Human capital analytics has been touted as the next big frontier in human resources for years, and yet according to recent research, only about 16 to 20 percent of companies say they are even attempting it. Whether you have started your journey (or are still deciding if you need to), this Spotlight webinar with Jodi Crawford, human resources quality and analytics leader of Westfield Group, will provide one company’s story so far, including the challenges it encountered along the way. During this webinar attendees will hear: How Westfield Insurance has approached its human capital analytics roadmap. Challenges you are likely to face in launching an HR analytics function within your organization. Lessons learned the hard way and recognizing the small wins.

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Human
Capital
Analy6cs
Is
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Jodi
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HR
Quality
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Leader
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Human
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9. Human Capital Analytics is a
Journey:Wear Comfortable Shoes
Jodi L. Crawford
June 22, 2016
JodiCrawford@westfieldgrp.com
9

10. Westfield Group
— Privately held property & casualty insurance
and financial services company
— Founded in 1848
— Approximately 2,500 employees
— Provides commercial and personal insurance
in 21 states and surety services in 50 states,
through a network of more than 1,000
independent insurance agencies
— $4.9 billion in consolidated assets and $1.8
billion in net written premium
10

11. Poll Question
What is your company currently doing
related to human capital analytics?
A. Most human capital decisions are made
based on data analysis
B. We do some analysis in addition to
providing standard and ad hoc reports
C. We provide standard and ad hoc reports
only
D. Nothing
11

12. Workforce Intelligence Maturity
This maturity curve is from PwcSaratoga. The Saratoga survey was founded by Dr. Jac
Fitz-Enz, who is widely considered the “father” of human capital measurement and strategic
analysis. NOTE: In reality the process is not this linear. Companies tend to achieve
greater maturity in some analytics versus others at different times, driven by investment,
resources, and timing related to business needs.
12

13. Workforce Intelligence Elements
• What is likely to
happen and how
can we be better
prepared?
• Why did it happen
and how/where can
we improve?
• What happened,
and how do we
compare to a
defined set of
metrics?
• What happened?
Ad hoc metrics
and reports
Descriptive
Dashboards/
Benchmarking
Predictive
Analytics
Workforce
Planning/ Survey
Analytics
13

14. Why Did We Start?
14

15. Where are we starting?
15

16. Maintain Existing/Start Building New
16

17. THE BUSINESS APPLICATION GAP
Analytic Impact and Effectiveness at Business Application and Sophistication
There are two paths to
improving Analytic
Impact; however, first
improving business
application leads to more
impact more quickly.
■ Improving business
application alone can
improve Analytic Impact by as
much as 14%.
■ Improving sophistication alone
can improve Analytic Impact
by as much as 5%.
■ Organizations that are high on
sophistication but low on
business application are losing
up to 17% of potential Analytic
Impact.
Business Application is the
ability to take specific actions
from talent analytics.
Source: CEB, CEB Corporate Leadership Council Analytics Survey, 2013.
High
Low
Low High
BusinessApplication1
Sophistication2
High Application,
Low Sophistication
Average Analytic Impact: 1.14x
3% of organizations
Leading Analytic
Organizations
Average Analytic Impact: 1.22x
17% of organizations
Low Application,
Low Sophistication
Average Analytic Impact: 1.00x
60% of organizations
Low Application,
High Sophistication
Average Analytic Impact: 1.05x
20% of organizations
1
Business Application is measured by effectiveness at identifying the right business problems, applying business judgment to
data, and engaging leaders to take action.
2
Sophistication is measured by effectiveness at complex analyses, (e.g., predictive and
prescriptive modeling).
Only 17% of organizations
match high sophistication with
business application of insights.
Best Path to Impact
Analytic Sophistication is an
organization’s effectiveness at
using advanced methodology
for talent analytics.
17

18. Technology
18

19. Scope
19
Employee Insight for
Performance Optimization
Employee Targeting
Analysis reveals key target areas which drive strategic decisions
Employee Analysis
Key elements are analyzed by segment
Employee Segmentation
Meaningful segmentation is identified and used
Employee Profiling
Basic data is accurate and complete
Employee Research
Predictive models created/maintained; additional segmentation
The missing link in Westfield’s Business Intelligence . . . .
SophisticationLevel
Dependencies

20. Prioritization of Work in Scope
20
Employee Insight for
Performance Optimization
Employee Profiling
Personal Data Role Data Hierarchy Data
Employee Segmentation
Performance
Segmentation
Network
Segmentation
Functional
Segmentation
Employee Analysis
Cost Analysis ROI Analysis
Engagement
Analysis
Employee Targeting
Selection Targeting Retention TargetingDevelopment
Targeting
Employee Research
Predictive Modeling Micro Segmentation
Priority Legend
High
Medium
Low Capability Data
Capability/
Experience
Segmentation

21. When We Started
21

22. Now
22

23. What Gets Most Praise?
23

24. Where are we?
24

25. Data Governance
25

26. 26

27. Likely Challenges
27
1. Multiple systems that aren’t integrated
2. Data structures that were not designed for
easy output
3. People don’t think of human capital data as
“business data”
4. People don’t know what they want/need
5. People trust their opinion more than they
trust data
6. No money (or priority) to implement
technology

28. 28

29. Lessons Learned
1. Set clear, specific goals before you start
2. You need people who WANT to (and
are capable of) doing data analysis
3. Commit the resources needed
4. Do not confuse measuring HR
processes with human capital analysis
29

30. Lessons Learned con’t
5. The people who do the analysis need to
be in the conversation about the analysis
6. One size does not fit all
7. People need guidance in using the data
or analysis
8. DO NOT underestimate the amount of
change management needed
30

31. Remember . . .
31
. . .And wear comfortable shoes!

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