In medicine - an MRI can quickly reveal a hidden ailment and actionable insight to get better. For IT and business leaders whose key concern with the mainframe is the platform costs and lean operations - the CA Mainframe Resource Intelligene reveals multiple sources of hidden mainframe costs and operational inefficiencies along with actionable recommendations. This is the only offering in the market that combines economic consulting services with proprietary utilities and automation technologies. View this SlideShare to understand the solution – how services, best practices and mainframe expertise of 40+ years from CA comes together to solve the CIO and CFO’s biggest challenge.
Call your account director or mainframe specialist.: https://www.ca.com/us/contact/mainframe-economic-consultant.html
2. CIO desired outcomes:
Cost Imperative: Must Save Now to Invest in the Modernization Journey
2
What are the barriers to
building a modern software
factory including Mainframe to
build and deliver mission critical
services at the same speed as
my cloud services?
What is the state of my
systems and skills to provide
99.999 availability and
redundancy?
How can I build
Digital Trust to enable
us to be a Digital
Enterprise?
How secure is our data and
how compliant are we with
GDPR and other regulations?
What resources can I free up to
self-fund digital initiatives?
Economics
3. 3
Economics
PROCESS MRI
AGILITY/SPEED
TOOLCHAIN
ECONOMICS MRI
CAPACITY COST
SOFTWARE COSTS
LABOR COSTS
SYSTEMS MRI
SLA’S
SKILLS
AUTOMATION
Start with Mainframe Resource Intelligence (MRI) to
assess your current state in the modern software factory
Economics
SECURITY MRI
PRIVACY
ACCESS
4. 4
Apply Best Practices with Mainframe Resource Intelligence
Optimization to Modernization
AI DRIVEN SELF DRIVING DATACENTER
AGILE DEVOPS
TOOLCHAIN
ECONOMICS MRI
DATA & USER CENTRIC
SECURITY
OPTIMIZE PLATFORM
PROCESS MRI SECURITY MRISYSTEMS MRI
• Dynamically
manage capacity
• Leverage Specialty
Engine and Java
• Software
Standardization
• Modern UX and
Visual analytics
• Leverage
experts to train
AI/ML
algorithms
• Augment people
with machine
Intelligence
• Automatic
remediation to
create self driving
datacenter
• Adopt open modern
tools familiar to
next gen
developers
• Automate to shift
“ops” left to enable
continuous delivery
• Provide everything
as a service
• Discover and
protect sensitive
data
• Implement multi-
factor
authentication
• Reduce risk by
Trusted user
management
5. An Economic MRI is the first step
Seems Simple but it’s not..
5
How can you
simplify what
you have..
..and
continuously
Iterate and
Optimize to
reduce both
CAPEX and
OPEX?
FTE
CAPACITY
CURRENT DESIRED
SOFTWARE
TOOLS
ECONOMICS MRI
7. Mainframe Economics Concern
Skills
~ 25% of spend
Situation
• Retiring specialists leading to skill gaps
• Tools under-utilization
• Higher probability of outage or degradation
• Inadvertent redundant purchases
• Lack of awareness of upgrades and
specialty engine exploitation
Source:https://diginomica.com/2017/07/20/mainframe-still-matters-skills-crisis-attached/
7
ECONOMICS MRI
8. Vendor 1
• Xxxxx
• Xxxxx
• xxxxx
Vendor 2
• Xxxxx
• Xxxxx
• xxxxx
Vendor 3
• Xxxxx
• Xxxxx
• xxxxx
Mainframe Economics Concern –
Tool Proliferation
8
~40% of Mainframe spend
Situation
• Redundant Capabilities
• Higher vendor management burden
• Higher maintenance cost
• Different skills to maintain for different tools
• Need to modernize – same toolset across
mainframe and rest of your datacenter
ECONOMICS MRI
9. 9
Solution? Here are the Best Practices to Optimize
the platform
o Optimize capacity
o Leverage Specialty
Engines and Java
o Pursue software
standardization
ECONOMICS MRI
11. 3 Ways to Optimize and Reduce Costs
11
Deploy tools to the fullest
with feature & zIIP
exploitation, upgrade
analysis, best practices
and configuration
consultation
Perform capping analysis
and select the right
workload candidates for
tuning, shifting and load
balancing to save MLC
Discover redundant
capabilities and
systematically analyze
consolidation and
modernization to reduce
license and skills costs
