1 Big Data in Talent Development Building a Competitive Talent Analytics Function – Steve Coito Bersin by Deloitte Deloitte Consulting LLP
1
Big Data in Talent
Development
Building a Competitive Talent Analytics Function –
Steve Coito Bersin by Deloitte
Deloitte Consulting LLP
2 2
Agenda
Bersin by Deloitte Introduction
What is Talent Analytics?
Big Data
Global Talent Challenges
Talent Analytics Maturity Model
Samples of Using Talent Analytics
What We’ve Learned – Keys to Success
3 3
Bersin by Deloitte What We Do - Global provider of leading practices, trends, and benchmarking
research in talent management, learning, and strategic HR.
- 60% of the Fortune 100 are Bersin by Deloitte research
members, with more than 19.5 million employees managed by
HR teams using Bersin Research.
Broad Research Practices • Human Resources
• Leadership Development
• Learning & Development
• Talent Acquisition
• Talent Management
• HR Technology and Operations
Membership, Benchmarking,
Consulting - WhatWorks® Membership: Research, Tools, Education,
Consulting
- IMPACT®: The industry’s premiere conference on the
Business of Talent
- Advisory Services & Consulting
Human
Resources
Leadership
Development
Learning &
Development
Talent
Acquisition
Talent
Management
4 4
The Bersin Practices
Learning &
Development
Organization &
Governance
L&D
Benchmarking*
Content
Development
Informal
Learning
Learning
Culture*
Learning
Measurement
Performance
Consulting*
Learning
Programs
Learning
Technology
Talent
Management
Competency
Management
Performance
Management
Succession
Management
Talent
Strategy
Workforce
Planning
Career
Management*
Talent
Management
Systems
Leadership
Development
Leadership
Strategy
Leadership
Competencies
Leadership
Dev. Solutions
Executive
Development
HIPO
Development
Leadership
Development
Evaluation
Organization &
Governance
Talent
Acquisition
Employment
Branding*
Sourcing
Prehire
Assessment*
Screening
& Assessing
Hiring &
Onboarding
Talent
Acquisition
Strategy
Talent
Acquisition
Tools &
Technology
Human
Resources
HR Organization
& Governance
HR Planning &
Strategy*
Engagement*
Rewards &
Recognition*
Services &
Programs
HR & Talent
Analytics*
HR Tools &
Technology*
5 5
Research & Big Data Working Group
8 years of research into the
measurement, operations of L&D,
leadership, recruiting and HR
20 leading practitioner organizations
advising us on strategy
Our goal: education and best-
practices on “how to build an analytics
function”
Develop assessment services and
tools to help you understand how to
advance your program
Continue to study state of the market
and the best-practice solutions
6 6
Agenda
Bersin-Deloitte Introduction
WHAT is Talent Analytics?
Big Data
Global Talent Challenges
Talent Analytics Maturity Model
Samples of Using Talent Analytics
What we’ve Learned – Keys to Success
7 7
What is Talent Analytics?
Talent Analytics refers to the
analysis of "talent-related" and
“business-related” data
for business decision-making
9 9
The Analytics Journey B
usin
ess V
alu
e
Be
lie
fs
Re
ac
tive
Ch
ec
ks
Gut Feel A
d-H
oc
a
nd
On
go
ing
Re
po
rts
Da
sh
bo
ard
s a
nd
Be
nc
hm
ark
s
Reporting
Co
rre
lati
on
s
Sim
ula
tio
ns
Pre
dic
tio
ns
C
au
sa
l M
od
elin
g
Analytics
10
The Value of Talent Analytics
HR organizations using predictive analytics are…
2X more likely to
improve their
recruiting
efforts
2X more likely to
improve their
leadership
pipelines
generating
30% higher stock
returns than
the S&P 500
over the last
3 years
3X more likely to
realize cost
savings and
efficiency
gains
Source: Bersin by Deloitte High-Impact Talent Analytics Study 2013 Source: Bersin by Deloitte, 2014
11
Finance Operations
IT
Business
Leaders
Sales
Marketing
Customers
Workforce
Planning
Learning &
Development
Talent
Acquisition
Talent
Management
Leadership
Development
Performance Management
Employee
Engagement
Compensation
Analytics
Succession
Planning
Source: Bersin by Deloitte, 2014
13
Which functions have strong analytics capabilities?
