Making a Difference through Analytics

© 2018 TAS Consulting Partner I All Rights Reserved
www.tas-consultingpartner.com
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Make a Difference through Analytics
2018 HR Forum
Personnel Management Association of Thailand

AMOUNT OF DATA WE ARE GENERATING IS IMMENSELY
THIS PROVIDES UNPRECEDENTED OPPORTUNITIES
hours of video uploaded on YouTube
million emails are sent,
million photos are viewed
tweets sent
million queries made on Google
100
200
20
300,000
2.5
90%
80%
of all data ever created,
was created in the past 2 years.
of the data generated
by organization is unstructured
and
These
things
happen
every
one
minute

BIG DATA IS NOT ABOUT BIG VOLUME OF DATA BUT
COMPLEXITY OF SIZE, SPEED, FORMAT AND SOURCE
Source : Introduction to Big Data, Xiaomeng Su, NTNU
3V
V E L O C I T Y
from batch processing, to near-real time and
real time streaming.
V O L U M E from megabytes, gigabytes, terabytes, petabytes,
exabytes to zettabyte.
VAR I E T Y from different data sources, various formats,
structured, semi-structured and unstructured data

THERE ARE DIFFERENT TYPE OF DATA THAT COMPANIES
CAN USE TO IMPROVE THE WAY THEY DO BUSINESS
*including semi-structured data
I N T E R
N A L
Source : Structured data vs. Unstructured data; What’s the Difference, Timothy King, 2018
S T R U C T U R E D
Structured data is traditional data,
consisting mainly of text files that include
very well-organized information. It is
stored inside of a data warehouse where
it can be pulled for analysis
U N S T R U C T U R E D *
Unstructured data is emerging data
sources which are made up largely of
streaming data coming from social media
platforms, mobile applications, location
services, and IoT Technologies.
E X T E R N A L E X T E R N A L
External data is the infinite array of
information exist outside the organization.
This can be publicly available or privately
owned by a third party meaning that the
company have to pay for access.
Source : Data Strategy, Bernard Marr, 2017
I N T E R N A L
Internal data refers to all information the
company currently has or has potential to
collect. It is privates or proprietary data that is
owned by the company meaning that the
company control access to the data

70 4
67
62
57
50
33
32
23
16
6
6
7
9
20
30
30
44
%
I n c r e a s e D e c r e a s eS t a y t h e S a m e
Online collaboration platform
Work based social media
Instant messaging
Social messaging apps
Personal social media
e-mails
Phone/voice mail
Face to face meeting
text
GROWING OF NEW COMMUNICATION BEHAVIORS AND
TOOLS MULTIPLYING VOLUME OF UNSTRUCTURED DATA
Source : Global Human Capital Trends, Deloitte University Press, 2018
Expected use of communications channels in the next three to five years

HR IS MOST FAMILIAR WITH RELATIONAL DATABASE
HELD IN ITS ENTERPRISE HR INFORMATION SYSTEM
S T R U C T U R E D U N S T R U C T U R E D
E X T E R N A L
I N T E R N A L
Economic outlooks
Security Intelligence Report
State of workforce & labor market
Unemployment rate, Cost of living index
Salary survey
Company reputation survey made by 3rd party
Documents, presentation, proposal, descriptive report
Content of e-mail correspondence
Open comment in opinion survey
Instant message conversation and SMS message
Record of interaction between HR services desks and
employee, Call center agent and customer etc.
CCTV video camera
Organization network interaction
Photo employee posted/shared on company portal
Publisher, aggregator database, wiki
Article, research, white paper
Comment on blog and social media post e.g.
Facebook, LinkedIn, Twitter, Glassdoor etc.
Photo and video posted on YouTube and social
media website
Sensor data e.g. GPS location detection
Business performance (P&L, Financial statement)
Customer Net Promoter Score
Production Efficiencies
Recruitment data
Employee personal details ; demographic information,
education, competencies, skills
Employee employment condition ; positon, unit, job
grade, salary, benefit & welfare , performance review
training record, training evaluation
Employee opinion survey

© 2018 TAS Consulting Partner I All Rights Reserved Source : Is Data Science Still on the Rise, DataCareer, 2017
PEOPLE ARE KEEN TO UNDERSTAND WHAT BIG DATA IS
A N D H O W T H E Y C O U L D M A K E T H E B E S T U S E O F I T
G O O G L E T R E N D S K E Y W O R D S
W O R L D W I D E
2 0 0 9 – 2 0 1 7
2009 2010 2011 2012 2013 2014 2015 2016 2017
100
Interestovertime
Big Data
Artificial
Intelligence
Machine
Learning
Business
Intelligence
Data Science

