7. fri 840 930 houston - workforce analytics for hr decisions

Deloitte’s Analytics Symposium 2010 Using advanced workforce analytics to make business-driven Human Resource decisions

Russell ClarkeJohn HoustonHoward Hamilton, ESPN

October 2010

Copyright © 2010 Deloitte Development LLC. All rights reserved.1 Using advanced workforce analytics to make business-driven Human Resource decisions

• What we are seeing in the marketplace

• Workforce analytics approaches

• Deploying point solutions: Solving

specific problems

• Workforce planning and optimization

• Recruiting and predicting performance

Agenda

What we are seeing in the marketplace


A great book about workforce analytics, not baseball


• In 1999, the Oakland A’s ranked 12th out of 14 in National League

payroll

– How could the A’s compete with richer teams to attract top players?

• The A’s manager (Billy Beane) decided to take an analytic approach

– He hired an analyst (Paul DePodesta) out of Harvard to try to predict player’s

future performance

– Example of DePodesta’s calculations

Runs Created = (Hits + Walks)*(Total Bases)/(At Bats + Walks)

• Beane used this model to hire excellent baseball players who had been

undervalued by the market

• The result?

– ―In 2006, the A's ranked 24th of 30 major league teams in player salaries, but

had the 5th-best regular-season record. This reflects a typical pattern

throughout Beane's stewardship.‖ — Wikipedia entry on Billy Beane.

The Moneyball story


Visibility into analytics can help business leaders make decisions more

accurately, objectively, and economically — a rapidly developing

consensus in business, education, law, medicine, and even professional

sports.

The importance of business analytics

http://images.google.com/imgres?imgurl=http://jeremylatham.com/images/freakonomics.jpg&imgrefurl=http://jeremylatham.com/blog/category/books/&h=600&w=406&sz=120&hl=en&start=1&um=1&tbnid=dkhU7A8y2Fr2zM:&tbnh=135&tbnw=91&prev=/images?q=freakonomics&svnum=100&um=1&hl=en&newwindow=1&safe=off&sa=N

http://cdn.harpercollins.com/harperimages/isbn/large/9/9780061353239.jpg


The importance of business analytics (cont.)

Analytics Description

Descriptive reporting Summarize and compare operational and/or financial data

on key workforce variables within defined time frames.

These are used primarily to create lagging indicators.

Retrospective analytics Analyze one or more internal data sources to discover

useful information. Used to create both lagging indicators,

performance benchmarks, and insights.

Predictive analytics Mathematical models are applied to multiple internal and

external data sources to predict future workforce events.

Used to create leading indicators and focus limited

resources on critical employee populations.


• Continued investment in technology infrastructure

– Enterprise Resource Planning (ERP) systems/data marts aggregate data

– HR process move to automated point solutions

• New type of HR leaders

– Come from finance and operations

– Common practice to use data and analytics for more effective business

decision making

• Challenging economy is forcing organizations to embrace change

– Visibility into business issues reduces risk for senior management teams

– Managing talent spend critical (one of the top three P&L line items)

• Dashboard overload

What’s driving interest/demand for analytics in HR?


Positive financial impacts from workforce analytics

Company Impact

Top-ten national bank Increased redeployment activities from 12% to 18%

saving the corporation $18M

Global services company Improved their recruiting yield (hiring ratio) without

adding additional headcount, driving a $5.6M savings

Major airline Reduced their Full Time Employee (FTE) headcount

within their Reduction In Force (RIF) services group by

50%, saving the company $600,000 annually

National financial services

company

Projected a cost savings of over $7M by reducing

voluntary turnover of key employees by 1%

Leading wireless company Accelerated their decision/selection process during a

large M&A, saving $5.7M for every 1,000 employees

separated

Workforce analytics approaches


1. Building an HR business analytics capability

2. Deploying point solutions: Solving specific problems

– Workforce planning and optimization

– Recruiting and predicting performance

Clients are following two paths


We utilize the Deloitte HR business analytics maturity model when we

work with our clients in implementing reporting and workforce analytics

solutions. These steps are demonstrated to help companies reduce their

risk, optimize their spend, and facilitate executing an integrated

approach:

• There needs to be a single source of the truth

• Tools and data need to be created across all work streams

• Help to ensure that data is consistent, timely, well defined, and careful

• Tools need to be tailored to the business needs and the knowledge and

capabilities of the user; not one size fits all

• Evolve adoption over time, as capabilities, skills, tools, and data improve

Where they are and where they want to be

As used in this document, ―Deloitte‖ means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see

www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries.


