Analytics: Key to Mainframe Modernization
January 2018
In this issue
Analytics: Key to Mainframe Modernization 3
Research from Gartner: Spend a Little to Save a Lot: Using Analytics to Support Cost Optimization of IT and the Business 8
About RSD 16
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“With the business oriented dashboard,
RSD z/Trim Operation Analytics provides a
synthetic overview of the Mainframe resource
consumption that can be accessed in one
click. As most of the processing is done on
a distributed platform, dashboards can be
updated at any time of the day. This solution
will help us save more than 30% of the
costs of our legacy Mainframe performance
analysis solution.”
z/Trim Operation Analytics early adopter – Banking sector
Analytics: Key to Mainframe Modernization
How to make the best business decisions to
increase efficiency and modernize the Mainframe?
Many Business Leaders and IT Decision Makers
ask this question almost daily. Consider
implementing Analytics to make informed strategy
and business decisions about the Mainframe.
Key Challenges
■ An Analytics view of Mainframe data
is overlooked leaving Decision Makers
uninformed.
■ Lack of visibility and understanding of
Mainframe usage data leads to poor strategic
business decisions.
■ Companies seek to optimize IT operations
environments, including Mainframe.
■ Pressures created by increased scarcity of
skilled support resources for the Mainframe.
Recommendations
■ Measure Mainframe performance using
standard Analytics methods to identify
opportunities for modernization.
■ Consider a solution that is easy to use and
yields better analysis of any application
running on the Mainframe.
■ Use Analytics to deliver clear and measurable
value with fact-based decision making about
Mainframe environments.
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Part 1: Analytics increase the business value of MainframesThis story is about servers in a data center.
Although similar in scope, it is not about a server
farm with hundreds of thousands of square feet of
racks. It is a smaller, yet highly secure data center
because at its core is a strategic platform that
deserves a closer look.
It’s a difference inside that is processing, managing
and securing almost 70% of critical company data
(worldwide average) – and it is called Mainframe.
“Who Says Elephants
Can’t Dance?”
Louis V. Gerstner, Jr., chairman and CEO of
IBM from April 1993 until March 2002.
Security the core of Mainframes
It is known that Mainframe systems deliver the
best security model available, and by definition is
the father of virtualization. All compelling reasons
to respect such a powerful tool. Still, most IT and
Figure 1: Analytics as a key enabler for mainframe cost optimization
Source: RSD
business professionals have little knowledge on how
to harness and control this powerful tool.
Usually viewed as a rock-solid infrastructure
synonymous with heavy machines, the perception
is: Mainframe is a static environment. Reality
paints a different view, and to paraphrase a famous
“mainframer” … these elephants can dance!
Increase business value with a flexible system
In truth, what really matters in IT Operations is not
the perception, but what is delivered. Managing the
infrastructure and making it agile are key aspects
of efficiency and ultimately of value brought to the
business. The good news that comes with Mainframe
infrastructure: not only is it powerful and secure, but
it can be a highly flexible tool … IF one knows how to
make it dance.
Make it happen using Mainframe Business Intelligence
The mission at RSD is to help decision makers make
a change – even those not familiar with IT Operations
or Mainframe systems:
■ Help them understand how to increase the
efficiency of their Mainframe system;
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■ Demonstrate the function of business
intelligence and analytics for Mainframe to
leverage efficiency for critical company data
processing;
■ Show them how to make this “elephant” dance!
Part 2: A recipe for business problems: not using Analytics Companies deciding to not take any action risk
continuing the following behaviors:
1. Ignorance:
A recipe for catastrophe! In business, some of the
worst problems are those identified too late.
■ Why wait for a business interruption to realize
that back-up policies were not tested?
■ Delay a pending investment decision until a
storage capacity issue stops operations?
■ Stop or reduce Mainframe budget allocations
under the assumption that nothing can be done
to cut the related costs?
The truth is that management is informed of the
risk, but has not listened because the requests were
poorly formulated!