Qualitative
Analytics
Business
Case Study
P r e s e n t F u t u r eP a s t
Source : adapted from a Visual Guide to Analytics, Data Sciences, BI, ML and AI, Ilaya Vachanov, 2018
Preliminary
Data Report
Sale Forecasting Optimization
of Operation
Digital Signal
Processing
Reporting
with Visuals
Creating
Dashboards
BUSINESS
INTELLIGENCE
BIG DATA
ADVANCED
ANALYTICS
BUSINESS
ANALYTICS
WE NEED TO UNDERSTAND MEANING AND RELATIONSHIP
AMONG THE FIELDS RELATED BIG DATA AND ANALYTICS
DATA
ANALYTICS
Creating
Real-time
Dashboard AIMACHINE
LEARNING
Client Retention
Fraud Prevention
Employee Retention
DATA
SCIENCES

WHEN DATA IS BECOMING MORE AND MORE COMPLEX,
BUSINESS REQUIRES MORE ADVANCED ANALTICS TOOLS
Source : Definition of Business Intelligence, Margaret Rouse on TechTarget,2017
Answ ers the
Questions
Includes
BUSINESS
INTELLIGENCE
What happened?
When
Who
How many?
Reporting (KPI, Metrics)
Automated monitoring and alerting
Dashboards
Scorecards
OLAP
Ad-hoc query
ADVANCED
ANALYTICS
Why did it happen?
Will it happen again?
What will happen if we change x?
What else does the data tell us ?
Statistical or quantitative analysis
Data mining
Predictive modeling
Multivariate testing
Big data analytics
Text analytics

BUSINESS ANALYTICS START FROM SUPPLY CHAIN,THEN
FINANCE AND CUSTOMER…NOW IT IS COMING TO HR
Source : Big data in HR : Why it is here and what it mean, Josh Bersin, 2012
F I N A N C I A L
E C O N O M Y
I N D U S T R I A L
E C O N O M Y
C U S T O M E R
E C O N O M Y
T A L E N T
E C O N O M Y
Logistics and
Supply Chain
Analytics
Financial &
Budgeting
Analytics
Integrated
Supply Chain
Analytics
Integrated ERP
and Financial
Analytics
Customer
Analytics CRM,
Data warehouse
Customer
segmentation
shopping basket
Web behavior
analytics
Predictive
customer
behaviors
Recruiting, Learning,
Performance Measurement
Integrated
Talent Management
Workforce Planning
Business-driven
Talent Analytics
Predictive
Talent Models
HR Analytics

WHY HR?-BECAUSE INTANGIBLE ASSESTS CONTINUES
TO BE THE GREATEST ASSET TO TODAY’S COMPANIES
1 9 7 5 1 9 8 5 1 9 9 5 2 0 0 5 2 0 1 5
8 4 %
8 0
6 8
3 2
1 7
COM PONENTS OF S&P 500 M ARKET VALUE I N TA N G I B L E
Source : Ocean Tomo LLC, January 2015
Brand, Goodwill
Patents, Copyright, Trademark
Customer Database
Licenses, Franchise
Knowledge, Trade secret

2006 07 08 09 10 11 12 13 14 15 16 2017
41
40
31
30
34
31
34 35 36
38
40
45%
Source : 2018 Talent Shortage Survey, Manpower Group, 2018
WHILE WORKFORCE WHO CREATE COMPANY’S VALUE IS
INCREASINGLY HARD TO FIND AND MUCH SOUGHT AFTER
GLOBAL TALENT SHORTAGE
REACH 12 -YEAR HIGH
IT SEEMS THAT HR HAVEN’T
MADE BETTER USE OF DATA YET.
Source : HR joins the Analytic Revolution, Harvard Business Review, 2014
54 %
47
44
37
29
27
Inaccurate, inconsistent or
hard-to-access data requiring too
much manual manipulation
Lack of analytic acumen or
skills among HR professional
Lack of adequate investment in
necessary People Analytics System
Lack of perceived value of a data-
driven culture
Lack of support or expectations by
C-suits executives
HR does not know how to talk about
HR data to relate it to business
outcome

