data-driven organizations...data-driven organizations people analytics mark arian –alan guarino...

Data-Driven Organizations People Analytics

Mark Arian – Alan Guarino

New York, April 5th 2018

The Rising Influence of People Analytics

2

4. SUPERIOR

ANALYTICS

EXECUTION3. SOME REAL

CHALLENGES2. LINKING

TALENT & BUSINESS

PERFORMANCE

1. THE

FUTURE

IS NOW

© 2018 Korn Ferry. All rights reserved

1. The Future is NowFrom AI for job interviews preparation

3© 2018 Korn Ferry. All rights reserved

Data analytics for better behaviors and decisions.

Live feedback using 3 AI engines: words, tone & pace, face expressions analytics.

Self-service analytics: benchmark of leaders’ profile.

1. The Future is Now“What does an agile / digital leader look like?”

© 2018 Korn Ferry. All rights reserved 4

Competencies Experience

Traits Drivers

Source: Korn Ferry Institute

Leaders’ drivers #1 request from KF clients.

1. The Future is NowBig Data analytics to solve perennial pay equity issue.

5

KF Pay Data

Average ‘headline’ gender pay gap is:

17.6%

Comparing people at the same level, in the same

company and same function, average gap is:

1.6%


1. The Future is NowThe 4th industrial revolution is underway.

6

Traditional employee life cycle

Recruit Exit

Open labor market

Talent Market Place

Supply Demand


Manage

AI | Programmatic

algorithms | Gamification

Predictive analytics

Machine Learning

APIs | Neuroscience

Big Data | Virtual Reality

Nanotechnology

Blockchain | Wearables

Internet of Things (IoT)

3-D Printing | Bots …

HA

RD

S

OF

T

33% of contingent workers in many large companies

New ‘digital workers’

Linking Talent & Business Performance

7

4. SUPERIOR

ANALYTICS



TALENT & BUSINESS

PERFORMANCE

1. THE

FUTURE

IS NOW


2. Linking Talent & Business Performance Talent analytics payoff for HR

2x as likely to improve their recruiting and leadership pipeline.

3x as likely to realize cost or efficiency gains.

3.5x as likely to get the right people in the right jobs.

Organizations more active* with HR data analytics are…


2. Linking Talent & Business Performance


Analytics & People Interventions

People to Profit Pipeline

Embedding talent analytics into business processes: how much

of the $ strategy is talent?

2. Linking Talent & Business Performance Key success drivers


Business Strategy

Pivotal Roles & Talent

• What are the critical jobs?

• What is ‘Best in Class’?

Assessment

• How good are talents against the strategy and ‘Best in Class’?

• How competitive rewards are?

Analysis & Planning

• How to address gaps (recruit, develop, contingent, structure…)?

Execution

• How effective are the talent processes?

Business Outcomes

• What are talent outcomes (retention, engagement, succession) and business results?

People to Profit Pipeline – Full talent and business analytics

New and better answers to those questions thanks to more data

and computing power.

2. Linking Talent & Business Performance Big, more granular data


Over half a million employees rated as a part of multi-rater feedback by over

5 million ratersOver 78,000assessments of

Organizational

Climate created

by leaders and

the Leadership

Styles they

employ in the last

three years

6.9 million

165,000 assessments of Decision Styles

62,000 learning agility assessments

High volume

Talent Q pre-hire assessments

53,000 assessments of leadership potential

Employee engagement

employee respondents across all industries within the last 3 years from over 350 organizations

56,000 KF4DSearch &

Enterprise

88,000 business

simulations

assessing

leadership

readiness

Assessments of

Emotional

Intelligence

Structured and unstructured data.Internal and external data.Cloud base, virtual data warehouse.Asynchronous, always on.

2. Linking Talent & Business Performance Discretionary Energy and Performance

SUPERIORPERFORMANCE

VISIBLE & EFFECTIVE LEADERS D I S C R E T I O N A R Y E N E R G Y

CLARITY12. Clear expectations

13. Coherent performance management

CAPABILITY14. Right people and bench

15. Clear talent strategy

16. Succession management

17. Continuous learning

COMMITMENT18. Full engagement

19. Career systems

20. Diversity and inclusion

CHOICE & FOCUS7. Winning strategy

8. Consistent operating model

9. Effective organization structure

ACCOUNTABILITY & FAIRNESS10. Doable jobs

11. Fair rewards and benefits, aligned with strategy

PURPOSE & VISION3. Meaningful purpose, vision & goals

4. Aligned top team

5. Clear business model

6. Strong and adaptive culture

LEADERSHIP

OR

GA

NIZ

AT

ION

E

NA

BL

ER

SP

EO

PL

E

DR

IVE

RS


2. Linking Talent & Business Performance Discretionary Energy and Performance – Client example

• Company A

• Best-in-Class

• Company B

• Company C

• Company D

• Client

• Company F

EB

ITD

A M

arg

in %

Discretionary Energy Index

50 75 100

45

40

35

30

25

Median DE

Average

EBITDA•

Company E

13

Client’s

Capab V&P Com. Lead. A&F C&F Clarity

AdditionalEBIDTA

Margin %+2.4 +1.4 +0.9 +0.8 +0.8 +0.4 +0.1

Additional EBIDTA USD M

+898 +548 +333 +319 +294 +143 +54

1 2 4Potential profit improvement of $2.5b with ~ $900m through Capability

3+4.3 +2.8 +1.8 +1.7 +1.6 +0.8 +0.3

Life Science Sector

2. Linking Talent & Business Performance Discretionary Energy – Impact on performance

20%3 1.83 6.7

13% 0.80 8.7

Annualized Shareholder Returns (%)

Sharpe Ratio - Risk-Adjusted Returns

- Higher is better

Credit Ratings- Used to assess

sustainability of business performance

- Lower is better

Two portfolios constructed for High and Low DE

High DE final

value: 124.9k

Low DE final

value: 113.2k



15

4. SUPERIOR

ANALYTICS



TALENT & BUSINESS

PERFORMANCE

1. THE

FUTURE

IS NOW


What data?