2. Lack of Understanding:
How can an organization increase understanding so
that management and business stakeholders listen
and take appropriate action?
When it comes to mainframe operations this starts
with sharing some vocabulary and acronyms
to promote understanding among business
stakeholders:
✔ MiPS & MSU:
Mainframe CPU consumed is expressed in MIPS
(Millions of instructions per second), the annual
mainframe cost per installed MIPS is declining from
$3,678 in 2013 to $2,700 in 2016.
Mainframe capacity is designated MSU (Million
service units); on the most recent Mainframe
machine (for example the latest z14), 1 MSU equals
around 8 MIPS, so a bit more than $20,000 per year.
These are critical components for all stakeholders to
understand. Until then, it will be impossible to argue
and picture any comparison with other platforms.
✔ MLC:
The MLC (Monthly License Charges) is a source of
much frustration. The primary objective here is not
necessarily to lower the cost, but to understand what
contributes to MLC.
Every stakeholder needs to understand the costs
and the options! If the components of MLC are
not understood, along with alternative choices, the
organization will struggle with efficient resource
allocation and cost. This results in the lack of
effective business controls.
✔ R4H:
This is the key to understanding Mainframe
invoicing challenges. The R4H may be the trigger for
opportunities: both in terms of resource allocation, and
how best to impact your monthly charges (see figure 2).
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3. No Disclosure:
The last risk, but not the least, for the business
stakeholders is the absence of easy-to-use and easy-
to-understand data about the Mainframe environment.
Graphic tools that are familiar to all business
stakeholders can be invaluable. Tools with simple
analytics that compare options and can present
various scenarios. Functionality that usually requires a
lot of time, energy and analysis and who has the time
to do that?
Part 3: Mainframe Modernization with z/TrimLeveraging 40 years of Mainframe expertise, RSD
partnered with IT Operations Users and Mainframe
Performance Analysis experts to develop z/Trim.
This Mainframe Performance Analysis solution is
available on Premise or as a Software-as-a-Service.
Visit www.rsd.com to see a demonstration
of z/Trim.
Figure 2: z/Trim – Simple analysis of the R4H by application
The red and orange boxes show the contribution of non-critical applications and identify an opportunity to reduce the R4H and decrease the mainframe costs. Source: RSD
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Beyond the technical challenges and the
commitment to the technology, the solution focuses
on two factors:
1. An easy to use solution at the right price
Develop a tool that provides a good understanding
of Mainframe resource allocation to both IT
managers, and non-mainframe professionals. Better
understanding prompts better decisions.
Key elements:
■ An intuitive user interface (see Figure 3)
■ A compelling pricing model to encourage
adoption – the SaaS solution.
2. Analytics for Mainframe Modernization
This is about analytics! Indeed, in this regard,
the Mainframe is no different from other types of
infrastructure. Therefore, metrics are important for
fact based decisions. The overall objective is to allow IT
stakeholders to simulate, project, and ultimately predict
which option best aligns with the company direction.
Contact your market analyst to get an
independent opinion on how best to leverage
Analytics and simplify your mainframe
modernization.
Figure 3: z/Trim – One click estimation of the contribution of any applications to the R4H
* Leveraging z/Trim analytics demonstrates a clear ROI: companies can save 5% of the Mainframe budget optimizing output management solutions. Source: RSD
Source: RSD
Spend a Little to Save a Lot: Using Analytics to Support Cost Optimization of IT and the Business
Cost optimization programs across IT and
business units can be significantly enhanced by
the use of analytics to compare options, support
decisions and predict outcomes. Data and
analytics leaders can use this research to identify
opportunities to deliver business value.
Key Challenges
■ Only 15% of Gartner-reviewed data and
analytics strategies contain concrete metrics
of success, despite the trend in business
being to demand tangible measures of
success from data and analytics initiatives.