CEOs DON’T WANT DATA ONLY TO UNDERSTAND WHAT
HAPPENS BUT EVIDENCE TO AID THEIR DECISONS
Information received is
comprehensive
Cost of
employee
turnover
Return
on investment
of human
capital
Assessment
of individual
advancement
Labor
cost
Employees’
views and
needs
Staff
productivity
Do not receive information
Not adequate
Adequate
but would like more
Source : 15th Annual Global CEO Survey, Price Waterhouse Cooper 2012
INFORMATION GAP
CEOs believe information is important but
do not receive comprehensive reports

HR Using Data
to Provide
Talent Report
HR Using Analytics
to Improve
Business Decisions
Purpose of report is to
provide talent information
Information provided is
driven by leader requests
and data availability
Reports provide leaders
with talent metrics
Purpose of analytic is to
improve business decisions
Analysis and insights
link explicitly to evolving
business challenges
Insights provide implications
for business outcome
Source : Innovations in Talent Analytics, CEB, 2016
HR NEEDS TO MOVE BEYOND TALENT MATRICS TO
DISCOVERING MEANINGFUL PATTERN IN TALENT DATA

NAME SOURCES METHODS RESULTS
N M S R J W I G T A O P M R T
Manee I G T A O 2 2 2 2
Somporn E I G A O 3 2 3 2
Mana N I T O 2 2 2 2
Bua M I T 2 2 1 1
Somchai J I T 1 1 1 2
Boonme W I G T A O 2 1 1 2
Somboon S A O 3 2 3 2
Pawinee E I G T A O 3 3 2 2
Tossapon N I T 1 1 1 1
Piya N I G T 1 2 2 2
N = Newspaper
M = Professional Magazine
S = Search firm
R = Referral
J = Job board
W = Walk-in
I = Personal interview
G = Group interview
T = Test
A = Assessment
O= Onboarding
P = Performance
M = Merit increase
R = Potential rating
3 = High
1 = Low
T = Tenure 1 = Gone
2 = Stayed
Source : adapted from Staffing Process Analysis, Predictive Analytics for HR, Jac Fitz-enz & John R mattox II, 2014
A N A LY T I C I S A B O U T U N D E R S TA N D I N G T H E PA RT ’ S
I N T E R R E L AT I O N S H I P S A N D I N T E R D E P E N D E N C I E S

JAN MAR MAY JUL SEP NOV
SHARK
ATTACKS
ICE CREAM
SALES
But they are not caused by each others
Probably they are caused by good
weather with lot of people at the
beach both eating ice cream
and having a swim in the sea
BUT NOT A CAUSATION
IT’S TEMPTING TO DRAW CONCLUSION BUT REMEMBER
THAT STATISTICS DON’T TELL THE COMPLETE STORY
Both ice cream sales and shark attacks
increase when the weather is hot and sunny
CORRELATION COEFFICIENT

Descriptive
STATISTICS
Includes
used to
includes
measure of
Central tendency
Correlation
is
is
is
Degree and direction
of relationship
between two
variables
Arithmetic average
Most often used
Sensitive to extreme
Most frequent score
describes
Sample
about
Sample
goes beyond
used to
Draw
conclusion
Interpret data
Determine
statistical
significance
taken from
about
based on
Mean
Mode
Median
Range
Standard
Deviation
Correlation
Coefficient
Variability
Inferential
Prediction
allows
Cause-effect
conclusion
does not allow
Source : adapted from process map created by IHMC CampTools.
t-test
ANOVA
Regression
used to Compare
two means
includes used to Compare
multiple means
used to Make prediction
about outcome
variable based
on knowledge of
predictor variable
A S A N O N - D ATA G E E K , H R N E E D S TO U N D E R S TA N D
BASIC STATISTICAL TYPES & FACTORS FOR CHOOSING THEM
Summarize and
organize data
Center of distribution
Population
Low probability
of observed
result due
to chance
depends on within or between
S group comparison
experimental or
non-experimental
qualitative or
quantitative variable
Can be displayed as Graphical
diagram

FIRST THING FIRST, LET’S UNDERSTAND THE TYPE OF
VARIABLE AS IT RELATES TO THE CHOICES OF METHOD
I N T E R VA L R AT I ON O M I N A L O R D I N A L
Quali - tative Quali / Quanti – tativeNature Quanti - tative Quanti - tative
Order No Yes Yes Yes
Distance n.a Not equal Equal Equal
True Zero n.a n.a No Yes
Source : adapted from The Analytics Lifecycle Toolkit, Gregory S. Nelson, 2018
C A T E G O R I C A L C O N T I N U O U S

