analytics - central michigan university analytics 92217... · 2017-10-25 · leverage mail audits...

Analytics

69,700+

Americas

112,900+

EMEIA

40,500+

Asia-Pacific

7,700+

Japan

150countries

250kpeople

$31.4b revenue

FORTUNE’S100 Best Companies to Work For®

18 years

Our global reach means you’ll have a global career.

A better working world starts in our service lines:

► Advisory

► Assurance

► Tax

► Transaction Advisory Services (TAS)

Page 3

We are set up

to meet our

clients’

challenges.

We focus on a variety of industries, including, but not limited to:

► Automotive

► Cleantech

► Consumer Products

► Government & Public Sector

► Health Care

► Life Sciences

► Media & Entertainment

► Mining & Metals

► Oil & Gas

► Power & Utilities

► Private Equity

► Real Estate

► Technology

► Telecommunications

Additionally, we have built a dominant position and focus on financial services through our Financial Services Office (FSO).

► Banking & Capital Markets

► Wealth & Asset Management

► Insurance

► Sectors

Performance Improvement• Customer

• Finance

• IT Advisory

• People & Organizational Change

• Program Management

• Strategy

• Supply Chain & Operations

Risk

• Actuarial Services

• Financial Services Risk Management

• Information Security

• Internal Audit

• Risk Assurance

• Risk Transformation

► Competencies

EY’s Analytics Practice

Our Analytics competency group helps clients

manage and use data, statistical and quantitative

analysis, explanatory and predictive models and

fact-based management to help improve business

performance, drive better business decisions and

proactively manage risk.

Example service offerings:

►Analytics and big data strategy

► Information infrastructure

► Information management

►Analytics governance

DnA Practice

AnalyticsAdvanced analytics

Advanced techniques, models and statistical methods to drive improvement

DataInformation management

Collect, protect anddistribute structured and unstructured

data

DigitalHolistic approach to digital transformation building

capabilities as well as technologies.

Around 400 Analytics practitioners in the US in 40 different offices around the country.

Analytics

Driving performance improvement, value generation & execution excellence

Doing analytics on what matters Business led – starting with questions that matter

Managing analytics as a portfolio as opposed to discrete projects

Making connections between and within functional silos

Fostering unity, clarity and efficiency between the business and IT

Improving business performance

Illuminating key performance drivers to align strategy and execution

Creating more insight to drive execution, with less need to view detail

Using agile delivery for clearer focus on end user requirements and faster delivery

Shaping a culture of fact-based decision making Applying leading practices and models to solve business issues

Increasing innovation and value addition

Optimizing information and tools that already exist

Encouraging collaboration through the change

Rules/

Algorithms

Focus of many companies

► Many of analytics companies in the marketplace today are dominated by data warehousing, dashboard & reporting solutions.

► Many clients are not realizing the full value of analytics as they struggle to systematically integrate analytics into operational decisions

EY’s strategic focus

► Our focus is on becoming the leader in “value-driven analytics”

► We fully appreciate the importance of change management and are well positioned to help our clients more effectively use analytics to create value.

We combine what is technically possible with the commercial ‘know how’ to create value

Strategic Focus

Analytics Defined

Analytics, is NOT just Reports

And it is not just a Data Warehouse

Analytics is business driven and technology enabled

Analytics is

the use of data, statistical and

quantitative analysis, explanatory

and predictive models, and fact-

based management to drive

decisions and actions within an

organization to create strategic

value.

Analytics Spectrum

Advanced analytics

Legacy practices in financial forecasting are not meeting the needs of today’s CFOs

Legacy practices

• Heavy use of spreadsheets to collect forecast data

• Dozens or even hundreds of analysts providing

forecast assumptions at a detailed/granular level

• Time-consuming processes to change forecast

assumptions for what-if modeling

• Forecasts influenced by personal and organizational

biases

• Reliance on a limited number of internal data sources

as inputs to the forecast

• Forecast horizons not extending past fiscal year end

• Improved forecast accuracy and timeliness

• Ability to rapidly change assumptions (what-if modeling)

• Ability to respond quickly to management requests

• Removing human bias from the forecast

• Ability to incorporate diverse data sources for a more

holistic view of the business (e.g., social media)

• Visibility into the causes (drivers) of variances

• Rolling forecasts with up to an 18 month time horizon

Today’s needs

1

Analytics can address the inherent biases and limitations in traditional forecasts

Improved accuracy

Improved efficiency

An analytics-driven

approach to

forecasting

eliminates the

biases inherent in

manual forecasts,

while minimizing

forecast errors and

process

inefficiencies. In

addition, the richness

of the forecast model

can be enhanced by

incorporating diverse

data sources,

including

unstructured and

external data.

