1. How Technology has Transformed HR
• Jonathan Gunter, Associate Consultant HR Systems
• November 30th, 2015
1
2. 2
Agenda
• Introduction
• The Early Stages of Human Resources
• The Growth of the HR function
• Evolution of HR Systems & Technology
• The Emergence of Big Data
• How will this apply to HR?
• Q&A
3. 3
Introduction
• B.Comm in HRM from University of Guelph
• Hired to Morneau Shepell in Aug. 2010, one of
the largest HR consulting firms in Canada
• Started in Total Rewards & HRIS
• Seconded to ERP Implementation Project,
which evolved into HR Systems & Technology
role
• Since then, involved in multiple HR technology
enhancement and implementation projects
• Enrolled in Certificate Program with University
of Toronto for Enterprise Data Analytics
4. 4
Human Resources played a less strategic role when the
economy was primarily production based…
Primary Org Focus “Personnel Dept”Economic Driver
6. 6
The Growth of the HR Function
• Economic shift from production-based
economy to knowledge-based economy
• The “War on Talent” started in the 1990s
• Rise of the global workforce
• Progressive companies now embrace HR as
strategic business partner
• Workforce management strategy critical to
driving the bottom line
7. 7
Evolution of HR Systems & Technology
(Bersin by Deloitte, 2015)
Process
Automation
Integration &
New Talent
Apps
Analytics &
“Systems of
Engagement”
2000 2015
• HRIS
• Comp & Benefits
Administration
• Applicant Tracking
• Recruitment &
Sourcing
• Learning
Management
Systems
• Workforce
Planning
• Social Recognition
• Mobile HR Apps
• Real Time
Engagement
• Predictive
Analytics
• Self-Service Apps
• Culture
Assessment
8. 8
Human Resources Management Systems
• Abbreviated as ‘HRMS’ or ‘HRIS’
• Implemented to reduce manual workload
• Track employee information related to status,
compensation, skills & competencies
• Intersection of Human Resources (HR) and
Information Technology (IT)
9. 9
Process Automation
• Increased complexity for the role of HR
resulted in a need to automate HR systems &
processes:
-> Payroll -> Time & Attendance
-> Performance -> Benefits Administration
-> Scheduling -> Absence Management
-> Recruitment -> Learning/Talent Management
-> Personnel Data Management
10. 10
Integration & Talent Applications
• Multiple systems supporting HR functions
› 33% of large organizations have 10+ HR Systems
• Need integration programming in order for systems
to exchange information
• Applicant Tracking Systems
› Database for handling recruitment activities
• Learning Management Systems
› Administer e-learning courses
› Often used for certification training
11. 11
Analytics & ‘Systems of Engagement’
(Bersin by Deloitte, 2015)
• Shift from ‘System of Record’ to ‘System of
Engagement’
• Mobile Platform – Apps
› 83% of organizations were expected to invest in mobile
technology in 2015
› Smartphone sales have increased nearly ten-fold in last
five years
• Embedded Analytics – Use Data as an Asset
› Move from Operational Reporting (what has happened)
to Predictive Reporting (what will happen)
12. 12
Transparency is the New Norm
• Easier for candidates to gather information on
companies
• Job candidates expect companies to convey
honest messaging about culture
• Important for companies to have focused
message that current and prospective
employees can identify with
15. 15
“From the dawn of civilization until 2003,
humankind generated five exabytes of data.
Now we produce five exabytes every two
days…and the pace is accelerating.”
- Eric Schmidt, Executive Chairman, Google
The Emergence of Big Data
16. 16
What is Big Data?
• Everything we do leaves a trail of data which
can be mined and analyzed
• Characterized by the ‘Four V’s’
› Volume, velocity, variety, veracity
• The ‘Datafication’ of Everything
› “The Internet of Things”
› Metadata = Data about data
19. 19
Data Analytics in HR
• Recruitment & Selection
• Retention Analytics
• Talent Management
20. 20
In Hiring, Algorithms Beat Instinct
(Harvard Business Review, May 2014)
• Study argues that humans are very good at
specifying needs for position, but tend to base
hiring decisions on inconsequential factors
21. 21
Retention Analytics Models
(Bersin by Deloitte, 2015)
Retention
Analytics
Models
• Performance
• Compensation
• Mobility
• Goals
• Education
• Work History
• Location
• Job
• Social Profile
• Compensation
• Connections
• Work History
External Data
Internal Data
• Flight Risks
• Retention Drivers
• Talent Problems
Outcomes
22. 22
Is HR Going to the Geeks? (Marr, 2015)
“ASKING THE
RIGHT
QUESTION”
DATA
COLLECTION
DATA
ANALYSIS &
INSIGHT
PRESCRIPTIVE
ACTION
• Do managers
impact
employee
performance?
