It is true today more than any point in history that talent is a company’s greatest asset. To thrive in our hyper competitive global economy, companies need the right talent to deliver exceptional customer experiences efficiently with minimal risk. Traditionally, Human Resource teams have made decisions on hiring, assigning and developing employees using experience, instinct, and basic statistical data. The same advanced analytics and machine learning techniques we use to improve the customer experience are now being used for our people. People analytics provides insights and enables better and faster data-driven decision making across all aspects of people at work. Topics covered in this presentation include: How analytics has changed the customer experience. Current state of employee engagement and its impact. Limitations of cognitive decision making process. What is people analytics? How companies are using people analytics today? Challenges in adoption of people analytics. Guidance to get started on people analytics journey.

1. People Analytics:
Improving employee experience and productivity
Dr Susan Entwisle
DXC.Technology Executive Director / Distinguished Technologist
@susanentwisle

2. Digital is rapidly transforming our lives
Home Life Work

3. Exceptional customer experiences
Descriptive
Network Analysis
Classification
Regression
Time Series
Clustering
Associates
Sequences
Recommendations
Scheduling
Optimisation

4. By 2020, more than three-quarters of S&P 500 will be
companies you have never heard of yet.
New revenue streams
Increase business efficiency
Increase employee productivity

5. 40-70% of company revenue spent on payroll.
Only 24% of Australians are engaged with their job.
60% are not engaged and 16% are actively disengaged.
Actively disengaged workers account for more quality defects, safety
incidents, higher rates of absenteeism and lower retention rates.
Source: Gallup Study, 2014

6. Human brain has two cognitive decision-
making systems.
Thinking Fast: System One (Default)
Quick, automatic, emotional and intuitive.
Subject to human cognitive biases.
Thinking Slow: System Two
Slow, conscious, deductive and logical.
Deliberate effort required.
Prone to analysis paralysis.
Cognitive decision making
Source: Thinking, Fast and Slow, Daniel Kahneman, 2013.

7. Thinking fast cognitive bias
Facial recognition -
stereotypes
Attractive People -
Earn 3 – 4% more
Focus on recent
events
First 10 seconds

8. Right people into the right jobs,
make them engaged and
productive, and get them to help
us attract more customers and
drive more revenue.
Requires fundamental shift in
leadership and culture.
Nirvana might be perfect blend
of domain experts, analytics
and psychometrics.
Using analytics to transform employee experience

9. People Data Everywhere

10. Bersin Talent Analytics Maturity Model
Source: People Analytics: Where are we?, Bersin Deloitte, 2016

11. People Analytics in Action
Better Hiring
Pre-employment screening
Predictive model to identify
candidates who are more likely to
perform better and stay longer
based on role requirements and
cultural fit.
Talent sources
Identify metrics to determine what
recruitment channels are best
source of talent for roles.
Higher Growth
Employee engagement
Identify key drivers for employee
engagement and use to classify
employees in groups.
Customer satisfaction, sales
and employee linkage
Identify metrics of customer
satisfaction, sale and employee
engagement / traits that have
strong linkages.
Workforce planning
Develop predictive models and
run simulations to calculate future
headcount requirements by
business unit, which can be rolled
up to company level.
High performing teams
Identify optimal reward structures,
workspaces, work practices, team
structures, and members that
enable high-performance.
Better Talent Mgt.
Attrition prediction model
Key drivers to attrition and
employee attrition probability
prediction, for proactive
management.
Top talent hunt
Predictive model to help identify
top talent in company.
Identify optimal role(s)
Predictive model to identify
optimal roles types within the
company for a candidate.
Diversity and inclusion
Identify metrics to gain insights
into business outcomes from
diversity and inclusion programs.
Safety and wellness
Use wearables, social,
gamification and analytics on
health to promote employee
safety and wellness.
Improve Learning
Key factors improving learning
outcomes
Identification of key factors that
drive improved learning
outcomes.
Training forecasting
Develop predictive models and
run simulations to determine
training requirements based on
workforce planning inputs.
Adaptive learning
Deliver training using methods
and at pace that is tailored based
on students individual
demonstrated learning style.
Improve Operations
Automate employee lifecycle
Identify tasks that can be
automated or performed by digital
assistance / chatbot.
HR call centre
Identify in real-time most common
HR queries to improve HR call
centre operations.
Asset usage
Develop methods and models to
monitor employee behavior to
detect misuse of assets / fraud.
Extend and connect across entire business