■ Analytics teams often overlook important
opportunities within IT and business
initiatives where fact-based decision making
can deliver clear, measurable value.
■ Leaders often made suboptimal, value-
destroying or bad decisions that ended up
with across-the-board budget cuts, because
their cost optimization programs lacked an
analytical fact base.
■ Analytics teams are often not at the table
for cost optimization meetings, so their
opportunity to guide better prioritization is
lost.
Recommendations
Data and analytics leaders looking to optimize
analytics and business intelligence (BI) strategies
should:
Research from Gartner
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■ Demonstrate value from analytics by joining
in with cost optimization initiatives. This value
could appear in the form of reduced costs or
increased revenue from the right investments.
■ Help the relevant IT and business units (BUs)
capture cost data where needed, even though
this is not a usual activity for analytics teams.
This enables the use of analytics to demonstrate
the benefits of understanding your business cost
profile.
■ Although creating, maintaining and allocating
costs to a cost model is not trivial, once the
data exists, cost optimization for analytics is
just analytics as usual — follow the typical best
practices for analytics programs.
■ Tie into existing programs or capabilities for
continuous improvement, business process
re-engineering, lean/six sigma, operational
excellence, benefits realization and so on to
find the right starting point to drive the use of
analytics for cost optimization internally.
■ Use analytics to monitor performance and
results for cost optimization efforts.
Introduction
Underpinning cost optimization programs with
analytics results in a larger investment business case
for long-term cost reduction. Analytics reduce the
temptation to apply across-the-board budget cuts
and short-term vendor renegotiations, and instead
helps stakeholders to understand and communicate
how to make better cost optimization decisions.
The whole of the analytics continuum can support
cost optimization. Figure 1 shows the mapping
between Gartner’s analytics continuum model, which
describes the nature of analytics processes, and
Gartner’s cost optimization framework.
Figure 1. Analytics as a Key Enabler for Cost Optimization
Source: Gartner (August 2017)
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Cost optimization is a business-focused, continuous
discipline to drive spending and cost reduction,
while maximizing business value. It requires the
calibration of conflicting constraints as the need to
drive costs out is balanced with making the right
investments to drive technological advancement.
For more information on cost optimization, see the
Gartner Key Initiative “Managing Cost Optimization.”
Many cost optimization programs start in IT
departments, for a number of reasons. IT
departments are methodology-driven and also have
frequent needs to fund innovation, which they do
by cutting existing costs. They are often fighting
for budget, trying to defend their value and arguing
against budget cuts in the form of outsourcing or
other initiatives.
However, when fully considering the cost of IT, it
has to be considered in total, with all relevant costs
— direct and indirect — owned and consumed in
line with the business processes that it supports.
More-advanced cost optimization programs consider
business cost optimization. The key challenge for
these programs is to gain enough data around how
costs are allocated between different technologies
and applications (marketing, sales support,
customer service, inventory management, finance,
R&D, HR and production, for example) and then to
business functions.
Digital business requires the creation of digital
products and services, or the addition of digital
capabilities to existing products. Digital business
requires fundamental changes to the way the
enterprise operates, from how an enterprise invests
in digital technologies, to how it measures the
impact with new key performance indicators, to
how connected ecosystems of people, business
and things will create value. Everything will be in
play, from business strategy and the industry the
enterprise competes in, to partnerships, ecosystems
and technology platforms.
In order to invest in the digital future while
still maintaining existing systems, mature cost
optimization programs will be essential. Predicting
and monitoring the effectiveness of investments
will be critical as digital transformation programs
advance. Effective cost optimization requires
calibrating conflicting constraints. As a result,
analytics become a powerful tool to enable and
support both cost controls and business value
(see Figure 1). With analytics to support informed
decision making, business leaders can compare
options, predict outcomes and confidently justify
their decisions.
Analysis
See Cost Optimization as an Opportunity for Analytics and BI Teams to Deliver Measured and Quantifiable Value
Many analytics teams are tasked with finding
opportunities to deliver value within their
organization, because BUs and IT departments lack
the expertise, vision and time to think about how
better data for decision making could serve them.