Fewer people

required to

input forecast

assumptions

Pre-population

with machine

learning allows

people to focus

on exceptions

Quickly

identifies critical

drivers

impacting

forecasts

Changing

forecast

assumptions

can be done

instantaneously

1

J F M A M J J A S O N D

EY assisted a Truck OEM to improve their revenue forecast accuracy and timing by coupling advanced statistical forecasting models with process expertise

► Actual units for fiscal years 2012 through 2015 at a business unit level were used to develop the predictive forecast model

► The objective of the predictive forecast model was to forecast revenue units on a rolling 6 month basis► In order to test its accuracy, the model was used to estimate monthly revenue units throughout fiscal year 2016

Forecast Model Timing – requires

prior month actuals

Forecast

Average

Monthly Error

Average Monthly

Absolute Error

Current Forecast 25% 26%

Predictive Forecast 2% 8%

Period Forecasted

► The predictive forecasting model can generate a forecast as soon as prior month actuals are obtained

► The forecast model was developed to enable a rolling forecast, not constrained by the fiscal year

► Coupled with process enhancements, EY was able to identify up to 3 weeks in cycle time reduction

Objective: Improve Accuracy

Objective: Enhance Timing

► The predictive forecast model reduced forecast bias, bringing the average error down from 25% to 2% for the company total revenue forecast

► Further, the predictive model significantly reduced the absolute error, proving the reliability of the model on a month-to-month basis

Error is defined as (forecast – actual) / actual

Month

Units

Forecast Comparison

FST1

FST2

1

Approach & Description: ► Bring a team of EY Advanced Analytics and Risk professionals together

to co-develop the solution with IA SMRs

► Rather than a rules based approach, focus on behavioral indicators that

would identify transaction abnormalities based on numerous

dimensions including local demographics, peer-group comparisons, and

product comparisons

► Develop predictive model using historical audit results (pass/fail) to

enable scoring of 100% of transactions

► Focus on-site dealership audits to those dealerships with the highest

aggregated risk and cost/benefit of audit. Leverage mail audits for

others to cover 100% of identified high risk transactions.

► Validate modeling approach and accuracy with CLIENT to assist in

change management and adoption

► Perform root cause analysis on audit findings and work with Sales &

Marketing as well as Warranty Management to reduce CLIENT Risk &

Exposure and refine processes

Value Delivered

► Improved the exception identification rate by 4x through predictive

modeling techniques

► Delivered over 80% accuracy in the aggregated percentage of

projected to actual recoveries

► Near real-time analysis and reporting allowed for rapid identification of

emerging issues

► Identified opportunities to deliver value to the business through

marketing effectiveness of incentive programs and warranty cost

reduction

► Enabled business to assess risk for 100% of all claims vs. random

sampling approach that typically covered 1% of population

Situation: INTERNAL AUDIT was interested in improving the

efficiency and effectiveness of their dealership audits by

leveraging advanced analytics and predictive modeling to focus

efforts on high risk dealerships and transactions.

Identify Specific

Behaviors at Each

Dealership Driving

Increased Risk Scores

in Predictive Model

Predictive

Modeling and Data

Visualization

Helped Plan

Dealership Audits

Dealer Risk Management Case StudyPredicting fraudulent transactions

EY Confidential - For Discussion Purposes Only

2

A department desired to improve their resource planning process and align

with company wide strategic changes

Resource planning was subjective and not program

specific, leading to large error in program estimates

Resource planning: Current resource estimate was

based off of a fixed percentage of total company budget

Facility planning: To estimate facility needs, facility

managers attempted to obtain estimates of asset needs

from individual program managers

Problem

Solution

A physicals based model was developed to estimate and calendarize total program Headcount and Facility needs for an individual program

Resource planning: A total resource model was developed using linear regression techniques. The total resource needs are then

planned for over a predicted program calendar curve

Facility planning: Total facility occupancy model was developed to estimate asset utilization needs. A dynamic calendar curve that relies

on behaviors of prior period activities was developed

Limited data points were available due to the long

project completion time (2-5 years)

Program attributes change over time and are

maintained in disparate spreadsheets and

SharePoint sites

Large variation in program durations and attributes

Inconsistencies in time keeping practices across

regions

Complications

3

The resource planning model produced unique program estimates based on the programs physical attributes

