© Copyright IBM Corporation 2015

2015 IBM 데이터 기반의 인사관리 세미나 : Talent Analytics Overview

Talent Analytics Overview

Seungyol Park (박승렬 상무)

Associate Partner

Business Analytics & Strategy Leader

IBM Global Business Services

1

Agenda

Key Trends

Advanced Talent Analytics Offerings

I. Talent Insights - HR 데이터 분석

II. Propensity Modeling – 경향분석 모델링

III. Workforce Mix Optimization – 인력구조 최적화

IV. Policy Optimization – 인사제도 최적화

V. Talent Analytics Roadmap - 로드맵

Lessons Learned

2

CEO들은 인재 관리의 중요성을 과거보다 더욱 더 크게 느끼고 있으나…

CEO의 직접적인 관여도가 높은 영역

Source: 1. IBM CHRO Study 2014 Question CEO5-What are the top priorities in your business strategy? (select FIVE without ranking); n=364 to 505 [CEO only] 2. Question B3–What are the top five areas you are personally involved in at an enterprise level?; n=907 [CEO only]

Previous 3 years

Next 3 years

58%

41%

핵심인재 확보1 에 대한 CEO의 전략적 우선순위

Customer experience management

Research and development

Talent management

eCommerce

+9%

+7%

+6%

+5%

3

… 여전히 많은 CEO는 CHRO를 전략적 조언을 구할 비즈니스 파트너로

인식하고 있지 않음

With the exception of the CFO, CEOs work as closely with

CHROs as they do with others in the C-suite1

33%

30%

27%

12%

CEO with CFO

CEO with CMO

CEO with CHRO

CEO with CIO

CEO with CSCO

74%

However, compared to other roles, CEOs are not turning to

CHROs for strategic guidance2

72% 63

%

42% 37

% 35

%

CFO CMO CIO CSCO CHRO

96%

CEO

Both CEOs and CHROs agree that the HR function is

perceived as more transactional and process oriented3

59% 64%

Provider of basic HR transactions

CEO CHRO

37%

27%

Strategic partner

Effective

CXOs’ Involvement in business strategy according to the CEOs

Collaboration level with CEO

Effective

Source: 1. IBM CHRO Study 2014 a) Question CEO2–Which senior executives are involved in formulating the organization’s business strategy?; n=701 [CEO only]

2. Question CEO2–Which senior executives are involved in formulating the organization’s business strategy?; n=701 [CEO only]

3. Question E13–How does your enterprise perceive the effectiveness of HR in the following areas?; n=887 [CEO only]; n=304 [CHRO only]

4

HR의 전략적 효과성 제고에 보다 더 많은 투자를 하는 글로벌 기업들은

Analytics를 활용하여 의미있는 비즈니스 효과를 거두고 있음

…and are seeing significant business

returns to their businesses

Analytic outperformers are more effective

in dealing with workforce challenges1…

Cumulated net benefit seen by IBM

enterprise delivered through

workforce analytics initiatives over

past decade

Improvement in gross profit margins

achieved by leading analytic

organizations2 4%

Source:

1. IBM GBS IBV CHRO Study 2014 Question CHRO2–How effective is your organization in addressing today’s workforce challenges?; n=31 to 137 [CHRO only]

2. Corporate Exec Board Analytics Survey 2013; Ad-hoc operational reporting denotes primarily descriptive analytics such as spread-sheet based manual analysis, benchmarking,

reporting of HR metrics /KPIs

Effectiveness in addressing workforce challenges

Employee engagement and commitment

Workforce productivity

Rapid development of workforce skills

Talent retention

Performance management evaluation

Talent management

41%

24%

50%

29%

52%

25%

53%

33%

53%

38%

72%

56%

Analytic Outperformers All others

Proven ROI% through workforce

analytics initiatives around talent

management, strategic workforce

planning, ROI-based HR policy

2x-5x

$100M+

5

기술의 발전과 행동과학의 발전이 결합하여, 보다 더 고도화된 Talent

Analytics 를 통한 의사결정 지원이 가능해지고 있음

Technology has changed the way, and the speed,

at which people communicate and connect

Insights on human behavior allow us to

understand what makes people excel

The combination of these two yields deeper analytics in support of better business decisions