1. Tie reporting and analytics to business-driven issues.

2. One technology is not the answer. Rather, a component strategy is

the key to success.

3. Implement in phases.

Keys to implementing an effective analytics strategy


Objective: Build a highly-effective human capital business analytics

capability and organization that is scalable and sustainable.

Bu

sin

ess A

naly

tics

Phase I

Nonexistent

Phase II

Developing

Phase III

Defined

Phase IV

Advanced

Phase V

Leading

Business Analytics Capability

In addition to

periodical reports,

management utilizes

data to test

hypotheses and

improve the quality

of business

operations

Building workforce analytics capabilities and delivering benefits

Analytics are not

used in business

operations

Analytics are used

on an ad-hoc basis

to improve business

operations

The organization

produces periodical

reports which

management uses

in decision making

Sophisticated

predictive modeling

is used in scenario-

based analysis; data

points management

to opportunities for

improving operations

and mitigating risks


Key business analytics enablers

Enabler Key Questions

People What kind of organization do we need? How will we

design our organization to leverage current analytical

capabilities and understand what gaps we have?

Process What is the impact of analytics on how we do business?

Can we improve our decision-support process to more

effectively manage our ―People‖ supply chain?

Technology What solutions do I need, and when? How do I stitch

together the required technology components to enable

data-driven decision making?

Data How do I get the most out of my internal and external

data?

Security/

Governance

How are analytical decisions made? Who should be

accountable for facilitating the analysis and leveraging its

insights?


“People” maturity dimensions: Definitions

Leadership and

strategy

Sponsorship leaders provide for championing workforce business

analytics capability and the approach an organization takes towards

this goal

Talent attraction Ability to attract and recruit the talent that the service organization

needs

Competency

development

Skills and capabilities that should be demonstrated by the people to

meet the objectives of the organization

Organization

structure

Structure of the organization designed to deliver maximum value and

capability

Cross-functional

integration

Level of cross-functional interaction required to make informed and

business driven workforce decisions

Job design Identification and definition roles and jobs to achieve the service

organization’s objective

Culture Extent to which analytics driven business decision making is

embedded in day-to-day operations, customs, and behaviors

Example: Workforce business analytics maturity model: People


Leadership

and strategy

We have limited focus on developing

workforce business analytics

capability.

Our leadership is educated on the

importance of workforce business

analytics.

Our workforce business analytics

capability is an essential component

of our business strategy and

leadership focus.

Our workforce analytics

strategy and our business

strategy are seamlessly

integrated and directionally

consistent.

Our business strategy is informed and

influenced by our workforce insights and

predictions.

Talent

attraction

We rarely seek analytical skills in

future hires, only transaction-oriented

Information Technology (IT) skills.

We seek ―soft skills‖ in analytics and

transaction- oriented IT skills.

We seek candidates with strong

backgrounds and experience in

statistics and analytical decision

making.

For hiring talent, we focus on

cross-functional business

analytics and advanced

technological capabilities.

Candidates must have prior experience

in advanced analytical analysis and

relevant background in the subject.

Competency

development

We have limited focus on

development of analytical skills

through training or hand-on

experience.

Our employee training touches on a

high-level analytics for limited

functions.

We have training for designated

analysts for BI, Analytic

Applications, Data Management,

and new technology software.

We focus on cross- functional

business analytics and

advanced technological

capabilities.

We encourage associates to get

involved in analytics-driven

experimentations, and seek

opportunities to collaborate on business

analytics across functions.

Organization

structure

We currently do

not have any

workforce

analytics

capability or

service

organization.

We have some ad

hoc local

resources helping

us generate

generic workforce

reports without any

specific reporting/

organizational

structure.

We have a

dedicated group

of individuals and

a defined process

in place for

generating

standardized

workforce

insights.

We have

implemented an

organizational

structure (Shared

Service Center or a

Center of

Excellence) for the

right service

level/cost/capability.

Cross

functional

Integration

While taking workforce decisions, we

often do not interact with other parts

of the business.