HR & L&D Lags Behind
Source: Bersin by Deloitte, 2014
14 14
Analytics is Definitely Coming to HR The Evolution of Business Analytics in other Functions
The Industrial
Economy
The Financial
Economy
The Customer
Economy and Web
The Talent
Economy
Early 1900s 1950s-60s 1970s-80s Today
The Waves of
Business Analytics
Steel, Oil, Railroads Conglomerates
Financial Engineering
Customer Segmentation
Personalized Products
Globalization, Demographics
Skills and Leadership Shortages
Logistics and
Supply Chain
analytics
1980s
Financial and
Budget Analytics
Integrated
Supply Chain
Integrated ERP
and Financial
Analytics
Finance & Logistics
Customer
Analytics – CRM
(Data Warehouse)
Customer
Segmentation
Shopping Basket
Web Behavior
Analytics
Predictive
Customer
Behavior - CRM
Customer & Marketing
Recruiting,
Learning,
Performance
Measurement
Integrated
Talent Management
Workforce Planning
Business-driven
Talent analytics
Predictive
Talent Models
HR Analytics
Talent & Leadership
16 16
Agenda
Bersin Introduction
WHAT is Talent Analytics?
Big Data
Global Talent Challenges
Talent Analytics Maturity Model
Samples of Using Talent Analytics
What we’ve Learned – Keys to Success
17 17
“Big Data” in HR Defined
“Big data is a collection of data so large and complex that
it is difficult to process using traditional data processing
applications.”
- Wikipedia
The typical HR system has more than 400 data elements
about your own employees, and this data is being
updated nearly every day.
We all have Big Data opportunities within our own HR,
training, recruiting, and talent organizations.
19
• HR Manages a Plethora of HCM Products
87% have more than two systems
20% have more than 6 systems
7% have more than10 solutions
18% of large companies report use of more than ten HCM different systems
• 57% plan to procure new software within the next 18 months
61% will both replace and procure new solutions
23% will solely replace existing solutions;16% will solely add new products.
• 33% will replace standalone TM applications
22% with an integrated suite.
10% will replace their existing suites.
Finding the Data is Work
20 20
The Rich Sea of Data Opportunities Workforce Plan
Scenarios
Time and Cost
To Hire
Internal
Mobility
Employer
Brand, Alumni
Managerial
Grievances
Span of
control
Skills
Certifications
Open
Positions
Development
Plans
Tenure
Education, etc.
Retirement
Projections
Age, geography,
Skill level
Talent
Demand Plan
Onboarding
Effectiveness Turnover
Ratings
Rankings
9-box
Grids
Succession
Depth
Seniority
Skills Depth
Promotional
Readiness
Employee
Opinion
Employee
Engagement
Employee
Value Prop
Innovation
Programs
Readiness
Demographics
Supply,
Demand
Recruiting
Onboarding
Performance
Succession
Engagement
Spending Satisfaction
With HR svc.
HR/L&D
Staff Allocation
HR/L&D
Spending
Systems
Usage/$
Succession
Depth
360 and Other
Assessments
Proficiency vs.
Leadership Comp.
Successor
Readiness Leadership
Budget by
Group
Comp by
level/perf
Compa
Ratios
Perf-Pay
differentials Compensation
\ \
24
Standards Remain Elusive You cannot wait.. you have to develop your own
Other Standards Out There:
Bersin by Deloitte Factbooks®
Turnover Metrics (SHRM…)
Diversity & Inclusion (SHRM…)
SAP Book of Data
TDR Reporting (Yay!)
Engagement Standards (coming)
SASB Sustainability
Many more…
26 26
Agenda
Bersin Introduction
WHAT is Talent Analytics?
Big Data
Global Talent Challenges
Talent Analytics Maturity Model
Samples of Using Talent Analytics
What we’ve Learned – Keys to Success
27 27
What We Face as Professionals:
Today’s Global Talent Challenges “We have entered a global economy
where talent and skills shortages
challenge world economic and
business growth around the world.” - Klaus Schwab, Chairman, World Economic
Forum
Despite the high unemployment rates in
many countries, more than 65% of
global leaders cite “talent and leadership
shortages” as their #1 business
challenge.
- Bersin & Associates
TalentTrends®, Fall 2012
28 28
A Nexus of Change
nexus (Noun)
A connection or series of connections
linking two or more things.
A connected group or series:
"a nexus of ideas".
29 29
A Nexus of Talent Challenges
Agile Management
& Leadership Models
A New Generation of HR Practices
and New Type of HR Organization
New Technology
Social Tools, Analytics
Need for Improved HR skills and
capabilities.
Business
Speed and Scale
Disruptive
Competition
1
Shift toward
Emerging
Markets
2
Borderless
Workplace
Team Model
of Work
3
Specialization
Contingent Work
New Job &
Career Models
4
21st Century
Models of
Leadership
5
Competition
for Talent
Social Sourcing
& Recruiting
6
31 31
Young, Diverse Workforce …. By 2025, 75% of the workforce will be Millennial
-- US Census Bureau
32 32
Increasing Work Specialization
Expertise drives competitive
advantage
Specialization improves
quality and reduces cost
Deep skills developed
through “deliberate practice”
and reinforcement
Deep skills come from a range
of developmental experiences
Intelligent leadership paths,
career paths, training, work
assignments, understanding
high-performing competencies
are all drives of success.