6

Agenda

Key Trends

Advanced Talent Analytics Offerings

I. Talent Insights - HR 데이터 분석

II. Propensity Modeling – 경향분석 모델링

III. Workforce Mix Optimization – 인력구조 최적화

IV. Policy Optimization – 인사제도 최적화

V. Talent Analytics Roadmap - 로드맵

Lessons Learned

7

분석 역량의 각 발달 단계에 따라 실제 집중하고 기여하고자 하는

비즈니스 가치 및 효과는 차이가 있으며…

Time and Resources

Va

lue

an

d Im

pa

ct

Data Management o Consolidation of data

o Data quality and accuracy

Basic Reporting o Standard reporting that is reasonably automated

o Slice and dice’ data based on standard variables

Benchmarking o Key Performance Indicators (KPIs)

o Performance Measured against Best Practices

Analysis o Multi-dimensional analysis to better

understand business challenges

Advanced Analytics o Segmentation, Predictive modeling

and Optimization

Efficiency

Effectiveness

Growth

Cognitive Computing o Reasoning, Learning, Natural Language

8

… Advanced Analytics를 통해 HR 전략과 비즈니스 전략의 연계를

가능하게 하여 HR의 효과성을 높이고 비즈니스 성장에 기여할 수 있음

인력 구조/운영의 최적화

Most productive mix of job roles

Dynamic resource management

스킬 개발을 통한 생산성 증대

Targeted employees for learning

핵심 인재의 유지

ROI based compensation

Predicted attrition mitigation

전략적 비즈니스 우선 순위에 따른 채용

Strategic Financial & Resource Planning

Capacity & Productivity Management

Develop

Retain Manage

9

IBM Talent Analytics Offerings

Talent Insights (Powered by Watson Analytics)

Advanced Analytics & Behavior Science

Industry Specific Use Cases

Talent Analytics CoE

Offerings

Talent Insights

(HR 데이터 분석/예측)

Propensity Modelling

(경향분석 모델링)

Workforce Mix Optimization

(인력구조 최적화)

Policy Optimization

(인사제도 최적화)

Analytics Roadmap

(분석 로드맵)

10

Talent Insights (HR 데이터 분석/예측)

Place the power of sophisticated analytics directly into

the hands of HR professionals, enabling them to make strategic, fact-based decisions

11

Talent Insights (powered by Watson Analytics)

Talent Suite Data

Integrated information

services provide data

access and refinement to

your Talent Suite data

Tell a Story

Visualizations support your

decisions and communicate

results

Understands the

Language of HR

Automated intelligence

accelerates your ability to

answer questions

Think Ahead

Guided analytics reveals

insights and opportunities

directly within the Talent

Suite

Get answers to your workforce talent questions faster than ever before

Analysis is made easier & more accessible for HR professionals

Available as a new capability within IBM Kenexa Talent Suite

12

Prediction

13

Prediction

14

Propensity Modeling (경향분석 모델링)

Predict individual employee behavior and tailor effective HR actions for maximum corporate