M AKING
COM PARISON
M EASURING
ASSOCIATIONS
M AKING
PREDICTIONS
DETECTING
PATTERNS
THEN, WE MUST REALIZE THAT DIFFERENT PURPOSE
W I L L R E Q U I R E D I F F E R E N T S TAT I S T I C A L M E T H O D
© 2018 TAS Consulting Partner I All Rights Reserved Source : The Analytics Lifecycle Toolkit, Gregory S. Nelson, 2018
Illustration by Top Employer Institute and Bright & Company

CORRELATION VS. REGRESSION – THE TWO ANALYSIS
THAT IS COMMONLY USED BUT OFTEN MISUNDERSTOOD
Meaning Usage Indicate Objective
Is a statistical
measure which
determines
co-relationship
or association
of two variables
Describes how
an independent
variable is
numerically
related to
the dependent
variable
Represent
linear relationship
between two
variables
X or Y
no difference
Fit a best line
and estimate
one variable on
basis of another
variable
X on Y is different
from Y on X
Correlation
coefficient indicate
the extent to which
two variables
move together
Regression
indicate the impact
of a unit change in
the known variable
on the estimated
variable
Find a numerical
value expression
relationship
between variables
Estimate value
of random
variable on the
basis of values
of fixed variable
C O R R E L A T I O N
R E G R E S S I O N
Source : Difference Between Correlations and Regression, Key Differences webpage, 2016

GOI N G B E Y ON D E XA MI N I N G R E L AT I ON S H I P A MON G
VA R I A B L E S TO E X A MI N I N G MU LT I P L E H Y P OT H E S I S
Source : Finding Training Value, Nick Bontis mentioned in Predictive Analytics for Human Resources, Jac Fitz-enz, 2014
Using “Structural Equation Modeling” to develop “Predictive Learning Impact Model”
Worthwhile
investment
Courseware
Quality
Instructor
Effectiveness
Perceived
Future
Business
Results
Perceived
Future Job
Impact
Business
Results in
60 days
Job Impact
in 60 days
Individual
Learning
R2
= 59.2%
R2
= 40.0%
0.563
0.263
0.077
0.085
0.571
0.420
0.556 0.083
0.625
0.337
0.483 0.189
0.592

USING ANALYTICS TO PROVE THAT THE PREVIALING
ASSUMTION WAS WRONG YIELDED FRUITFUL OUTCOME
Source : The Datafication of HR, Josh Bersin, 2014
What Matter to High Performing Sales Candidates
The company’s assumption was
College degree or
reputation of colleague
Grade point average
Quality of references
In fact, what matter are
Lack of typos or
misspelling in resume
Successful experience
selling autos and real estate
Completing degree-
which one did not matter
+US$ 4m
of new revenue in
the first six months

BRANCH 1 BRANCH 2 BRANCH 3
O R G A N I Z AT I O N N E T W O R K A N A LY S I S H E L P I M P R O V E S
PERFORMANCE GAP BETWEEN THREE BANK BRANCHES
+US$ 1bio
sale increase
11% within one year
Communication networks of three bank branches
Branch 1 has the highest performance,
Branch 2 has the lowest performance, and
Branch 3 has a high performing core with
new employees that haven’t been socially
integrated into the larger team
Source : www.humanyze.com,2018

R E VA M P I N G L I S T E N I N G C H A N N E L S H A S P R O V E N T O
B E T T E R U N D E R S TA N D I N G E M P L O Y E E E X P E R I E N C E S
Transformation of Intuit’s Listening Strategy
FROM
A large annual survey with
100+ questions
80+ active employee surveys
company wide, not including
rogue “survey monkey”
Survey included nearly
every question the company
could think of
Analyzing data and
producing report was slow,
manual process
“HR Care”* data was siloed
Shorter,
more frequent
pulse surveys
Fewer, broader questions
that let employee decide what
important for company to know
Surveys structured employee
sentiments and behaviors
Analyses leverage technology
for quicker access to insight
“HR Care” data integrated
as listening channel
Improving
EX
*measure employees’ behaviors and interactions with HR processes (e.g., filling out expense forms, updating personal details) in order to grasp how employees feel during day-to-day
interactions with these processes. Finally, the company also integrated what it calls “HR Care Data” (data resulting from HR tickets or HR service delivery) into the larger listening strategy.
TO