We realize the importance of

interacting across functions to make

workforce decisions and are looking

for ways to improve it.

We have a well-defined process of

engaging business partners to

provide insight in interpreting data

and taking business decision.

We are seamlessly integrated with other

functions for generating business-driven

workforce analytics as a service to other

business functions .

Job designWe currently have not identified any

job or role responsibility for attending

to workforce analytics.

Job responsibilities, skills and

reporting structures are defined to

support workforce business

analytics as a core capability.

Culture

All of our workforce decisions are

based on hindsight and no analytic

applications.

The current decision-making culture is

primarily hindsight driven, but we want

to move to data-based decision

making.

We give high importance to

data/analytics driven decision

making and it is linked to

competitive differentiation.

There is initial adoption of data

mining and predictive modeling

tools. Analytics is the key to

sustainable competitive

advantage.

We emphasize on acceptance of

analytic applications and predictive

modeling by mainstream. Analytics and

analytics-driven insights drive strategy

development.

Example: Workforce business analytics maturity model: People (cont.)

Nonexistent Developing Defined Advanced Leading

“P

eo

ple

” m

atu

rity

dim

en

sio

ns


Bu

ild

cen

tralized

cap

ab

ilit

ies

“People” Maturity Dimensions: Definitions

Roadmap Development

It’s a journey and regardless of your strategy, most large organizations want to

create an internal capability that is scalable and sustainable. Understanding,

upfront, what are the required components and ―when‖ to invest will allow for an

efficient use of investment dollars.

Accelerating progress — Building a high-performing capability and organization

Phase Year 1 Year 2 Year 3

Model

Development

Develop New and Renewal

Underwriting Models for LOB 1


Underwriting Models for LOB 2 and

LOB 3

Recalibrate Models for LOB 1

Interim ScoringScore LOB 1 New and Renewal

Business Off-Site at Deloitte

Consulting

Technology

Integration

Develop and Deploy Scoring

Engine for LOB 1

Develop and Deploy Scoring

Engine for LOB 2 and LOB 3

Integrate LOB 1 Models into Policy

Administration Systems for New

and Renewal Business

Integrate LOB 2 and LOB 3 Models

into Policy Administration Systems

for New and Renewal Business

Communication

& Training

Develop Communications Training

and Conduct Pilots for LOB 1

Launch Communications Training

to all LOB 1 Underwriters

Develop Communications Training

and Conduct Pilots for LOB 2 and

LOB 3

Launch Communications Training

to all LOB 2 and LOB 3 Underwriters

Monitor, Assess, and Revise

Training as Necessary

Business

Integration


Business Rules, Pricing Rules, and

Reason Messages for LOB 1

Establish Performance Metrics for

LOB 1


Business Rules, Pricing Rules, and

Reason Messages for LOB 2 and

LOB 3

Establish Performance Metrics for

LOB 2 and LOB 3

Monitor, Assess, and Revise

Business Rules and Pricing Rules

as Necessary

Monitor Performance Metrics

Prioritized Opportunities

Low Cost/

Defined Service Level

Knowledge Transfer/

Management Involvement

Center of Expertise

• Expertise Focus — Ability to Leverage

• “Best Practice”Development

• Issue/Knowledge Intensive

• Organized by Region

Transaction Processing

• Consolidated Organization

• Operational Focus

• Standardized Services

• Process Intensive

• Could Cover Countries or Region

Business Partner

• Aligned with Function/ Unit

• Line/Management Focus

• Knowledge & Know-How Transfer

• Decision/Action Intensive

Site Support

• Distributed to Location(s) for Local Service Needs

• Required for Local Input/ Data Capture or Local Programs

• Manual or End-User Intensive

Method of adding value

Rela

tio

nsh

ip t

o t

he b

usin

ess

Focus on enhancing skills and governance and deploying

operationally

Focus on enhancing skills and governance

Focus on standardizing local delivery

Focus on segregating and optimizing efficiency

Sp

ecif

ic (

sit

e,

un

it,

reg

ion

)

Gen

eric

(co

mp

an

y-w

ide)

Service Delivery Model

Bu

sin

ess I

nte

llig

en

ce Phase I

Nonexistent

Phase II

Developing

Phase III

Defined

Phase IV

Advanced

Phase V

Leading

Business Analytics Capability

People Process Technology Data Security / Governance

What kind of

organization do we

need? How will we

design our organization

to leverage current

analytical capabilities

and understand what

gaps we have?