benefits

15

이직 예측모델을 통해 과거 historical data를 분석하여, 이직 위험성이

높은 직원을 식별해내고, 연관된 위험 요인을 제시하며 대응 방안을 제시

2 1 Analyze historical patterns of high attrition

Identify employees with high propensity to leave

Historical population Active population

Vo

lun

tary

att

riti

on

rate

25%

15%

5%

Identify attrition drivers for every employee

3

$

Tenure

Demographic Career Velocity

Compensation

4 Align workforce policies and manager actions to minimize attrition drivers

$

Tenure

Demographic* Career Velocity

Compensation

+ $4K/yr Mentor or Career Discussion

Work with local manager Promotion

• 개별 직원 수준에서 이직 가능성을 예측하고, 이직 요인을 분석

• 이직 위험 요인에 맞는 맞춤형 대응 방안을 추천

• 이직 비용 감소를 위해 사전에 高 위험 직원에 대한 직접 보상 진행

※ 이직비용 : 채용/온보딩/교육 비용, (헤드헌팅) 수수료,

이직 전후 생산성 감소, 기회비용 상실 등

Business

Impact

16

Predictive model

8000만건 이상의 multi-source 데이터 분석을 통해, 이직 핵심

동인 파악

Turnover Assessment Tool

개별 약국 단위에서 이직으로 인한 매출액, 처방전, 고객

만족도에 대한 영향 요인 분석

Long term roadmap

이직을 최소화하기 위해, 각 지역/인력별 맞춤형 대응 방안 및 장기적 인력 유지 방안 제공

미국 Leading Retailer 기업 대상

이직율 높은 Target 직무 역할 수행자의 이직 동인 분석 수행

연간 약 $143M 의

이직 비용 손실 중

(for part-time pharmacy

techs and clerk cashiers)

17

Workforce Mix Optimization (인력 구조 최적화)

Derive resource and asset mix to support business

plan. Understand trade offs and risks. Strategic & tactical long planning or staffing focus

18

중국의 한 소매 물류 기업은 각각의 물류 지점별로 고객 변동 수요에 맞춰

인력 공급을 어떻게 최적화 할 것인가의 문제에 직면함

What about the influence of seasonality (e.g. holidays)?

Static workforce planning, instead of detailed daily planning, results in high idle times and times of too much workload

Leaves inter-shop effects unnoticed

Challenge in quantifying relation between workforce and revenue( i.e. Productivity)

Not optimizing workforce mix (e.g. Part time, Role)

Lack of visibility to what drives demand patterns

Influence of external factors (e.g. Competitors, Advertisement) on workforce planning for individual store?

Neglects intraday workload differences (e.g. Hourly peak demand)

19

이에 분석 기법을 활용하여, 각 지점별 인력 수요 패턴 및 핵심 동인을

분석하고, 각 역할별 비용/가치를 고려한 최적의 인력 mix 구조를 도출함

Bu

sin

ess

Qu

estio

n

Measure the business value of the optimal workforce

What is the business value for change?

Design Optimal workforce given demand and business constraints

How to optimize workforce size and mix to meet demands?

Analytics helps understand demand patterns in workload at a store

What drives demands?

Ou

tco

me

s

Classify stores based on demand patterns and top demand drivers

Recommend optimal workforce size and mix (e.g. part time, role)

1 2 3

Measure business impact of recommendations in stores

Profitability increase through optimization of workforce mix to meet store demands (e.g. cost savings and reduction in lost sales)

Increased accuracy and effectiveness in workforce planning down to hourly basis

Increase in analytics capability to tackle complex business issues and unlock values

Clie

nt

Be

ne

fits

20

Workforce Mix Analytics는 구조화된 데이터 마이닝 Approach를 통해

수행할 수 있음

Business Understanding Data Preparation Data Understanding

How to harmonize and cleanse millions of rows of data from disparate sources?

Data

Dictionary

What hypothesis can we make?

What is the business problem, objective

and success criteria?

Stakeholder Interviews

Focus Groups

Project Kick-Off

Workshops

Surveys

Expertise in transforming big

data into analyzable dataset

Industry Expertise and Decades of

Analytics Transformation Experience

• Part-time workers helps fulfill peak demand

• Part-time workers can decrease idle time from

full-time workers

• ....

What data is required to validate

hypothesis?

IBM Big Data Analytics Data

Platform (e.g. Netezza)

IBM-unique approaches/tools

Achieve cost savings$ by optimizing

workforce mix

Business

Objective

Demographics Financials

Geography

Hourly Demand

Store Specifics

Integrated Data for Analytics

Workforce

Product Volumes

Modeling & Analysis Roadmap for Deployment Validate and Feedback

How do we embed model insights in

business operations and further scale to

reap benefits?

Close partnership model to ensure

long-term implementation success

Sample tool prototype

What is the insight and impact?

How do we test and validate the model?

Proof-of-value approach to test

model approach and get feedback

Part of the PoV

approach includes

business case

evaluation

Given data, what model and assumptions

should we employ?