Cost optimization programs are widely established,
and there is considerable documented practice
around how they should be planned and executed.
Before now, analytics teams have not usually been
involved in cost optimization programs, mainly
because cost optimization programs often lack
sufficient data, while analytics teams tend to focus
on opportunities where data is readily available and
ideally of good quality.
However, there are a number of reasons why cost
optimization programs present a good opportunity
for analytics teams to deliver business value:
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■ Cost optimization programs are strongly focused
on making measurable improvement and
demonstrating value.
■ Visualization of cost data can be hugely valuable
for business and IT leaders, giving analytics an
opportunity to delight its users.
■ Strategic cost optimization programs provide the
opportunity to align with high-level initiatives that
may occur as follow-ons, and the involvement
of the analytics team will help promote broader
analytical maturity and a data-driven culture
across the organization.
Help the IT Department and/or Business Unit to Capture Cost Data to Support Analytics
Cost optimization has been overlooked by many
analytics teams, because they look for opportunities
where data is available. This is often not the case
for cost optimization programs, which start with
generating data via a number of methods, including
cost allocation. The first step is to ensure that costs
are correctly categorized and accounted for. Only
when budgeting and acquisition processes have
been improved to fully capture all aspects of costs
data — the purpose for which money was spent —
can costs be allocated correctly. The general ledger
rarely contains all the necessary data to support this
analysis to drive optimal spending.
Cost allocation is the process or method of
attributing IT costs to specific units of value —
services, applications, BUs, projects, asset classes,
technologies, products or investment profiles. One
major benefit of cost allocation is that it links IT
spending directly to BU activities based on usage,
consumption, access, capacity or some other metric
that apportions IT service costs. When stakeholders
see the percentages that they spend on different
systems, they often feel in control of the spending
discussion. In addition, it can motivate the BUs to
avoid special requests that do not contribute to their
bottom lines or lack a solid business case. Thus, the
internal customers of IT provide budget justification
via their willingness to pay for the services rendered,
and to balance the supply, demand and price for
services to optimize spending.
In addition, allocation of IT costs to BUs or projects
(sometimes referred to as chargeback) provides
the business with a cost base from which pricing
decisions can be made. For many end-customer
business products and services, IT costs can be
significant, and therefore, it needs to be included in
the price-setting decisions.
Many models of cost allocation are in use today (see
Figure 2). Selecting the proper model will impact
cost and accuracy, and is commonly a function of
internal politics, accuracy requirements and the
third-party tools used to automate the process.
Analytics to Support Cost Optimization Works Like Any Other Analytics Initiative — Follow Best Practices
Once the data is captured correctly, analytics to
support cost optimization work like any other
analytics initiative. The usual best practices for
analytics projects apply.
Below are Gartner’s recommendations for the data
and analytics leader’s first 100 days. They apply
here to the data and analytics leader undertaking a
cost-optimization-related analytics initiative:
■ Focus on a subset of the decisions made within
cost optimization programs that could benefit
most from analytic insight. Drive actions that
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Figure 2. Seven Common Methods of IT Cost Allocation (Chargeback)
Source: Gartner (August 2017)
deliver near-term improvements in a few key
areas.
■ Forge solid relationships with all key
stakeholders and sponsors, and set appropriate
expectations for commitments, funding,
business benefits and time scales.
■ Understand how success — your own, that
of the cost optimization program and that of
the organization — is defined, and how it can
be measured. Use this research to guide your
planning and delivery. Portfolio analysis typically
involves analyzing not only cost, but also risk
and value metrics.
■ Communicate the business value of analytics to
the cost optimization program, and engage your
stakeholders in regular, open communications.
■ Promote enterprisewide analytics adoption.
Support more decisions by evolving the analytics
scope beyond its cost optimization role. Identify
and target specific use cases.
