Automate your Kamailio Test Calls - Kamailio World 2024
Customer Presentation - IBM Cloud Pak for Data Overview (Level 100).PPTX
1. Instructions on how to use this deck
1. Delete this slide before use
2. Always customize your presentation for the intended audience. This deck covers market
dynamics, Cloud Pak for Data overview, client results, Ladder to AI video demo, and how to get
started with Cloud Pak for Data. It’s unlikely you would use all slides during a single meeting.
3. This deck contains 6 embedded video records within the “Ladder to AI” end-to-end demo. This
demo takes 15 – 25 mins to pitch. Speaker script and example recording of how to pitch the
demo are available here
4. The Appendix contains slides on Cloud Pak for Data licensing and the overall IBM strategy
1
5. CEOs have a
consistent top
priority-harness
digital transformation
to jump start growth,
speed time to market,
and foster
innovation…
5
“
Source: McKinsey&Company, “Unlocking business acceleration in a hybrid cloud world” Report (August 2019)
of global executives
say they won't
achieve their growth
objectives without
scaling AI.
of global executives
globally believe they
risk going out of
business in 5 years
if they don't scale AI.
Source: Accenture, “AI – Built to Scale” Report (October
2019)
6. “
CIO reasons for pursuing
Infrastructure
Modernization
Source: McKinsey&Company, “Unlocking business acceleration in a hybrid cloud world” Report (August 2019)
CIOs believe that
business benefits
cannot be achieved
by lifting and
shifting
applications and
need rethink the
infrastructure
stack…
Talent and Skills
Security and Risk
Reduction
Cost efficiency
Quality of Services or
Reliability
Agility and time to market
Source: McKinsey expert interviews (N=52)
28
27
20
19
6
7. 7
No amount of AI algorithmic sophistication
will overcome a lack of data [architecture]
“
Data collection & preparation is the most
time consuming and difficult part of AI
Sources: 2018 MITSlone ”Reshaping business with AI”
There is no AI without an IA
“Information Architecture”
8. The AI Ladder
COLLECT - Make data simple and accessible
ORGANIZE - Create a business-ready analytics foundation
ANALYZE - Build and scale AI with trust and transparency
INFUSE - Operationalize AI throughout the business
AI
MODERNIZE
Make your data ready for an
AI and hybrid cloud world
One Platform,
Any Cloud
Talent &
Skills
9.
10. Eliminate data silos,
connect all data
Automate and govern
the data & AI lifecycle
Operationalize AI with
trust & transparency
Avoid lock-in, run
anywhere with agility
Visualize data of every type,
regardless of where it lives
The AI Ladder
Cloud Pak for Data
Certified on Red Hat OpenShift
11. Collect
– Data virtualization
– Provision SQL and
NoSQL databases
– Event ingestion
– Streaming Analytics
– Apache Spark
IBM Cloud Pak for Data
The AI Ladder Analyze and Infuse
– Business reporting
– Data science and visualization
– AI lifecycle automation
– AI Apps
– Industry accelerators
App Developers and SREs| Business Partners | Data Engineers | Data Stewards | Data Scientists | Business Users
Extensible: APIs, partner ecosystem, accelerators, and solutions
Integrated User Experience
Core services – User access management
– Security contexts and RBAC
– Volume management
– Monitoring and metering
– Service provisioning
– Operators
– Diagnostics
– Backup and migrate
IBM Cloud | Amazon Web Services | Microsoft Azure | Google Cloud | Hyperconverged
system
Red Hat OpenShift
Infuse
Analyze
Organize
Collect
Organize
– Data transformation
– Data quality and classification
– Policies and rules
– Data cataloging
– Self-service discovery &
search
12. Cloud Pak for Data
+
• A hyperconverged system combing storage, compute,
networking, and software into a single system
• Reduces complexity and increases scalability
• Extensible system – think “Lego for the data
center”
• No-assembly required data & AI services
from Cloud Pak for Data
• Eliminate silos, connect to all data
• Automate and govern the data and AI
lifecycle
• Operationalize AI with trust and transparency
13. Cognos
Analytics
Business Intelligence
Reporting & Metrics
Data Visualization
Planning
Analytics
Financial Analysis
Next-gen CFO
Planning & Budgeting
Reg
Tech
Payments & Insurance
Anti-money
Laundering
Conduct Surveillance
Watson
Discovery
Compare & Comply
Documents
Knowledge Work
Watson
Assistant
Expert Assistance
Next-gen Call Center
Customer Experience
Multicloud Data and AI Platform
Cloud Pak for Data – Collect, Organize, Analyze Infuse
Hyperconverged
Private Cloud Systems
IBM Cloud
AI Apps
17. Client Results with Cloud Pak for Data
link
Differentiators
• Unified user experience
increases agility, security,
and productivity for data
engineers, data scientists,
business analysts, and
SREs
• Broad set of integrated
Data & AI capabilities
• Secure container platform
Red Hat OpenShift
• AI-power automation
infused throughout the
data and AI lifecycle
Business Impacts
• Reduced ETL
requests by
• Data
virtualization with
data
access
• Data science
benefits of
• Reduced
infrastructure
management
effort of
Financial Impacts
• Total economic
impact net
present value of
• Projected return
on investment of
“The improvement in overall
governance and control with
Cloud Pak for Data,
especially from a regulatory
perspective, is significant –
it has exceeded our
expectations.” Director of
data management, financial
services industry
“Data virtualization is a
major benefit. By giving self-
service capabilities to users,
they will have more
understanding of what
enterprise data there is, and
also understand if their data
request is worth sending
through the ETL chain. And
when the development team
does get a request at that
point, they’ll know it will be
an important request.”