Optimize Capacity
Leverage specialty
processors and Java
Standardize Software
ECONOMICS MRI
12. 12
A Economics MRI from CA
based on
Across 500+
Engagements
12
• 8-10% baseline savings against total MLC
• Up to $200K savings per LPAR
• 55% - 65% offload of workload to specialty processors
saves MLC
• 24% tool reduction due to removal of redundancy
• Same development tools for distributed and Mainframe
• Lower FTE costs due to common skills
Optimize Capacity
Standardize Software
Leverage Specialty Engines & Java
ECONOMICS MRI
13. Economics MRI Engagement Model
Services, Best Practices & Technology Combined for Predictable Delivery
13
Discovery and Data
Collection
Cost analytics using
proprietary utilities and data
automation
CA delivers findings Client decision
Portfolio data DNA
Scenarios of potential cost
savings
implement findings
SMF 70, 72 X-Ray Ease vs. impact analysis
Use of CA services &
onversion utilities
Product inventory
In-built health-checks and
metrics
Estimation of customer effort
Adopt new capabilities to
realize business case goals.
Business case objectives
ANALYZE ASSESS RECOMMEND FOLLOW
2 weeks! Fast recommendation powered by analytics and automation
14. Economics MRI Engagement Model
Automation and utilities minimizes your effort
14
• Tailor your own engagement: Pick one one, 2 or all three reviews
• Least disruption to your team – CA services professionals guide the way.
ANALYZE
CA tools with Customer provided data Customer Level of Effort
Optimize
capacity
“X-Ray” or Dynamic Capacity analyzer runs a
90-Day extract of SMF 70, 72
Low to moderate
Leverage
specialty
engines
Best practices and product health-checks with
metrics & usage data
Moderate
Standardize
software
DNA automated discovery of customer installed
portfolio
Low
APPROACH
16. 16
Automated Capacity Mgmt.: Depicts all LPAR usage, but most expensive LPAR had the highest usage
Optimize Capacity
17. 17
Optimize Capacity: Identify peak usage
Automated Capacity Mgmt.: Easily view peak real-time rolling four-hour (R4HA), Caps and MLC
18. 18
Optimize Capacity – Pricing scenarios
Pricing Optimization View: Show scenarios of
Advanced Workload License Chart usage vs. Country Multiplex Pricing
19. Optimize Capacity
Recommendations include
Peak usage vs. Peak cost LPARs
Optimal Pricing model (e.g. CMP)
Time shifting – moving batches to night
White-space usage potential
Risk scenarios
ECONOMICS MRI
20. CA’s Dynamic Capacity
Intelligence is a win –win for
us. It provides automated
and predictable capacity
management so we can
optimize system resources
to the most critical business
needs
CHALLENGE:
Needed to monitor thresholds that were relevant for pricing predictions in real-time.
Take timely and appropriate actions against unplanned peaks in usage and costs
to maximize ROI.
German Insurance Company
German insurance giant needed to reduce cost in their data center
20
10% software
cost reduction
Across
mainframe
operations
Reduced
manpower
involved in
capacity
management
Prioritized
MSU
capacity
based on
workload
priorities
across LPAR
The company in this case study has policies against publicly endorsing vendors and prefers to remain anonymous.
“
21. 21
Leverage specialty engines and Linux
Assessment of products and
capabilities to leverage
specialty processors
Cost avoidance or MIPs
savings estimations
Product best practices
Configuration
Tuning
Installation and upgrade
guidance
22. CHALLENGE:
Reduce Mainframe HW and SW licensing cost while maintaining “5 9’s of
availability. Key delivery applications run on CA’s highly resilient CA Datacom
database technologies.
DHL
Is a global logistics company specializing in packaging, courier and express
delivery with a network of operations spanning 220 countries and territories.
22
CA provides mobile-to-
mainframe visibility and
machine learning
intelligence for a better
customer experience
55% of
workloads
offloaded to
specialty
engines
Higher
throughput at
reduced
licensing fees
Lower TCO
The company in this case study has policies against publicly endorsing vendors and prefers to remain anonymous.