INCREASING NUMBER OF SUCCESS STORIES ON HOW
THE COMPANIES COULD LEVERAGE PEOPLE ANALYTICS
Age and
business
performance
A 2009 study conducted by Lancaster university management
school found that the presence of older employee (aged over 60)
improve customer satisfaction and consequently had a major
impact on company business performance
Contributing
factors and
successful
hiring
People analytics team recommended Goggle to reduce number
of interviews – no more than four, and get rid of problem solving
question such as “How many golf balls would fill in an aircraft?”,
they also found that grade, and degree from big name schools
do not guarantee employee performance quality at work.
As a result, proportion of people without any colleague
education at Google has increased over time.
Source : People Analytics in the Era of Big Data, Jean Paul Isson, Jesse S. Harriott, 2016
Characteristics
and job
performance
An analytics start-up Evolv helped Xerox reduce call center
turnover as much as 20% by gathering and study data on the
characteristics and job performance of front-line employee. Evolv
found that employee without call center experience were just
successful as those who had it, allowing Xerox to broaden
candidate pool. Creative personality stay longer than those with
inquisitive personalities. Also those who are active on at least one
but not more than four social channel has better chance to be
successful

THE ANALYTIC VALUE CHAIN @ GOOGLE PRACTICALLY
CHANGE OPINION AND MYTH TO INSIGHT AND ACTION
INSIGHT
ACTION
METRICS
OPINION
Process or policy change; new initiatives
Leads to action; influences decision makers
Identifies relationships, trends or special populations
Ratios, counts; trendable, but audience gets numb over time
Structured, but raw; now easily digestible
“gut feel”, “based on experience”, “I just know”
Source : People Analytics : Using data to drive HR strategy and Action, Kathryn Dekas, YouTube, 2011
DATA
ANALYSIS

OPINION
ACTION
Launch program to train people manager
Managers don’t impact Googlers performance
INSIGHT
List of eight behaviors that great managers exhibits
METRICS
They knew the most Googlers had favorably rated their managers
They knew how many manager they had
DATA
ANALYSIS
They discovered that good manager has statistically significant impact
on turnover and performance of team than struggling manager
EIGHT BEHAVIORS THAT GOOGLE’S GREAT MANAGERS
MUST EXHIBIT IS AN OUTCOME OF PEOPLE ANALYTIC
Source : People Analytics : Using data to drive HR strategy and Action, Kathryn Dekas, YouTube, 2011

Value Level
Source : Data Analytics Level, Predictive Analytics for Human Resources, J. Fitz - enz and Jonh R Mattox II,, 2014
PRESCRIPTIVE
PREDICTIVE
DESCRIPTIVE
Level 5 : Evaluate
Level4 : Model
Level 3 : Relate
Level 2 : Display
Level 1 : Organize
Collect data and organizing human capital data into database and validate accuracy
Need to ensure that traditional rational data is designed for analytics
Develop dashboard to satisfy internal customer which reporting degrees of performance
Though it doesn’t speak to the future but can reveal possibility for improvement
Look for impactful external and internal forces affecting the organization
Show effect of interaction among human, structural and relational capital
Design predictive experiment to connect people, policies, process & performance
Describe expected patter of relationship to uncover correlation or causation
Apply statistical or other methodology to validate predictive model’s validity
Show top line and bottom line change that increase all stakeholders'’ value
THE FURTHER ORGANIZATION ADVANCE ON MATURITY,
THE MORE VALUE ORGANIZATION WOULD GAIN

FOUR LEVELS OF PEOPLE ANALYTICS MATURITY THAT
DIFFERENTIATE OUTSTANDING COMPANY FROM OTHERS
Source : High Impact People Analytics, Deloitte 2017
LEVEL 1 : FRAGMENTED & UNSUPPORTED
LEVEL 2 : CONSOLIDATING & BUILDING
LEVEL 3 : ACCESSIBLE & UTILIZED
LEVEL 4 : INSTITUTIONALIZED & BUSINESS INTEGRATED
Use of advanced real-time, AI-aided tools & technology to collect, integrate & analyze data
PA integrated into talent decision and everyday work,
Cross-functional or centralized PA team, All HR is highly data fluent
Use of multiple “Listening Channel” & advanced tool and technology to collect , integrate & analyze data.
PA focus shift from HR to business goals, Sharing data and insight made broadly.
Larger centralized PA team, all HR is moderately data fluent.
More frequent & timely data-gathering, focus on creating a “Single Source of Truth” by building a data warehouse
Time and effort spent addressing HR reporting needs
Dedicated PA leaders to build a centralized team to mainly serve HR and sometime partnership with business,
Sporadic & reactive data gathering with limited or no capacity for data integration
Intuition, experience & precedence drive decision rather than data insight Data not considered as value-driver
A few disconnected, PA focused role across the organization.

