
AI Open Source Frameworks

28. Data Sources Analytical
Data Management & Storage
Security (pre-integrated stack, user roles, monitoring, industry certifications, etc.)
Pre-integrated Platform, Deploy Anywhere (private cloud, on-premises, AWS, OpenStack, OpenShift, etc.)
Actionable
Insight
Analytics In-Motion
Enhanced
Applications
Discovery & Exploration
Ingestion &
Integration
Data
Access
Machine &
Sensor data
Image
& Video
Content
Services
Social
Data
Weather
Data
Commercial
Data Sets
Third-Party
Data
Transactional
Data
System of
Record Data
Data
acquisition
&
application
access
Internet
Data Sets
Application
Data
Customer
Insights
New
Business Models
Planning
& Analysis
Compliance
& Fraud
Security
28
Architecture Overview– Cloud Pak for Data
Operations
Information Management & Governance
DataStage
DataFlow
Designer
Data
Virtualization
Enterprise
Search
Data Refinery
Data Catalog
Cognos Dashboards
Watson Studio (Open
source components)
Data Science
(SPSS Modeler, Decision
Optimization, WEX, etc)
Watson OpenScale
Watson AI
Services
Cognos Analytics
Base:
- Postgres Netezza
-Db2 Warehouse (SMP,
MPP)
IBM Streams
Business
Glossary
Policies & Rules Autodiscovery
Industry
Accelerators
OMRS
Governance
Catalog
Outside Cloud PakD
Leverage Existing
Investments:
IIAS (Sailfish)
Hortonworks HDP
Cloudera CDH
Oracle
Db2, Db2zOS
MongoDB, Postgres
Netezza
Teradata
Microsoft SQL Sever
And more
Cloud Pak for Data (Base)
Customer Investments Outside Cloud PakD
Cloud Pak for Data – Premium Add-ons
Premium Add-ons:
Db2 AESE
Db2 Event Store
MongoDB
On Cloud PakD

29. Cloud Pak for Data
R E S O U R C E S
Walk-Through Demo - LINK
14-Day Guided Trial - LINK
Access to Cluster - LINK

30. 30
IBM AI on IBM Cloud Private for Data
Data Collection Services Data Organization Services
Open Source frameworks to build,
train and deploy Machine Learning
models powered by Watson
Studio and Machine Learning
AI Services o Data visualization
o Machine learning learning
o Model build & deploy
o Model management
o Dashboards & reporting
Watson Studio/ML
Data Science Premium
Premium tools to design,
build & deploy AI models
Watson AI OpenScale
Watson Assistant
Watson Discovery
Watson APIs
AI Digital Automation
Operational optimization services
Conversational AI services
Knowledge discovery AI services
Interactive AI services (e.g., speech)
AI-automated business processes
Foundational
AI Services
SPSS
Modeler
Data
Refinery
Model
Builder
Decision
Optimization
Watson
Explorer
Streams
Designer
Add-on
AI Services

31. Questions?
Name
Phone number
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