Back Office, Operational, Contingent Employees
Functional Specialists / Front-Line Employees
Top
Management
Senior
Management
Middle
Management
Senior Specialists First Line
Management
The
Experts
33 33
Agenda
Bersin Introduction
WHAT is Talent Analytics?
Big Data
Global Talent Challenges
Talent Analytics Maturity Model
Samples of Using Talent Analytics
What we’ve Learned – Keys to Success
34
Operational Reporting Reactive, Operational Reporting of Efficiency & Compliance Measures •
Focus on Data Accuracy, Consistency, Timeliness
Advanced Reporting Proactive, Operational Reporting for Benchmarking & Decision-Making •
Multidimensional Analysis & Dashboards
Advanced Analytics Statistical Modeling and Root Cause Analysis to Solve Business Problems •
Proactive in Identifying Issues & Actionable Solutions
Predictive Analytics Development of Predictive Models • Scenario Planning •
Risk Analysis & Mitigation • Integration with Strategic Planning
Level 1
Level 2
Level 3
Level 4
Talent Analytics Maturity Model B
er
sin
b
y
De
lo
it
te
Source: Bersin by Deloitte, 2014
35
Operational Reporting Reactive, Operational Reporting of Efficiency & Compliance Measures •
Focus on Data Accuracy, Consistency, Timeliness
Advanced Reporting Proactive, Operational Reporting for Benchmarking & Decision-Making •
Multidimensional Analysis & Dashboards
Advanced Analytics Statistical Modeling and Root Cause Analysis to Solve Business Problems •
Proactive in Identifying Issues & Actionable Solutions
Predictive Analytics Development of Predictive Models • Scenario Planning •
Risk Analysis & Mitigation • Integration with Strategic Planning
Level 1
Level 2
Level 3
Level 4
Be
rs
in
b
y
De
lo
it
te
4%
10%
30%
56%
Talent Analytics Maturity Model
Source: Bersin by Deloitte, 2014
36
Advancing Takes Effort
Level 2
Advanced Reporting
Level 3
Advanced Analytics
Level 4
Predictive Analytics
Level 1
Operational Reporting
Level of Effort
Level of Value
Choke Point
for Most
Organizations
Source: Bersin by Deloitte, 2014
37 37
Level 1: Reactive Operational Reporting
Goals: - Implement a scalable, accurate, easy to use reporting environment
- Understand all the data and systems you have to work with
Tasks: - Understand and collect data you have
- Build a Data Dictionary
- Work with IT to implement standard reporting tools
Key Skills - Patience and database “interest”
- Great relationship with IT
- Ability to write, document, and manage projects
Expected Outcome - Standard tools and reports
- Ease in “responding” to any report request
- Tools to help managers find their own data
Skills:
38 38
Level 2: Proactive - Advanced Reporting
Goals:
• Develop skills and tools to implement “proactive” reporting and tools for line managers
• Look at trends, benchmarks, and results against plan
• Develop actionable business dashboards
Tasks:
• Understand all the “dimensions” of your data (how it will be drilled and filtered)
• Audience analysis – who are your “audiences” and what decisions do they make?
• Performance consulting – start focusing on one or two major “problems”
• Purchase or select benchmarking data
Key Skills
• Understanding of multi-dimensional reporting
• Business acumen and relationship with finance organization
• Strong business alignment and partnership with business leader (or leader)
• Ability to influence what IT does
Expected Outcome
• Dashboards used by the business
• A business unit success
Skills:
39 39
Level 3: Strategic Analytics
Goals:
• Developing “causal models” or “people models” which identify cause and effect
• Segmenting people into groups which can be analyzed in detail
• Integrating data with recruiting, performance, compensation, leadership, etc.