IBM modeling tools and techniques

(e.g SPSS, iLOG)

21

peak-demand 동인 분석에

따른 각 지점별 workforce

mix 최적 예측 모델 수립

예상 누적 비용 절감액

(2015-2017)

서로 다른 시스템의

소스를 통해 약

1억건 이상의 데이터

추출/분석

중국의 Leading Logistics 기업 대상

Workforce Mix Optimization

~$20M

22

Policy Optimization (인사 제도 최적화)

Link HR policies to business benefit and support

planning, investment, and transformation

23

ROI 기반의 보상 정책 수립을 통해, 이직을 최소화하고 순이익을 극대화할

수 있는 적절한 수준의 개인별/그룹별 보상 투자 재원을 결정함

2 1 Establish

relationship between

voluntary attrition

(VA) rate

Assign population

to different comp

clusters

Optimize group comp

attrition profile

3 4 Allocate individual

investments and

estimate benefit

IBM ILOG CPLEX Optimizes net benefit of compensation investment

Maximum net benefit

and minimum viable

attrition is calculated (I) Target attrition

(II) Maximum benefit

(III) Minimum attrition

1

2

Confidence level boundary

Current attrition rate

Cost

Savings

24

IBM optimized compensation policy

for maximum business impact

Business problem: Indirect impact of changes in ‘labor costs’

budgets may look as savings on the financial plan but actually result

in negative net benefit

Solution: Identify optimal investment levels for employee clusters

based on ROI criteria considering cluster responsiveness to cash

incentives, internal and external factors, business input and target

turnover rate

Solution Components:

$44M Net benefits July 2013 – June 2014

185% ROI for July 2013 – June 2014

25

Analytics Roadmap (분석 로드맵)

Develop innovative and transformative analytics

talent and growth engines within the client

organization

26

HR Analytics의 로드맵 수립을 위해서는, 비즈니스의 핵심 과제에 대한

이해와 이와 연계된 HR 우선 과제에 대한 인식으로부터 출발해야 함

Identify Right Talent

& Winning Teams Increase Workforce

Productivity

Provide the Right

incentives

Transform the

Workforce

What is the ROI of HR

investments? (e.g.

Compensation, learning,

promotions)

How to transform my

workforce strategically to

meet future business

demands and labor shifts?

What defines top talent?

How to identify, hire,

develop and retain such

talent?

How to measure productivity

accurately so it reflects true

business performance?

What are the levers to increase

productivity?

27

평가에 따라 선정된 HR Analytics Agenda는 수행 복잡도와 소요 기간 및

각 과제 간의 상호 연계성 및 시너지에 따른 구분으로 Agenda Map을 구성

Time

Social Media

Analytics

Retention

Labor Trans-

formation

Productivity

Skills /

Optimal Mix

Low

Impact

High

Impact

HR Analytics Agenda

Co

mp

lex

ity

Define & prioritize capabilities to enable

and optimize through analytics

Dependencies

Synergies

Benefit/Cost/Risk Benefit/Cost/Risk

Analytics Agenda

Identify Right Talent

& Winning Teams

Transform the

Workforce

Increase

Productivity

Provide the Right

incentives

Strategic Fit Dependencies /

Synergies

ROI based

HR Policy

Top Talent

Retention

Optimal Skill

Mix

Workforce

Optimization

Learning

Analytics

Predictive

Hiring

Capacity &

Productivity

Management

Compensation

Analytics

Illustrative

Strategic

WF Planning

WF Scheduling

Optimization

28

Agenda

Key Trends

Advanced Talent Analytics Offerings

I. Talent Insights - HR 데이터 분석

II. Propensity Modeling – 경향분석 모델링

III. Workforce Mix Optimization – 인력구조 최적화

IV. Policy Optimization – 인사제도 최적화

V. Talent Analytics Roadmap - 로드맵

Lessons Learned

29

Key Lessons Learned from IBM advanced analytics transformation

Executive sponsorship and integrated operating and funding

models enable success

Business-issue driven analytics agenda is key to drive value

Do not wait for perfect data to start advanced analytics

Start small, show the value and scale up

End to end ownership of the transformation ensures

business value is delivered

Analytics driven culture & transformation is a journey

30

Enabling success through innovation