■ Develop a roadmap for BI and analytics that
shows how you will build and develop the
maturity, scale, and scope of BI and analytics.
■ Build your own credibility, and raise the profile of
BI and analytics.
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■ Audit the technical landscape and analytics
usage, and identify hot spots or gaps that are
suitable “quick win” initiatives.
■ If an analytic center of excellence or BI
competency center exists, make sure the cost
optimization work aligns with it. Staff this with
a cross-functional BI and analytics team that
balances short-term tactical delivery with long-
term strategic infrastructure. This will ensure a
sustainable integration with the overall analytics
program.
Use Gartner’s Analytics Matrix for Cost Optimization to Understand Which Cost Optimization Activities Are Familiar and Which Are New
Gartner has created a matrix of analytics activities
for cost optimization, shown in Figure 3. This matrix
helps analytics teams understand the nature of the
important activities for cost optimization.
Easy — New
One cost optimization activity that is new to
analytics teams is the need to create cost data for
IT and/or business capabilities, and to add records
to this dataset on an ongoing basis. Analytics teams
typically target use cases where data is already
available, and are unused to this type of activity. This
data is best created in a spreadsheet or personal
database, which are tools analytics teams are usually
unwilling to use and unwilling to promote the use
of, because of their history with individualistic
information needs, rather than centralized,
consistent information that analytics and BI
teams want to provide. This reluctance should be
suspended, because the base cost data is essential
for the project, and personal tools are the best place
to build and store it in the early stages. Analytics
teams can provide value in supporting the creation
of this data, because understanding how data could
be used for decision making is a useful perspective
on what attributes the data could contain.
Figure 3. Gartner’s Analytics Matrix for Cost Optimization
Source: Gartner (August 2017)
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Difficult — New
Identifying the data sources and processes relevant
to IT consumption can be challenging and involves
investigation, extraction and reconciliation of data
from systems such as requisitions, IT asset and
system management, IT service management, and
project portfolio and program management tools.
Easy — Familiar
The cost optimization activity that is both easy and
familiar to analytics teams is to supply visualization
and analytics tools to show business and IT leaders
the information that underpins the cost optimization
program. The ability to explore and navigate through
the cost optimization information, and look at
different scenarios, can be hugely beneficial to the
perceived success of the cost optimization program
and adds value far in excess of the cost of buying
and implementing the tools. It is usually very easy
for these tools to ingest spreadsheet data, which
accelerates the time to value.
Difficult — Familiar
Some aspects of cost optimization programs
will be difficult yet familiar to analytics teams.
The politics of sharing information that could be
used to take difficult decisions like reducing and
increasing budgets is something that analytics teams
are generally accustomed to being involved with.
Finding insights into your business often leads to
uncomfortable or painful change.
Where the cost optimization program is dependent
on allocated cost data, analytics teams need to
support trust in the data, which is particularly
challenging for generated data that does not
come from a transactional system. Sharing and
communicating the methodology used to create the
data, driving open discussions of alternative ways to
allocate cost data, and communicating why certain
methods were and were not chosen are all parts
of this process. Often, it is impossible to please all
the people all the time, but disclosing why certain
methods were used can at least explain what people
are seeing in the data. For example, a user may
want to use service-based cost allocation, but if the
service units are not acquired or measured, then
a time-and-materials allocation may be faster and
easier to work with as a first approximation.
Some cost optimization requires complex technical
integration, because the cost optimization program is
focused on operational processes. An example is cloud
services, where enterprisewide consumption has to be
carefully monitored and mapped back to BUs.
Driving a culture of data-driven decision making
is the objective of many analytics teams. While
challenging and open-ended, this is a familiar issue.
Wider use of analytics, wider trust in data and more
value gained from supporting decisions with data are
all key ways to expand and improve this culture, and
cost optimization is a rich seam of opportunity.