OutcomeModel ApproachSolution Overview

To account for the small sample

size available an ensemble

linear regression model was

used to estimate total headcount

Decision trees and linear

regression models were used to

determine the shape of the

calendar curve

Total Resource Model

Resource Role Allocation and

Calendarization

Program Lifetime Estimate

Total resource hours needed over the

life of the program can be estimated

based on program physicals

The output of this model is then

distributed across resource roles,

geographies, etc. and is distributed

over the predictied life of the program

This model resulted in a unique calendar

curve for each program and accounted for

the differences in roles, responsibilities, and

time keeping practices between regions

The model improved portfolio accuracy by

10x (from 17% to 1.7%) and reduced the

average absolute error per program by

+- 27,000 hours

Pre

dic

ted

Pro

gra

m H

ou

rs

Actual Program Hours

3

Analytics Careers

Why go into a career in Analytics?

Analytics is more popular now than ever before, hence we are seeing significant investments being made into this space by leading fortune 500 clients

2007 – 2011 saw rapid increase in searches related to Analytics, post 2012 the searches have consistently plateaued due to continued demand

►Data Scientist / ►Analytics Role

► #1 Job in America

In a survey by Glassdoor, the data scientist/analytics role was

voted the best job in America for 2016

High Demand for Analytics

Percentage of global companies that agree Big Data & Analytics

are changing the nature of competitive advantage?

78%

*Forbes Insights and EY

Percentage of global companies that are investing $5m+ in

analytics?

66%

Percentage of organizations that describe their analytics maturity as

leading?

12%

Global Analytics Survey – EY & Forbes did a joint survey across F500 companies

Roles driving contemporary analytics capabilitiesDrive innovation and incubation of business questions

Strengths

► Lead workshops to facilitate the art of the possible

► Build the analytics portfolio

► Connect dots across initiatives

► Build relationships with key stakeholders

► Drive the cultural transformation

► Pull together data from disparate systems

► Solve business problems / hypotheses with data and advanced

analytical capabilities

► Leverage technology to semi-automate capabilities

► Drive data discovery efforts

► Pull together data from disparate systems

► Solve business problems / hypotheses with data and

visualization capabilities

► Hold design sessions with the business

► Construct intuitive visual representations

Activities

Analytics Evangelist

Data Scientist

Data Designer

► Storytelling (new possibilities)

► Very strong communications

► Strong understanding of

analytics (descriptive, diagnostic,

predictive and prescriptive)

► Data Wrangling (bringing together

disparate sets of data)

► Data Mining

► Programming

► Statistics / Mathematics

► Storytelling (visualizing the future)

► Data Visualization

► Data Wrangling

► Strong communications

Roles driving traditional IT analysis/delivery capabilitiesDrive industrialization and sustainment of IT solutions

Strengths Activities

Solution Developer

Information Builder

Data Analyst

Business Analyst

► Collaborate with the business on functional requirements

► Advocate for the business during development cycles

► Perform testing

► Driving change management and training efforts

► Conduct profiling, standardization and harmonization to collect

business rules

► Query data to identify patterns and/or anomalies

► Perform triage to questions and/or issues

► Support testing efforts

► Drive data integration, data management and data quality

initiatives

► Lead governance framework

► Leverage technology to create scalable assets for the enterprise

► Design and build technology solutions

► Automate integration of data between multiple sources

► Apply technology architecture on a ‘best-fit’ basis to enable the

analytics initiative

► Perform unit, integration and performance testing

► Strong communication skills

► Deep understanding of

business processes

► Requirements gathering

► Data Visualization

► Data Profiling

► Database querying

► Functional knowledge

► Data Integration

► Master Data Management or

Governance

► Data Integration

► Technology

► Programing

► Database

EY on Campus

Date Time Event Location

September 6, 2017 5:00 – 8:30 PM School of Accounting

Resume Workshop

Bovee UC –

Maroon Room

September 14, 2017 6:00 – 8:00 PM Meet the Recruiters Finch Fieldhouse

September 21, 2017 7:00 – 8:30 PM BAP Presentation Pierpont Auditorium

September 22, 2017 1:35 – 2:25 PM CMU Student Visit to

Detroit Office

EY Detroit Office

September 22, 2017 11:30 AM – 12:00PM Data Analytics

Conference

Grawn 100

October 4, 2017 5:30 – 7:00 PM Pre-Night – On

Campus Interviews

La Seniorita

October 5, 2017 8:00 AM – 5:00 PM On Campus Interviews

– FT and Intern

Career Center –

Ronan Hall

October 25, 2017 6:30 – 8:00 PM SAP SUG Presentation Grawn (TBD)

analytics - central michigan university analytics 92217... · 2017-10-25 · leverage mail audits...

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