Information architect,
financial services industry
A general manager stated:
“Time-to-market and time
to innovate has to be faster,
and we need to do more
without doubling the data
scientists we have on staff.
18. 18
Modernize
Unlock the value of your data for
an AI and hybrid cloud world
Collect Organize Analyze Infuse
AI driven insights
for speed and
efficiencies gains
Benefit: Accelerate
innovation
Sprint chose Cloud Pak for
Data because it enables AI
projects in weeks rather
than months through
unifying and simplifying
three critical stages in the
journey to AI: the collection,
organization and analysis of
data.
Facing its own path toward
digital transformation,
Sprint started preparing its
data for Artificial
Intelligence (AI) – with the
goal of using machine
learning algorithms to gain
quicker insights and
increase responsiveness
to customers.
Michelle Gehl
VP Networks OSS
Applications and
Operations, Sprint
“IBM Cloud Pak for Data
enabled Sprint to digest high
volumes of data for near, real-
time ML/AI analysis, and the
trial results have shown
potential to take Sprint to the
next phase of digital
transformation.”
A deeper look in the press:
Business Chief US,
Business Chief Canada ,
Gigabit Magazine
Sprint
Industry: Telecommunications
Geography: North America
19. 19
Collect/Organize
Unlock the value of your data for
an AI and hybrid cloud world
Collect Organize Analyze Infuse
Rapid deployment
and scaling of AI
Associated Bank is
adopting IBM Cloud Pak
for Data System, for rapid
deployment and scaling of
AI. Initial projects the bank
is planning to work on
include a new Customer
360 system for improving
client experiences and a
new governed data
dashboard for improved
analytics results.
Steve Lueck
Vice President, Data Management
Associated Bank
"One of the great things about the Cloud Pak for Data System is the
speed with which we'll be able to launch and scale our analytics
platform. The integrated stack contains what we need to improve
data quality, catalog our data assets, enable data collaboration, and
build/operationalize data sciences. We're able to move quickly with
design, test, build and deployment of new models and analytical
applications."
Associated
Bank
Industry: Banking and Financial Markets
Geography: North America
Benefit:
Cloud Pak for Data System
provides the Bank a single
interface platform for end-
to-end enterprise analytics
that immediately leverages
their many existing data
sources.
20. 20
Analyze
Unlock the value of your data for
an AI and hybrid cloud world
Collect Organize Analyze Infuse
IBM Data Science
Elite helps Standard
Bank South Africa
accelerate and
commercialize data
science and AI use
cases across the
bank
Industry: Banking and Financial Markets
Geography: Middle East and Africa
“It was an immense pleasure to
partner with IBM Global DSE
team. The 12 week journey
allowed us to identify gaps in our
processes, re-imagine our delivery
and commercialisation process as
well as leverage some of the best
in class technologies and practices
to deliver our solutions in a rapidly
changing environment.”
John Mukomberanwa
Head: Digital Insights
Corporate and Investment Banking
Standard Bank South Africa
Benefit:
Increase and expand
deployment of AI at
Standard Bank, to enhance
the value of data science
and inject the results into
workflows for business
users and clients and to
map out a way forward for
AI at the bank.
Data scientists and
engineers spend much of
their time on grunt-work.
Workflows are often
repeated and models are
remade and retained for
every new use case.
Reimagine and scale
data science
21. 21
Infuse
Unlock the value of your data for
an AI and hybrid cloud world
Collect Organize Analyze Infuse
transforms Call
Center into an AI-
powered intelligent
business
With Cloud Pack for Data, call center
organizations can leverage Watson's
core capabilities wherever their data a
nd applications reside. In addition, Clo
ud Pack for Data enables en
terprise customers to build AI reg
ardless of the location of the call cent
er-related data and applications. This
makes building intelligent call center
s simpler, faster, and more cost-ef
fective to operate.
