Describes components of Talent Analytics from a systems perspective: People, process, technology, tools, leadership, context. Highlights difference between goals and systems. Describes how analytics can be used to build an innovation engine. Provides real life examples from predictive retention analysis in a Financial Technology firm.

1. Use of Analytics in Talent
Decisions: Systems
Perspective
Sharad Verma
Indian Human Capital Summit
Taj Land’s End, Mumbai
23 Jun 2016

2. Analytics in Talent Decisions
Points to be covered today:
A systems Approach to Talent Analytics
Analytics as an Engine to Innovation
Real example of Human Capital Analytics (results from
predictive retention analytics)

3. “In God we trust, everything
else is data”
Frequently heard at Google

4. Systems approach to Talent
Analytics
What is a system and how does it work?
Goals versus systems
Components of a Talent Analytics system
Deterrents: why analytics fail to deliver results

5. What is a system
A system is a way of doing things that gives
predictable and improved results repeatedly over a
period of time. It includes processes, people, tools,
technology, mindset, habits, culture and environment.
Analytics can be used to build robust and high
performance systems over time

6. Goals versus systems
Goal is a specific, measurable target to be achieved
within a defined timeframe
System ensures delivering results repeatedly over time
EXAMPLES

7. Goals versus systems
Goal: Reduce employee attrition by 5% in the next financial year
System: Build a way to continuously assess potential attrition,
identify drivers of both retention and attrition, inform decision
making and take a series of corrective actions, measure
effectiveness of actions, course correct (Talent Retention
System)
Note: (An intelligent system self-corrects via feedback)

8. Goals versus systems
Goal: Achieve internal promotion target of 60%
System: Build a way to identify factors that prepare people for
next level, predictably build those factors in selection, talent
assessment and promotions, measure whether internal
promotees are successful or not and reasons why (Talent
Management System)
Note: (System may sound like “how” or process, but its more, it
is a specific way of doing things to deliver results)

9. Goals versus systems
Personal Goal: Lose 5 kg weight in next 2 months
System: Implement a way of living that reduces risk of
disease and improves fitness
Note: (System may sound generic compared to a
specific Goal but it is not - its success can be
measured based on the results delivered - good or bad)

10. Components of a Talent
Analytics System
People
Technology
Culture Leadership
Processes
A BRESULTS
SMEs Context

11. Talent Analytics Systems
For good outcomes, start with good questions
What qualities make excellent internal leaders? How do we spot
and develop those qualities?
What are the predictors at the time of recruitment that someone will
be a star performer? What are the right questions to assess those
predictors?
What factors can accurately predict risk of attrition?
What factors result in consistent high performance on a specific
job?

12. Talent Analytics Systems
After defining a clear desired outcome:
List hypotheses
Prepare datasets
Map current biases arising from experience, judgment or personal
worldview
Use statistical modelling
Understand context
Draw from subject matter experts

13. Why analytics fails to deliver
These org factors are deterrents to a data-driven culture:
Not knowing what to measure and “why” (ill-defined results and outcomes, bad start questions,
presumptive approach)
Hierarchy
Experience
Bias (conscious and unconscious)
Leadership (can be an enabler also)
Judgment / beliefs / emotions
Skills
Tools
Lack of scientific and statistical temperament: desire for quick conclusions / reading data wrong

14. Analytics as an engine to
innovation
> There are less than 50 companies that are true innovators and not more than
500 individual innovation / analytics influencers. In any field, less than 5% of
companies/people fit this category
> Imitators repeat the work, ideas, practices of innovators many many times over
> Analytics need to be built keeping the context in mind - just repeating an
isolated practice from Google may not give same results
Influencers Imitators
BUILD YOUR OWN ANALYTICS MODEL

15. Real life example
A predictive model for talent retention (Financial Technology)
Over 2 years, a rigorously tested predictive analytics tool
helped to:
- reduced attrition by over 12% annually
- significantly improved “managing for retention” ability
- delivered important lessons on drivers of retention and
engagement - used by leadership, managers and HR

16. Some interesting findings
Starting question:
What is more important for retention managers or
compensation?
Notes:
- Managers believe and repeatedly tell management it is
compensation
- Leadership believes “people leave managers not companies”

17. Some interesting findings
Statistically valid findings:
- People who are unhappy with compensation are willing
to wait for 6 months or more before they decide to leave
- People who are unhappy with managers leave within 3
months

18. Some interesting findings
Starting question:
What are the most important predictors of retention?

19. Some interesting findings
Statistically valid findings:
4 highest scorers and most consistent predictors of employee
retention when together:
1. “I am in the right place” (environment, company)
2. “I am in the right job” (meaningful, challenging role)
3. “I have great relationships at work” (colleagues)
4. “I am treated fairly” (rewards, performance measurement)

20. Some interesting findings
Other important findings:
1. Work/life balance when positive is an “equalizer” - it compensates for low scores on role,
compensation etc
2. Just scoring low in terms of satisfaction does not mean the employee will leave. -
Unhappy employees will stay because of “equalizers” - in fact many tenured employees have
lower happiness scores
3. A dissatisfied manager is more likely to have dissatisfied team and higher attrition rate
4. Work/life balance when negative is an immediate deal breaker
5. There are four SWEET SPOTS: a) a convenient commute time b) a great manager c) a
perfect role d) great people to work with
6. Three most important qualities of managers - TRUST, HOPE and WORTH

21. Thank you
Email: sharad@iSharad.com
Twitter: @iSharad
Blog: iSharad.com