• Performance
Data
• “Best Manager”
Award
• Qualitative
Interviews
• Plot performance
data
• Text analytics on
qualitative data
• Identify core
competencies of
good managers
• Insights
embedded into
performance
evaluations
• Alert system to
detect ‘good’
and ‘struggling’
managers
23. 23
HR Analytics at Work…Retention Analytics
“I received an alert last month from the HR system that
there is a 75% probability that Joe is likely to leave for a
new position that opened at a competitor…percentage
jumped to 95% when he requested a day off last
Monday – likely for a job interview…Mondays and
Fridays are usually interview days.”
- Jac Fitz-Enz, CEO of Human Capital Source Inc., from
Predictive Analytics for Human Resources (2014)
25. 25
Great Authors on HR Technology & Analytics
• Jac Fitz-Enz
- ROI of Human Capital
- New HR Analytics
- Predictive Analytics for HR
• Josh Bersin
- HR Technology for 2016: Ten
Disruptions on the Horizon
- Catalog of HR Technology Articles at
http://home.bersin.com/
26. 26
• Bernard Marr
- Big Data: Using Smart Big Data Analytics and
Metrics to Make Better Decisions and Improve
Performance
- Catalog of Articles on LinkedIn
• Viktor Mayer-Schonberger &
Kenneth Cukier
- Big Data
Great Authors on Big Data
27. 27
References
• Big Data, Viktor Mayer-Schonberger & Kenneth Cukier (2015)
• Is HR Going to the Geeks?, Bernard Marr (March 2015)
• In Hiring, Algorithms Beat Instinct, David Klieger, Nathan Kuncel, & Deniz
Ones, Harvard Business Review (May 2014)
• Predictive Analytics for Human Resources, Jac Fitz-Enz & John R. Mattox III
(2015)
• HR Technology 2015 – Ten Disruptions: Ignore Them At Your Own Peril,
Josh Bersin (December 2014)
• Watson and the Jeopardy! Challenge, IBM Research (Nov 2013),
https://www.youtube.com/watch?v=P18EdAKuC1U
• Human Resources Management System, Wikipedia (2015).
• Learning Management Systems, Wikipedia (2015).
Initially the profession was dominated by transactional work
Pay and benefits administration
Repository for employee information
Policy formation
Business more focused on sales & production, personnel management more of an afterthought
“War on Talent” – “Dot.com” boom, shortage in labour supply with individuals with tech-based educational background
80’s – sales office there would have been nine assistants, today there’s one
People have become self-sufficient in technology
Workforce management strategies – global vs local, temporary vs permanent, efficiencies gained in technology
JW: Huge push on bottom line, have to find efficiencies through people
HR establishes governance & framework, IT assigns resources to implement system security and enhancements
Primarily established a ‘system of record’
HRIS/HRMS
Could have combination of internally developed systems and externally purchased systems
Applicant Tracking Systems = Manage job postings, applicants, resumes, interviews
Many cases can filter through applications to focus on specific skill sets, educational institutions, work experience, etc.
AODA = Accessibility for Ontarians with Disabilities Act
JW: Cause and effect, have an idea of what the impact of our people are on our business strategy, build business case and provide ROI to make case more impactful, execs speak in $$$
Expect to use mobile technology for learning & training, employee directory, time & expense management, etc.
- Enough data to fill over 150 million iPhones
Volume = Growth of Big Data triggered by social networking sites
Companies like Facebook & Twitter trade on stock market as high as 100x their earnings, due to opportunities to monetize data available to them
Volume of total global data has increased ten-fold in six years
Velocity = New tools such as ‘Hadoop’ developed by Google can process substantial volumes of data in shorter time frames
Variety = Structured vs Unstructured
Internet of Things = Asthmapolis uses GPS data on inhalers to identify triggers to asthma attacks (i.e. proximity to certain plants)
Internet of Things = Sensory data in floors, automate use of electricity and care for elderly
Meta-analysis of 17 studies of applicant evaluations
Graph depicts % of above average hired through algorithmic work as opposed to chance
People easily distracted by things that are marginally relevant
85-97% of professionals rely on intuition or ‘gut feeling’ to ultimately cast hiring decision
One bank assumed their best performing people would be those with Ivy League degrees, but data analytics clearly showed the assumption was wrong. It turned out that candidates from non-prestige universities outperformed the top-university candidates, allowing the business to recruit the right talent for less money (Bernard Marr, “Is HR Going to the Geeks?, 2015)
Call centre staff with criminal records outperformed staff without criminal records (Marr, 2015)
High attrition impacts the bottom line
Cost to replace employee anywhere between 0.5 to 2 times base annual salary
Retention models can look at employee profile comprehensively, extracting attributes from internal and external sources
Cast a wide net
Can look at potentially hundreds of employee variables or attributes and determine how much each correlates to employee resignation
Employee reasons for leaving can be various and evolve over time, which emphasizes why it’s so critical to take all attributes into account with analytics exercise
Analytical model is live and will adjust as new data is put in
Need to validate prediction model is valid, cast a wide net in search for relationships, and tailor data collection strategy to company
Visier looked at over 140,000 current and former employees across several companies
Able to successfully predict “Top 100 at Risk”, which means all of top 100 in model left company
“The Analytics Revolution” by Dave Weisbeck