Hansol Inticube
Industry: Technology
Geography: Asia Pacific
"Recently the financial market has d
eep interest in the next-generation c
all center utilizing AI-based voice ag
ent. Collaboration between IBM, wh
o has the most advanced AI tec
hnologies for enterprise, and Han
sol Inticube, who is the leader of the
financial call center solutions and se
rvices, will be able to bring togeth
er the most powerful solution to the
market. We will drive busin
ess with proven capabilities and ex
pertise through various project
s we’ve already conducted.”
Gwang-sun Go, CEO of Hansol Inticube
Natural language processing grasps
the expression of the customer in the
overall context and natural language l
earning recognizes the intentions a
nd nuances included in the
customer's query. It also uses
Watson Speech to Text, which
converts voice into text to help
understand the context of the
question. This allows AI voice agent t
o quickly provide the best answer in t
he context of a customer inquiry.
Benefit
The demand for intelligent call centers
is growing as an AI voice assistant
can address customers queries and
further connects them to human
agents only when detailed
consultation is required. In addition to
responding to customers more
quickly, the call center staff can now
handle more complex queries, and
will not need to handle repetitive
tasks, improving the overall
operational efficiency and quality of
customer service.
23. App
Deployment
Data
New Requirements
& Engagement
Development
Test
Monitoring &
Retraining
Data Access
Acquiring Data &
Self Service
Model Design
& Deployment
Refining
Data
Continuous Delivery
of Applications
Continuous Delivery
of Data & AI
Transforming the Delivery of Data & AI to the Business
> Hybrid Multicloud
> Unified User
Experience
> Security
24. Consider an
example of a
German Bank…
The Credit
Department
must increase
revenue while
balancing risk
25. The problem
is… 1. Hundreds of data
sources, scattered
everywhere.
2. Building & deploying AI
models that balance
credit worthiness with
risk of default.
3. Explaining how AI models
work. Often to regulators.
33. Understand the structure and
dynamics of data used to train AI models.
The AI Ladder: Organize
System Admin Data Engineer Data Scientist Risk Analyst
39. Operationalized AI models that
are monitored, explainable, and trusted
The AI Ladder: Infuse
System Admin Data Engineer Data Scientist Risk Analyst
40.
41.
42. What
Solved:
Now, the German Bank can:
1. Use a common Data and AI
stack anywhere they need
2. Access hundreds of data
sources as one
3. Offer governed Self-service
4. Quickly explore data and
use “AI for AI” to build AI
models
5. Explain AI predictions
including to regulators
44. Driving Outcomes
Ladder to AI Value
Experience
Better understand
how data and AI
drives revenue,
costs, agility, and
risks that impact
your business
Data
Modernization
Workshop
Discovery how to
modernize your
data topology for
hybrid multicloud
to accelerate data
access by 40X and
reduce ETL by 64%
DataOps
Workshop
Investment from
IBM in your
business to
improving the
communication,
integration, and
automation of data
flows between data
managers and data
consumers
Data Science & AI
Elite Team
Have your data
scientists working
side by side with
our data scientists
to jointly
implement data
science and AI
projects
45. Ladder to AI Value Experience
https://viewer.mediafly.com/ibm
46. DataOps Center Of
Excellence Team
• Missioned to work with customers to show
the value & impact DataOps delivers
• Dedicated team of highly skilled, experts and
thought leaders
• Rooted in development, offering
management and SMEs for the direct
connection to latest innovations
47. Meet the Data Science and AI Elite Team
47
Who are we?
A global and diverse
team of Data Science
and AI experts with
deep knowledge of all
aspects of AI/ML.
What do we offer?
Guidance, skills, tools,
and a proven
methodology to put AI
projects on an
accelerated track and
scale.
What do you get?
Investment from IBM in
your business with the
expertise to transform
your business with AI
What do we ask for?
Your data scientists
working side by side
with our team
Your happy references
to others
6
Industries
39
References
42+
Countries
130+
Total engagements
51. IBM Cloud Pak for Data is licensed by Virtual Processor Core.
A virtual processor core (VPC) is based on the number of virtual cores (vCPUs) that
(vCPUs) that are available to a product. A vCPU is a virtual core that is assigned to a
assigned to a virtual machine or a physical processor core if the server is not
partitioned for virtual machines.