“
24. CA’s approach is way
ahead of other
intelligence engines
which aren’t real time
CHALLENGE:
Multiple tools, multiple vendors in their mainframe environment built over years
resulted in redundant capabilities, costly shelf-ware costs as well as unnecessary
licenses & maintenance costs
Leading Insurance Company
An insurance giant serving 90% of the Fortune Global 500 needed to leverage
their valuable mainframe data and support a variety of new business initiatives
24
45% product
reduction
14% vendor
reduction
50% Software
License cost
savings
Conversion
duration:
4 months
from start to
production
The company in this case study has policies against publicly endorsing vendors and prefers to remain anonymous.
“
25. Tell your CA Account Director
Tell us about your concerns and overall
business challenge
CONTACT US!
Provide a list of your vendors, tools, & current
capacity status
Jointly determine the type of review for maximum
near term benefit
PARTNERING FOR SUCCESS
Get your saving assessment from CA
Determine next steps for implementation
Feel confident that CA’s there for you
today and tomorrow
CELEBRATE
Notas del editor
Find the low-hanging opportunities first and then tackle the more complex transformations
Identify and eliminate redundant tools and shelf-ware
Establish culture of proactive planning – avoid last minute surprises that lead to capacity buys
Look for ways to increase productivity – by tapping new capabilities, automation
Introduce modern tooling to avoid reduce reliance on specialized skills
Find the low-hanging opportunities first and then tackle the more complex transformations
Identify and eliminate redundant tools and shelf-ware
Establish culture of proactive planning – avoid last minute surprises that lead to capacity buys
Look for ways to increase productivity – by tapping new capabilities, automation
Introduce modern tooling to avoid reduce reliance on specialized skills
Find the low-hanging opportunities first and then tackle the more complex transformations
Identify and eliminate redundant tools and shelf-ware
Establish culture of proactive planning – avoid last minute surprises that lead to capacity buys
Look for ways to increase productivity – by tapping new capabilities, automation
Introduce modern tooling to avoid reduce reliance on specialized skills
Find the low-hanging opportunities first and then tackle the more complex transformations
Identify and eliminate redundant tools and shelf-ware
Establish culture of proactive planning – avoid last minute surprises that lead to capacity buys
Look for ways to increase productivity – by tapping new capabilities, automation
Introduce modern tooling to avoid reduce reliance on specialized skills
Newest capability in our cost optimization portfolio.
The company in this case study has policies against publicly endorsing vendors and prefers to remain anonymous.
Protect data privacy
Zions Bank – CAW Presentation
https://www.youtube.com/watch?v=mr9wpgjICGY&list=PLO7SodxCJyn6OXtHI1kPoLkKmfDKIxDdt&index=81
CA Data Content Discovery Customer Use Case
CA Data Content Discovery scans the mainframe data infrastructure to identify the location of sensitive data, and classifies the data based on sensitivity level so the appropriate business decisions can be made to secure, encrypt, archive or delete the data identified.
One of the first customers for CA DCD was a large Western US National Bank who originally purchased the solution to discover Payment Card Industry data (PCI DSS) on their mainframe – which as a bank has a lot of. They needed to meet audit and compliance requirements quickly, protect their customers highly sensitive data, and keep their operations running smoothly.
In their first 100 scans, the customer surprisingly found that over 5% of their scanned datasets contained Payment Card Industry data and Personally Identifiable Information in places not expected. And once the bank knew the location of the PCI and PII data in their mainframe infrastructure, they were able to take the second action and secure it appropriately.
With the initial success of the solution, the bank has since found a variety of other search uses for the tool:
The customer is executing DCD in development and scanning production data to have proactive insights sooner
They are currently running scans against user datasets
The customer is leveraging customer classifiers with scan info that is customized to their business to find PCI/PII data
The customer is automating the scans by setting up jobs in batch and scheduling them, to combine effective security and business agility
Increased risk assessment through automated non-manual efforts
Improved business agility by automating and scheduling scans
Complete flexibility by customizing classifiers
Additional Data Content Discovery Customers:
EOY FY17 – 34 customers licensed, 1 installed
Additional DCD customers (yet to install)
Broadridge
Morgan Stanley
Department of Justice
Department of Education
Metlife
Licensed EMEA DCD customers (yet to install)
AXA
DWP Bank
Lufthansa Airplus & Technik
Raiffeisen e-force GmbH
The company in this case study has policies against publicly endorsing vendors and prefers to remain anonymous.
