analytics - central michigan university analytics 92217... · 2017-10-25 · leverage mail audits...
TRANSCRIPT
Analytics
69,700+
Americas
112,900+
EMEIA
40,500+
Asia-Pacific
7,700+
Japan
150countries
250kpeople
$31.4b revenue
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FORTUNE’S100 Best Companies to Work For®
18 years
Our global reach means you’ll have a global career.
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A better working world starts in our service lines:
► Advisory
► Assurance
► Tax
► Transaction Advisory Services (TAS)
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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
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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
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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
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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
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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.
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Analytics Spectrum
Advanced analytics
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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
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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
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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
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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
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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
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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
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Analytics Careers
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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
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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
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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
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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
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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)