What is the impact of

analytics on how we do

business? Can we

improve our decision-

support process to more

ef fectively manage our

customers, employees,

distributors and

suppliers?

What solutions do I

need, and when? How

do I stitch together the

required technology

components to enable

data-driven decision

making?

How do I get the most

out of my internal and

external data?

How are analytical

decision made? Who

should be accountable

for facilitating the

analysis and leveraging

its insights?

Business Analytics Enablers

Analytics are not used

in business operations

Analytics are used on

an ad-hoc basis to

improve business

operations

The organization

produces periodical

reports which

management uses in

decision making

In addition to periodical

reports, management

utilizes data to test

hypotheses and

improves the quality of

business operations

Sophisticated predictive

modeling is used in

scenario based

analysis, data points

management to

opportunities for

improving operations

and mitigating risks

Capability Assessment

• Further independent

variable considerations

and design

• Modify target variables

• Modify independent

variables

• Develop Univariate

Reporting System

• Document Univariate

Analysis results

• Discuss and document

model validation

techniques

IT

Un

derw

riting

Mark

eti

ng

Operations

Advanced Analytics Enable Business Strategies for Success

Underwriting Excellence

• Improve pricing precision

• Increase objectivity throughout the

underwriting process

• Enhance risk selection and risk

avoidance capabilities

• Improve pricing competitiveness in

prof itable segments

• Improve underwriter negotiation

capabilities

Operational Efficiency

• Reduce transaction costs

• Straight through processing of select risk

segments

• Improve ease of doing business with

agents

• Improve claims management activities

• Improve customer service

Marketing and Retention

• Target the right risks for non-renewals

• Improve retention of prof itable risks

• Increase cross-sell opportunities

• Identify geographic and product

expansion opportunities

• Enhance recruiting of prof itable

producers

Enhanced Decision Making

• Increase f raud detection capabilities

• Improve monitoring of underwriting

performance

• Enhance ability to react to market forces

sooner

• Increase information processing

capabilities and data governance

Strategic Alignment

Roadmap

Deploying point solutions: Solving specific problems


Typically, a bottom-up approach creates a gap between data and business

problems by emphasizing the data, leading to a common complaint:

―We have a lot of data, but no useful information.‖

Traditional, bottom-up approach

Workforce data is often not

available. Business drivers

do not dictate what data is

collected and how.

Data sits in multiple

repositories. It is not

translated into a useful

business format and does

not correlate with critical

business drivers.

Relevant, real-time

workforce data needed to

make informed decisions.

Company’s data repository

GAP

Translating data into a

useful format

Having relevant data to inform

decision making

Market pressures

Profitability

Credit crunch

Customer demographic shifts

Technological change

Globalization

Cost containment

Growth

Shrinking workforce

Evolving workplace

Risk and regulatory compliance


A top-down approach can bridge the gap between data and business problems by

providing a repeatable framework for achieving resolutions.

Alternative, strategic approach

Workforce Solution Sets

Workforce

planning

and

optimization

Recruiting

Workforce

transition

Organization

design and

modeling

Training &

learning

Leadership

development

Retention

Workforce

productivity

Predetermined Workforce

Solution Sets incorporate

leading-practice lessons to

provide leaders with the

information they need to take

action.

Existing internal and external

data can be leveraged for

relevant workforce data to

populate the predetermined

Workforce Solution Sets.

A top-down approach

determines that business

drivers dictate which workforce

metrics are necessary to make

informed decisions.

Company’s data repository

Market pressures

Profitability

Credit crunch

Customer demographic shifts

Technological change

Globalization

Cost containment

Growth

Shrinking workforce

Evolving workplace

Risk and regulatory compliance

Using data

to enable

better

business

decision

making


Deloitte has worked with our clients to develop and deliver innovative analytical

solution sets that are tied directly to today’s pressing business workforce issues.