3. Some Real Challenges Spectrum of data and analytics

16

AI - Machine Learning, neural network analysis

Distribution, ranking

Forecasting

Geospatial analytics

Networks analytics

Optimization

Probability

Reporting, visualization

Social media analytics

Sorting, rules engines

Statistics

Text analytics

What-if simulations / game theory

Alerts, risk management, turnover

Anticipation, prediction

Awareness building, feedback

Decisions, choices

Insight, foresight, learn

Needs anticipation, workforce planning

Negotiation

Recommendation, prescription

Talent decision, hiring, promotion, rewards, perform. management

Trade-offs analysis, investment

Graphs

GPS output

Machine-generate data, sensors, IoT

Raw data, observations

Scientific data, neuroscience, physics

Social media data, blogs, tweets, likes

Streaming, real-time continuous data

Structured data, tables, records

Time series

Text, survey verbatim

Unstructured data, human language, audio, video

Connected systems, cloud

Databases

Data warehouses

IT systems

Mobile, VR devices

Operational systems

Real-time

Reporting platforms

Self-service simulation platforms

Which platform? What analytics? How to utilize?

3. Some Real Challenges Separating the signal from the noise


Most of data scientists’ activities is about ‘cleaning’

and structuring data’.

OutcomeData analytics

3. Some Real challengesContinuous learning : package delivery client example


Delivery delays and traffic accidents.

More accidents in left turns.

Design new routes with very few left turns.

Drivers continue to turn left to ensure quick delivery.

High number of accidents continues.Behaviors have not changed.

Link district managers incentives to compliance with new recommended routes.

100% compliance.Meaningful reduction of accidents.Less delays.

Decision Behavior Data analytics Decision

‘Overconfident’ Financial Services leaders example

3. Some Real Challenges Data quality and bias: financial services leaders example

Comparison of Self-Others Gaps on Emotional and Social Competencies (ESCI).

Adapta

bili

ty

Conflic

t m

anagem

ent

Coachin

g,

mento

ring

Em

path

y

Em

otional s

elf

aw

are

ness

Inspirin

g le

ader

Influence

Org

aniz

ational

aw

are

ness

Positiv

e o

utlook

Team

Work

Achie

vem

ent

Financial Services

Other industries


‘Garbage in – garbage out.’

‘Correlation doesn't imply causation.’

‘AI-ML Black Box.’

Distorted, biased or skewed internal or self-reported data, influencing outcomes.

Spotty, fragmented, wrong, incomplete, not clean.

Costly data access and management. FSS leaders N= 1,021; Other industries leaders N=12,385.

Some data issues

3. Some Real Challenges Data analytics ethics


Data management

Modelling & algorithms

Insights & applications

MonetizationData sources

Data analytics value chain

Will we hit a point of

“knowing too much” about employees?

Can data analysis begin

to shape outcomes in

real time?

Can we avert misbehavior

and manipulation

before it happens?

New practices:Cyber-securityOff-limitsPIP - GDRPCulture


21

4. SUPERIOR

ANALYTICS



TALENT & BUSINESS

PERFORMANCE

1. THE

FUTURE

IS NOW


4. Superior Analytics Execution Talent managed as assets


4. Superior Analytics Execution Embracing data analytics for business performance

Fix

ed

min

dset

Ad

ap

tive

Own, past dataTraditional HR analysis

(e.g., retention)

Open, unstructured dataAdvanced analytics

Business optimization

StaticData as a

dormant asset

DisruptersData as a

competitive advantage

Followers Most organizations

today

Analytics capabilities

Learn

ing

ag

ilit

y

Structured data Search for insightsTalent optimization

Pro

acti

ve

23

Emerging:Discretionary EnergyPeople to Profits PipelineStrategic Workforce Planning v.2Integrated data and analyticsTalent Supply Chain analyticsAlways-on analytics; AI-ML

Today:Talent scorecardMarket calibrationHigh potential identificationLeadership & talent gapsSuccess driversEngagement and retention

4. Superior Analytics Execution Towards analytics v.2

24

4. Superior Analytics Execution

Data analytics has a competitive advantage

Executive Search candidates who rank in the top third of Korn Ferry

assessment are 1.8x more likely to be high performers on the job.

32%

48%

59%

Bottom third assessment Middle third assessment Top third assessment

Percent of group that were subsequently high performers on the job


Investment in science, data and analytics

4. Superior Analytics ExecutionLinking business performance to talent analytics


Identify critical business issues

Define

key data sources

Build analytical capabilities

Learn

from the insights.

Enact decisions based on analytically derived results

How can data analytics help you execute your strategy?

What data do you need?

Do you have the right analytics talent and organization?What analysis will give you the answers you need?

How will you use your analysis and insights for business impact?

Thank you.

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