Licensing can be Perpetual or Committed Term:
• Perpetual: Client purchases the software license and commits to a minimum 1 year of Subscription and
Support (S&S). If Software Subscription and Support is not renewed, Licensee will no longer receive the
receive the benefits of Software Subscription and Support, but Licensee may still use the software
software according to the license agreement.
• Committed Term License: Like monthly licenses, Committed Term Licenses enable clients to purchase
licenses to IBM software in a cloud-centric model. This new licensing method aligns to cloud-centered
centered consumption models. Clients must commit to a specific term (1 to 5 years), and can pay
pay monthly, quarterly or annually.
How IBM Cloud Pak for Data is Licensed?
51
52. Advanced
Technologies
Cloud Paks
Foundation
Infrastructure
AI Blockchain Quantum
Cloud Pak for
Applications
Cloud Pak for
Data
Cloud Pak for
Integration
Cloud Pak for
Automation
Cloud Pak for
Multicloud
Management
Open Hybrid Multicloud Platform
IBM public
cloud
AWS Microsoft
Azure
Google
Cloud
Endpoints
Private IBM Z
IBM LinuxOne
IBM Power
IBM Storage
Consulting
Services
Strategy Migration Development Management
IoT
Analytics
ISV Applications/Solutions
Build once. Deploy anywhere.
Cloud Pak for
Security
52
The problem that many enterprise face is that 80% of data is either inaccessible untrusted or unanalyzed.
According to MIT Sloan, “No amount of AI algorithmic sophistication will overcome a lack of data [architecture]…bad data is simply paralyzing.”
Put simply: There is no AI without IA (information architecture).
---------------------------
Below are some stats and additional information to support the bullet points above. If used, full source accreditation is necessary:
A report by MIT Sloan compared AI goals and efforts of over 3000 business executives, managers, and analysts. Only 20% of companies surveyed have implemented AI into some of their processes. Fewer than 39% have any AI strategy at all. According to the research, companies that are not investing in AI tend to misunderstand the resources that are needed to do so:
AI algorithms learn inductively and must be trained.
An information architecture is necessary to provide access to data needed for AI.
Companies that lead in AI have senior leadership support and a developed business case for implementing AI.
Sometimes a culture change is needed to provide the organizational flexibility to support AI and accessible data.
Most AI leaders view this technology as a way to reduce boring or unpleasant tasks rather then to replace personnel and jobs.
https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/
IBM is uniquely suited to language based on our work with our clients–as we help them navigate what we refer to the ladder to AI of collecting, organizing and analyzing their data, and ultimately operationalizing AI throughout their business
Cloud Pak for Data is also now certified on Red Hat OpenShift.
Full certification brings added confidence knowing that all the components came from a supported source.
Container images contain no known vulnerabilities.
Most importantly containers running throughout are compatible across Red Hat Enterprise Linux environments, regardless of the cloud type.
As I mentioned earlier, Cloud Pak for Data is your hybrid, multicloud platform to give your employees superpowers.
And we’re not stopping there. Today, I also want to announce…
When clients start with IBM Cloud Pak for Data, they get the foundational capabilities for the COLLECT, ORGANIZE and ANALYZE rungs on the AI Ladder, all integrated via the flexibility of cloud-native microservices. They can access data, create policy-based catalogs, build open source AI/ML models, visualize data with dashboards, and more. You can think of these capabilities as cloud-native microservice versions of our core offerings (like Db2, InfoSphere, DataStage, Watson Studio, and Cognos Analytics), integrated into a platform. No assembly required.
Another one of IBM’s key differentiators is our ability to help customers achieve business value much quicker with pre-built applications. These add-ons provide a competitive advantage by helping businesses predict and shape outcomes, optimize labor value, automate decisions and processes, and reimagine business models.
The top use cases for pre-built applications we see in the market are:
Watson Assistant transforms how people engage with your business with conversational AI services
Watson Discovery employs AI to help people find answers to complex topics
Business Analytics for business intelligence as well as planning, budgeting, and forecasting
Business Automation powers AI infused business processes
So, today, I’d like to take you through an example of how Cloud Pak for Data actually applies to a business use case. The use case I’m going to use is a German Credit Risk company.
Their Credit Department must increase revenue while balancing risk.
Global bank… roll-out a financial product….
The problem they have is…
Thousands of data sources, scattered everywhere.
Building & Deploying AI models that balance credit worthiness with risk of default.
Explaining how AI models work. Often to regulators.
So, how does modernize relate to the AI ladder? The ladder is everything you need to connect your data and AI. But a modern AI Ladder, which is to say, a modern information architecture, makes the AI Ladder available wherever data is.