Support Continuous Improvement in Cost Optimization
Even though cost optimization is now a continuous
activity in many IT organizations, many do not
know how to reduce their budgets and make the
savings stick, or have not evolved legacy frameworks
that have run out of steam. Analytics can support
the continuous nature of best practice in cost
optimization, because the process of defining and
visualizing the data makes the value of the program
clearer, and opportunities for ongoing improvement
become clearer.
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Case Study
Manufacturer’s IT Department Goes From Expensive Provider to Strategic Business Partner
A multibillion dollar U.S. manufacturer had
successfully created shared services to reduce costs.
However, the remainder of IT not part of shared
services, including all the operational technology,
remained an opportunity. Although the company was
embarking on a digital transformation journey, it had
no intention of increasing its IT spend. The new CIO
introduced cost optimization based on analytics,
with an initial six-month exercise to gather and
compare cost-related data using an artefact-based
approach.
As a manufacturing leader, the CIO considered his
role the same as any other leader in manufacturing
— to create capacity without adding the need for
extra capital — and took this approach to IT costs.
The company is structured in five service lines,
and each service line has its own IT budget. Each
line optimized the budget spend for its own needs,
with no thought to savings or technology usage
optimization across the whole company. The CIO’s
challenge was to discuss costs with business leaders
from a consumption perspective. Business leaders
had no interest in costs of servers, OS instances
or networking; they wanted to understand the
cost of consumption of particular IT services and
capabilities.
The business results from the cost optimization
program included:
■ Verifying to the business that the IT department
is cost-competitive compared with external
suppliers. Before the consumption-level data
was available, the business perceived the IT
department to be expensive compared with
external suppliers, due to rising IT costs.
The actual reason for the rising IT costs was
increasing business consumption, but the original
cost optimization data that focused on servers,
software instances and network capacity did not
tell the business about consumption levels.
■ Increase in the ratio of permanent employees
versus contract staff. The company’s irregular
revenue cycle encouraged them to keep staff
costs low, so they used contractors often for IT.
The improved data demonstrated that they could
afford to move to permanent hires without losing
scalability.
■ Rationalization of acquisitions. The company
had grown quickly during the credit crunch
period with a number of small acquisitions and
a few larger ones. Because of the individual
focus of each service line, the IT systems of
the acquisitions had not been rationalized,
but the detailed data showed the benefits of
rationalizing to the overall IT spend.
■ IT becoming a strategic partner to the business,
not just a tactical supplier. The IT department
gained great credibility with business leaders
due to the understanding of IT costs at the
consumption level that the cost optimization
system provided.
For more information, see Gartner Events on
Demand — “Consulting Demonstration Session:
Designing a Best-in-Class Cost Optimization
Strategy.”
Source: Gartner Research Note G00323780, Alys Woodward, Gareth Herschel, Neil Chandler, 02 August 2017
Mainframe Modernization leveraging Analytics is published by RSD. Editorial content supplied by RSD is independent of Gartner analysis. All Gartner research is used with Gartner’s permission, and was originally published as part of Gartner’s syndicated research service available to all entitled Gartner clients. © 2018 Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner’s endorsement of RSD’s products and/or strategies. Reproduction or distribution of this publication in any form without Gartner’s prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner’s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see “Guiding Principles on Independence and Objectivity” on its website.
About RSD
Headquartered in Geneva, with offices in the US and
in Asia, RSD develops and sells enterprise-grade
software solutions to help its Customers to make a
change in the way they use and manage their hybrid
IT environment.
Built upon 40 years of expertise, innovation and
the highest professional standards, RSD’s offerings
enable customers to optimize their IT resources
whether on mainframe or open systems and reduce
their operating costs thanks to a flexible and
breakthrough licensing model.
RSD has built a strong and loyal customer base
of Fortune 2000 companies with millions of users
worldwide. RSD offerings are available around the
globe – both directly and through business partners.
Please visit www.rsd.com or contact us at
[email protected] for more information.
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