Using analytics to help solve the HR business issues

Descriptive reporting Retrospective analytics Predictive analytics

Solutions set Benefit

Workforce planning and

optimization

Increases accuracy of predicted revenue and talent demand by

incorporating valuable third-party data

Workforce transitions Allows for enhanced compliance and financial oversight through

centralized reporting

Recruitment analytics Confirms that every resume is reviewed and considered in the

recruitment process

Retention risk analytics Changes the paradigm to a proactive strategy that mitigates risk by

predicting the attrition problem among critical workforce

Leadership development

modeling

Provides an insight into the recognizable characteristics of those who

will thrive in leadership roles

Organization development

modeling

Simulates to-be structures to right size the organization with optimal

management layers and spans of control

Workforce planning and optimization


Workforce planning and optimization forecasts and visualizes the supply

and demand of individuals for critical roles and provides the foundation to

evaluate the actions needed to meet the corresponding talent

management objectives.

Workforce planning and optimization — Creating more business value

From lagging…

• Once a year, annual planning

• Macro-level planning

• Reactive organization

• Ad-hoc reporting

• Time-intensive and labor-intensive

• Historical view of data

• Internal data only (HR, finance,

operations, sales)

• Limited alignment with strategy

…to leading practices

• Continual monitoring and planning

• Micro-level planning at employee level

• Proactive organization

• Enterprise-level reporting

• Automated and real-time data visibility

• Forecasting and scenario planning

• Internal and external data with

macroeconomic insights

• Integration with business and

HR strategy


A broad approach to planning considers combinations of data from multiple sources:

Workforce planning and optimization process

Step 3: Scenario

planner

Allows for ―what if‖

planning

Step 4: Report and

monitor

Provide real-time data,

enterprise data to

leadership and

stakeholders

Accelerate workforce planning and optimization

Deloitte’s supply, demand, and scenario planning approach leverages

existing systems and data to help organizations make business decision

on talent solutions.

Step 2a: Supply

projector

Visualize the organization

down to the individual

level and calculate

―inflow‖ and ―outflow‖

trends (i.e., attrition, hires,

mobility)

Step 2b: Demand

projector

Incorporate

macroeconomic

data and drivers to

project workforce demand

for the organization

Industry specific

Finance

Operations

Macroeconomic

data

HR

Internal

benchmarks

Data sources

External

Labor market

Internal

Benchmarks

Sales pipeline

Step 1:

Data collection


Current approach

• Develop one annual plan

• Labor- and time-intensive process to develop plan

• Plan is closely monitored, but adjustments are infrequent because it requires

same investment of labor and time

Current approach to workforce planning

What is new in demand estimation?

Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb

FY start

Long

planning

process

FY plan

developed

Plan in action

1


Proposed approach

• Develop annual plan and monthly forecasts

• Internal and macroeconomic data incorporated into plan

• Prior month’s information may be incorporated into next month’s forecast,

enabling frequent comparisons between the forecasted and actual demand

• Adjustments to the plan can be made regularly as new information is available

Proposed approaches to workforce planning

What is new in demand estimation?

FY start

Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb

Shorter

planning

process

Plan

developed

Plan in action

(more accurate with macroeconomic data)

1

New data included

for

reforecasted model

Updated plans (monthly or as

needed)

2


Demand model application — Using reforecastingcapabilities

Demand estimation models are ran frequently to reforecast projections (e.g., monthly updates)

Salary estimation Salary excess Year-end headcount Opportunity cost

Apr-Mar Required $108,873,457 1,139

Client Plan (@ Mar XX) $133,463,223 $24,589,766 1,698

ModelFcst0 $124,386,621 $15,513,164 1,499 ($9,076,602)

ModelRFcst2 $121,124,778 $12,251,321 1,436 ($12,338,444)

ModelRFcst4 $117,157,213 $8,283,756 1,312 ($16,306,009)

ModelRFcst6 $113,911,222 $5,037,765 1,167 ($19,552,001)

800,000

900,000

1,000,000

1,100,000

1,200,000

1,300,000

1,400,000

1,500,000

1,600,000

1,700,000

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Project Hours

Actuals

Baseline Plan (3/xx)

Model Fcst 0

Model RFcst 2

Model RFcst 4

Model RFcst 6

Recruiting and predicting performance


Companies still struggle managing the top of the recruiting pipeline…how to more efficiently

manage and optimize prospects and suspects. How do we identify qualified candidates who

most resemble our best employees while decreasing recruiting cost and time?