Protect data privacy
Zions Bank – CAW Presentation
https://www.youtube.com/watch?v=mr9wpgjICGY&list=PLO7SodxCJyn6OXtHI1kPoLkKmfDKIxDdt&index=81
CA Data Content Discovery Customer Use Case
CA Data Content Discovery scans the mainframe data infrastructure to identify the location of sensitive data, and classifies the data based on sensitivity level so the appropriate business decisions can be made to secure, encrypt, archive or delete the data identified.
One of the first customers for CA DCD was a large Western US National Bank who originally purchased the solution to discover Payment Card Industry data (PCI DSS) on their mainframe – which as a bank has a lot of. They needed to meet audit and compliance requirements quickly, protect their customers highly sensitive data, and keep their operations running smoothly.
In their first 100 scans, the customer surprisingly found that over 5% of their scanned datasets contained Payment Card Industry data and Personally Identifiable Information in places not expected. And once the bank knew the location of the PCI and PII data in their mainframe infrastructure, they were able to take the second action and secure it appropriately.
With the initial success of the solution, the bank has since found a variety of other search uses for the tool:
The customer is executing DCD in development and scanning production data to have proactive insights sooner
They are currently running scans against user datasets
The customer is leveraging customer classifiers with scan info that is customized to their business to find PCI/PII data
The customer is automating the scans by setting up jobs in batch and scheduling them, to combine effective security and business agility
Increased risk assessment through automated non-manual efforts
Improved business agility by automating and scheduling scans
Complete flexibility by customizing classifiers
Additional Data Content Discovery Customers:
EOY FY17 – 34 customers licensed, 1 installed
Additional DCD customers (yet to install)
Broadridge
Morgan Stanley
Department of Justice
Department of Education
Metlife
Licensed EMEA DCD customers (yet to install)
AXA
DWP Bank
Lufthansa Airplus & Technik
Raiffeisen e-force GmbH
The company in this case study has policies against publicly endorsing vendors and prefers to remain anonymous.
Protect data privacy
Zions Bank – CAW Presentation
https://www.youtube.com/watch?v=mr9wpgjICGY&list=PLO7SodxCJyn6OXtHI1kPoLkKmfDKIxDdt&index=81
CA Data Content Discovery Customer Use Case
CA Data Content Discovery scans the mainframe data infrastructure to identify the location of sensitive data, and classifies the data based on sensitivity level so the appropriate business decisions can be made to secure, encrypt, archive or delete the data identified.
One of the first customers for CA DCD was a large Western US National Bank who originally purchased the solution to discover Payment Card Industry data (PCI DSS) on their mainframe – which as a bank has a lot of. They needed to meet audit and compliance requirements quickly, protect their customers highly sensitive data, and keep their operations running smoothly.
In their first 100 scans, the customer surprisingly found that over 5% of their scanned datasets contained Payment Card Industry data and Personally Identifiable Information in places not expected. And once the bank knew the location of the PCI and PII data in their mainframe infrastructure, they were able to take the second action and secure it appropriately.
With the initial success of the solution, the bank has since found a variety of other search uses for the tool:
The customer is executing DCD in development and scanning production data to have proactive insights sooner
They are currently running scans against user datasets
The customer is leveraging customer classifiers with scan info that is customized to their business to find PCI/PII data
The customer is automating the scans by setting up jobs in batch and scheduling them, to combine effective security and business agility
Increased risk assessment through automated non-manual efforts
Improved business agility by automating and scheduling scans
Complete flexibility by customizing classifiers
Additional Data Content Discovery Customers:
EOY FY17 – 34 customers licensed, 1 installed
Additional DCD customers (yet to install)
Broadridge
Morgan Stanley
Department of Justice
Department of Education
Metlife
Licensed EMEA DCD customers (yet to install)
AXA
DWP Bank
Lufthansa Airplus & Technik
Raiffeisen e-force GmbH