In most organizations, data is spread across a variety of environments: public clouds (with many providers), private clouds, and traditional on-premises deployments. Hybrid, multicloud platforms address this data proliferation challenge that many organizations are facing -- information distributed across multiple silos, databases, and clouds. If an organization is deploying and running AI projects in these environments, they are forced to use the AI tools from those specific cloud providers. This represents modern day “vendor lock-in,” which can stifle innovation and prevent businesses from scaling AI efforts. Hybrid platforms provide the foundation necessary to support all the capabilities a business would need to build, deploy, and manage AI models at scale.
Let’s assume the role of an system admin. She needs a single Data & AI stack deployed on multiple clouds. Let’s see how one works…
Collect is all about making the data being simple and accessible. What does that mean? It means collecting data regardless of where it lives, and what type – whether it be data from sensors, bank statements, traffic cams, etc. – and making it accessible throughout the organization. For too long data has been held captive, and as businesses continue to evolve, their data continues to build and grow in different forms. In order to embrace and start doing AI, you need to be able to access your most valuable commodity, which is data.
They need a way to access the hundreds of data sources in one place.
Now, let’s assume the role of a data engineer. He consistently access, combine, and deliver data to whoever needs it.
As you're working your way up the AI Ladder, once you're able to collect your data and make it accessible throughout your business, the next step is to make sure that you have it organized in a trusted and business ready way. Many business businesses underestimate the pitfalls that come from poor data governance. Your data needs to be organized, catalog and governed, not only to ensure that people who shouldn't access it can access it, but more importantly so that people who need to have access to it, can get access to it.
In fact, one of the things that is often overlooked is -- if you use governance correctly, governance can move from being a roadblock into being an enabler. And this is really important as we start thinking about getting value from the data that you have to govern for regulatory privacy or just corporate policies.
Let’s take the role of a data scientist, who needs to understand the structure and dynamics of data used to train AI models.
After an organization has been able to collect its data and organize it in a trusted, unified view, it can now tap into that data to build and scale AI models across the business. This allows companies to glean insights from all of their data, no matter where it resides, and engage with AI to transform their business—putting themselves at a clear competitive advantage.
To build AI models from the ground up and scale them across the business, organizations need capabilities covering the full AI life cycle. This includes how to prep, build, run, and manage AI.
The build phase is the most critical because you have to use the right algorithm to build a model that scores well. And this is where Watson Studio can help because it has features like AutoAI where you just feed the data and it can create model for you.
Then, once you deploy the model, and it’s running, the question becomes: how do you scale it? How do you make sure you have the management set so you know who changed the model, when model was deployed, and the lineage on the model. Meaning -- once the model is deployed, how do you make sure you know it's not biased? How do you make it explainable and transparent? All those things are very critical.
Now a data scientist needs to interactively explore data and build AI models.
When we talk about infuse, we’re talking about how clients are able to operationalize AI throughout their business. What do we mean by that? Well, businesses need to be able to put AI to work across multiple departments and within various processes - drawing on predictions, automation, and optimization.
Most organizations are still determining how and where to use AI to transform their business. However, we know interest is growing, because AI has the power to unlock the value of data in totally new ways.
AI is helping organizations predict and shape future outcomes, optimize people to do higher value work, automate decisions, processes, experiences, and reimagine new business models. Let’s talk about how one of our clients is putting AI to work.
So, taking the role of a risk analyst, he needs to operationalized AI models that are monitored, explainable, and trusted.
To recap, what did we just see IBM Cloud Pak for Data solve for? Well, multiple things. Now, the German Bank can:
Use a common Data and AI stack Anywhere they need.
Access hundreds of data sources as one
Offer governed Self-service
Quickly explore data and use “AI to build AI” models
Explain AI predictions including to regulators
BM Cloud™ Paks are enterprise-ready, containerized software solutions that give clients an open, faster and more secure way to move core business applications to any cloud. Each IBM Cloud Pak™ includes containerized IBM middleware and common software services for development and management, on top of a common integration layer — designed to reduce development time by up to 84 percent and operational expenses by up to 75 percent.* IBM Cloud Paks run wherever Red Hat® OpenShift® runs and are optimized for productivity and performance on Red Hat OpenShift on IBM Cloud.
We like to say that Red Hat Enterprise Linux (RHEL) is the one thing that ends up being the same. The layers above and below it, the applications running on it or the hardware it is running on, can be variable, but how you interact, manage, and maintain the operating system is the same. The same can be said for the Red Hat OpenShift Container Platform (often referred to as just OpenShift, or OCP)
This characteristic is crucial to the build and manage stages of digital transformation. Being the same is being predictive, meaning developers can leverage their skills regardless of the deployment model.