Recruiting/talent acquisition — Sourcing more competitively

People Process Technology Data

25,000 Applicants

12,500 career profiles

6,250 background checks

3,500 candidate interviews

2,000 new hires

1,000 Employees at Year-End

(Yield: ~4%)

Defining your target (what you are trying to

replicate) is extremely important in modeling. By

segmenting the group of hires, you can define and

compare smaller subsets such as high performers

and/or long-term employees. This insight can be

invaluable throughout the entire hiring process.


Traditional application/recruiting data can make it difficult for recruiters to

differentiate between prospects.

Traditional recruiting data

Fred

• Twelve years of work experience

• Four previous employers in past

10 years

• Current employer is small

company

• Has completed several relevant

courses

Bill

• Fifteen years of work experience

• Two previous employers in past

10 years

• Currently unemployed

• Has completed no relevant

courses

Joe

• Twenty five years of work

experience

• One previous employer in the

past 10 years

• Current company is a

large company

• Attended community college

Who would be the most successful?

Who would be the long-term employee?

• Traditional recruiting data makes it difficult to differentiate

people

– Simple set of rules comparing work experience and/or education and

training levels

– Uniform approach across candidate base


Expanding the data elements from internal/external sources provides a more

comprehensive and detailed view.

Advance analytics — More detailed view

Predictive models built from these and hundreds of other data elements can better

quantify the likelihood and reasoning of future individual employee events.

Fred

• Twelve years work experience

• Length of residence — two years

• No children

• Currently renting a home

• Four previous employers in past

10 years

• Foreclosure/bankruptcy

indicators

• Medium-estimated household

income

• MVR negative correlations

• Owns pickup/SUV

• Hobbies — Sports

• Low regional economic growth

Bill

• Fifteen years work experience

• Length of residence — 10 years

• Household size is four with

small children

• Owns home

• Reading: Science Technology

• Urban single cluster

• Premium bank card

• Medium/high-estimated

household income

• No MVR data

• Owns two midsized cars

• Hobbies — Techie

• Medium regional economic

growth

Joe

• One employer in past 10 years

• Twenty five years work

experience

• Current company is large

company in a different industry

• Attended community college

courses in relevant topic area

• Renting a home

• Length of residence — one year

• Household size = one

• Revolve large monthly balances

• Suburban Striver Psychographic

Cluster

• High-estimated household

income

• MVR neutral correlation

• Owns three or more cars

• High regional economic growth


Workforce Analytics uses new and traditional sources of information to quantify

the likelihood and reasoning behind future employee events. If effectively

implemented, it allows scarce resources to be better focused, resulting in

measurable benefits.

More detailed view enables better recruiting

Fred Bill Joe

Likelihood of future event

40% less likely than average to be a successful hire and stay with the company for three years

60% more likely than average to be a successful hire and stay with the company for three years

30% more likely than average to be a successful hire; however, low retention indicators

Top three reasons

• Suboptimal employment history

• Low household responsibilities

• Poor financial indicators

• Optimal past employment history

• High household responsibilities

• Good financial indicators

• Suboptimal employment history

• No household responsibilities

• Higher financial indicators

Possible actions

• Unlikely pursuit — Third tier • Actively pursue — Primary tier

• Possible Pursuit — Second tier


The marketplace shows a developing

interest/demand for analytics in HR as

evidenced by:

– Continued investment in technology

infrastructure

– New type of HR leaders

– Challenging economy is forcing organizations to

embrace change

– Dashboard overload

Use an HR business analytics maturity model

when implementing reporting and workforce

analytics solutions. This helps reduce risk,

optimize spend and facilitate an integrated

approach.

Summary: Using advanced workforce analytics to make business-driven Human Resource decisions


Workforce planning and optimization forecasts

and visualizes the supply and demand of

individuals for critical roles and provides the

foundation to evaluate the actions needed to

meet the corresponding talent management

objectives.

Predictive models built from hundreds of data

elements can better quantify the likelihood and

reasoning of future individual employee

behavior and events.

Summary: Using advanced workforce analytics to make business-driven Human Resource decisions (cont’d)

Questions?


Russell Clark

Director

Deloitte Consulting LLP

[email protected]

John Houston

Principal

Deloitte Consulting LLP

[email protected]

Contact information

mailto:[email protected]

mailto:[email protected]

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"This presentation contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of

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