Tasks:
• Building strong relationships with all areas of HR
• Selecting a key problem to start analytic study
• Implement analytics “project,” iterate, and demonstrate results
Key Skills
• Analytics and statistics skills
• Information visualization and compelling presentation skills
• Excellent performance consulting and ability to understand work environment
• Partnership with line executives and ability to focus on key problems
• Skills in development of tools across many areas of HR and Talent Management
Expected Outcome
• A “success” project which delivers some breakthrough findings
• Direct change or decision-making tools in the hands of the business
Skills:
40 40
Level 4: Predictive Analytics
Goals:
• Putting place models which can “predict” future scenarios
• Integrate your work with workforce planning and business planning functions
Tasks:
• Expand your analytics skills and expertise
• Directly connect with business planning, finance, and recruiting teams
• Expand relationship with 3rd party data, engagement, and consulting firms
Key Skills
• Modeling and deeper statistics skills
• Finance and business planning
• Senior experience in organization design
• Strong relationship with or deep experience in workforce planning and acquisition
Expected Outcome
• A “workforce planning” model which describes how performance can be improved
• Repeatable models which can be extended into new domains
• Credibility with Finance
• An integrated, strategic analytics function
Skills:
42
Staffing Increases by Maturity Level
(Assumes 10,000-person company)
Level 2:
Advanced Reporting
Level 4:
Predictive Analytics
Level 3:
Advanced Analytics
Level 1:
Operational Reporting
Source: Bersin by Deloitte, 2014
43 43
Most Likely Analytics Home Runs in 2014
Recruiting – Understanding the quality of sources, best candidate qualifications,
cost and efficiency of source, analyzing quality of hire
Sales Analysis – Profile of high-performing sales professionals, sourcing, and
training needs
Workforce Planning – Determining the optimal mix of skills and competencies in
order to meet future demand in the most cost-efficient manner
Retention Analysis – Developing the capability to determine levels of engagement
and predict retention risks to proactively address issues
Leadership Analysis – Leadership effectiveness, pipeline strength and diversity,
talent mobility
44 44
Agenda
Bersin Introduction
WHAT is Talent Analytics?
Big Data
Global Talent Challenges
Talent Analytics Maturity Model
Samples of Using Talent Analytics
What we’ve Learned – Keys to Success
45 45
Questions to ask ….
What characteristics drive high performing sales people?
What work assignments will lead to strong leadership?
What attributes of a job candidate will lead to perfect fit?
Why retention is low in certain locations and jobs?
What is the real result of poor on the job safety?
Why some of your top people leave for competitors
What compensation and rewards will drive most value?
….
49 49
Big Insurance
A $33 billion insurance company has developed a
behavioral assessment based on a set of beliefs held by
the top executives
Top sales people need college degrees from top rated
schools, they should have good grades, and they should
have experience selling high value products.
But the data proves otherwise.
Insurance
Company
50 50
Data Showed Six Things Matter:
The Belief
System
Was Wrong
Within six months
of implementing a
new screening
process
revenues went
up by $4 million
What Did NOT Matter
• Where they went to school
• What grades they had
• The quality of their references
Insurance
Company
52 52
Agenda
Bersin Introduction
WHAT is Talent Analytics?
Big Data
Global Talent Challenges
Talent Analytics Maturity Model
Samples of Using Talent Analytics
What we’ve Learned – Keys to Success
53 53
The Ugly Part of The Story
Visual
Dashboards Advanced
Analytics
Predictive
Models
The Ugly Part of The Story
Visual
Dashboards Advanced
Analytics
Predictive
Models
Data
Integration
Data
Dictionary
Data
Quality
Data
Governance
Data
Entry
Scalable
Computing
Reporting
Tools
Disparate
Systems
Data
Visualization
Data
Analysis
The Ugly Side: Data Management
54 54
The Big Aha!
In HR, talent, leadership, and capabilities you already have
most of the information you need to deliver breakthrough new
solutions for your organization.
What most organizations do not have is the organization structure,
skills, leadership, and tribal knowledge to use this information
yet. Many of the skills are likely available in your
organization.
This marketplace is rapidly evolving and over the next few years
companies who do not implement a Big Data in HR strategy will fall
behind those that do.
55 55
We Don’t Measure the Right Things
Source: Bersin & Associates 2012
High-Impact Learning Organization® (HILO)
56 56
Focus on The Problem, not the Data Business problem first, then focus on arranging and using the data
Why is turnover high in some areas?
What drives sales productivity?
Why is their fraud in some branches?
What sales or service processes drive account renewal?
What is the impact of training on long term productivity?
How do we assess the “right” candidates for sales?
What will our talent gaps be next year based on retirement?
Business
Problem Data
57
Who Will be Top Sales Performers?
What will Reduce Fraud?
How do we Improve Client Retention?
Will Increasing Pay Drive Higher Levels of Retention?
How do we Develop Leaders in China?
58 58
Treat Measurement as a Process Why you must build an analytics function, not a set of tools
Measurement as Process, not a Project
60 60
Conclusion
Big Data in HR is has become a
business imperative
Integration of analytics teams and
building capability are key, not tools
and technology
Analytics is a journey which will
change the way you think
Talent analytics will extend to the
other business analytics groups in
your organization
Expertise and patience is key, but
focus on key business problems first