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

info@rsd.com for more information.