12. Smart talent sourcing and development
46% companies are sometimes or
frequently understaffed. Impacts
customer experience.
Time to hire has more than doubled
in last 5 years.
68 days on average to hire
in Australia.
$4,000 average cost to hire
in US.
Source: LiveHire Investor Presentation, 2016

13. Detect and manage employee flight risk
Understand drivers and better
manage attrition across HPE
HP 300,000+ employees.
Flag employees that are
high-flight risk.
Identify actions to be suggested
to managers.
•Pilot across sales
team ~300 FTE.
Attrition decreased
5% to 15%.
•Implementation
across HR,
engineering and
‘high-rated’
populations.
•Estimate business
impact from better
planning.
•Evolve analytical
model using
logistic
regression.
•Test model
accuracy using
out of sample and
out of time data.
78% accuracy.
•Employee level
information
including salary,
age, role, career
progression,
bonus, etc.
•Confidentially
maintained
through usage of
masked ids.
3. Insights2. Model1. Data
Identified savings of $300 M associated with 1%
reduction in attrition and related improvement in
productivity and replacement costs

14. Identify top talent to grow
Identify top talent from within
HPE executives
Understand characteristics of top
talent at HPE.
Identify executives with these
characteristics.
•Review model
periodically, based
on new data points
available.
•Scoring (e.g.
logistic regression
/ classification)
model using
current talent
pool.
•Predict potential
leaders from
executive base.
•Key data across
performance (e.g.
rating, role,
promotion) and
talent (e.g.
leadership skills,
market
calibration).
•Data clean-up and
test.
3. Evolve2. Model1. Data
Model expected to help improve succession planning
across HPE.

15. Fatigue management to keep workers safe
Smartcap uses EEG to measure
brainwaves to detect and alert when
driver is fatigued.
Alerts driver when at risk of a micro
sleep.
Reports on fatigue patterns for
individuals and across organisational
units.
Use insights to adjust work policies
and practices to prevent fatigue.
Source: Smartcap - http://www.smartcaptech.com, 2017

16. Intelligent ‘bot’ assistant at work
Cortana, Siri and other general personal assistants.
Talla supports recruitment process and answers HR
queries.
Mila used at Overstock to automate employee
scheduling when team member calls in sick.
Jane responds to HR queries in real-time. Jane also
learns and improves over time.
IBM Chip automates on-boarding process and is “one-
stop shop” for HR program and policy information.
Imagine a future where every worker has a virtual assistant to help them
do their job

17. Social analytics to improve interactions at work
Team work and engagement: what are the
communication patterns between staff?
Space planning: where do staff spend most time?
What layout best supports collaboration?
Process improvement: evaluate how process
changes alter team collaboration and cohesiveness.
Source: Humanyze - https://www.humanyze.com, 2017

18. Neuroscience for improving performance

19. Big Data Challenges
Volume, Variety, Velocity and Veracity
Skills and capabilities
Data sources and integration
Infrastructure
Risk and governance issues –
security, privacy and data quality
Funding for initiatives

20. Employees are concerned……
63% of employees lack confidence that
their employer is keeping data about them
private.
72% believe their companies are not telling
them what data is being collected.
Source: Big Data Does Not Mean Big Brother, Conference Board, 2015.

21. Mature People Analytics Organisations
Share price of organisations with mature people analytics capability outpaced
S&P 500 by 30% over a three year period.
Source: Bersin Deloitte, 2016
2x higher employee engagement
2x more likely to improve recruiting efforts
2x more likely to improve their leadership pipeline
3x
more likely to realise cost reductions /
efficiency gains
more likely to improve mobility – right people,
right jobs2.5x

23. susan.entwisle@hpe.com
@susanentwisle