GENERATING MAXIMUM VALUE THROUGH ANALYTIC
NEED MORE THAN DATA AND STATISTICAL TECHNIQUES
DATA
30%
Gather, clean and
connect disparate data
Source :Carl Schleyer, mentioned in Process Analysis, Predictive Analytics for HR, Jac Fitz-enz & John R mattox II, 2014
ANALYSIS
15%
Craft and test
statistical model
STAKEHOLDERING
5%
Collect key hypothesis
from executives
STORYTELLING
20%
Explain what the insight
mean and how to take
them into action
IMPLEMENTATION
20%
Take insight into actions
EMBEDMENT
10%
Celebrate and
sustain the
momentum

Result
Activation
Analytics Model
Development
Data
Sense Making
Problem
Framing
What Understand
Why Observe
How Contextualize
Explore
Describe
Explain
Predict
Optimize
Share Storytelling
Test
Apply
Pilot
Operationalize
Source :Analytics Life Cycle Toolkit, Gregory S. Nelson, 2018
BEGIN WITH DEFINITON OF PROBLEM AND CLOSE LOOP
W H E N A N A LY T I C S I N S I G H T A R E O P E R AT I O N A L I Z E D

READINESS AND IMPORTANCE
R e a d i n e s s I m p o r t a n c e
46 85
42 85
37 84
49 84
46 85
37 77
51 77
31 72
34 69
30 65
ABILITY OF HR TO OPTIMIZE PEOPLE DATA AND ANALYTIC
TO GENERATE RICH OPPORTUNITIES IS STILL EVOLVING
2018 Human Capital Trends
Symphonic c-suit
People data
From career to
experiences
Well-being
Hyper-connected
workplace
New rewards
Citizenship and
social impact
AI, robotics &
automation
Longevity
dividend
Workforce
ecosystem

Symphonic c-suit
People data
From career to
experiences
Well-being
Hyper-connected
workplace
New rewards
Citizenship and
social impact
AI, robotics &
automation
Longevity
dividend
Workforce
ecosystem
ASIA PACIFIC VS. GLOBAL
ASIA IS SLIGHTLY BEHIND THE CURVE BUT CATCHING UP
WHILE SIZE DOES MATTER IN ADOPTION THE ANALYTICS
ORGANIZATION SIZE
global
5 0 5 0 I m p o r t a n c eI m p o r t a n c e
Asia pacificGlobal
Large 10,000+
Small 1,000 and fewer
Medium
<10,000 - 1000

INVESTMENT IN BUILDIG ANALYTIC CAPABILITY IS MERELY
INSUFFICIENT, EXISTING HR TEAM IS BEING STRETCHED
Most companies use
internal skills
Yes
No
Are external consultants used to
supplement skill sets?
42
58
HR experiences dominates
People Analytic team
What’s the experience of PA team?
81
50
46
44
26
HR
IT, System
Data
Statistics
Mathematic
Support
Others
%
Source : HR Join the Analytics Revolution, Harvard Business Review, 2014
%
Investment in people analytics
What’ s the actions taken in building analytics capability
Source : Human Capital Analytics Survey, i4cp, 2016
Allocate HR budget for
analytics software/solution
30
26
16
9
9
6
34
%
Increased funding to develop
HR analytics expertise
Approved new data and
analytics positions for HR
Hired a CHRO with a strong
business or finance background
Moved workforce analytics
out of HR
Hired a CHRO with a strong
data and analytic background
Outsourced workforce analytics
21
None

HR PRIMARILY APPLY BASIC DATA ANALYSIS TECHNIQUES
TO PLAN, DEVELOP AND RETAIN THEIR CRITICAL TALENT
Plan, Acquire, Develop
and Retain
How important is PA to decision
making in these areas?
67 %
65
64
62
62
61
59
46
45
48
Leadership
Development
Talent
Retention
Workforce
Planning
Talent
Acquisition
Engagement
Training &
Development
Performance
Management
Compensation
Diversity
Organization
Design
239 807
750
562
334
273
348
568Basic data
analytics e.g.
means, medians,
ranges, percentiles
Advanced
multivariate model
e.g. structural
equation modeling
Intermediate data
analysis e.g.
correlation,
standard deviation
Basic multivariate
model e.g. factor
analysis
regression
HR are more likely to be using basis
analytical techniques
How often do you undertaken the following in your
current day job
Source : People Analytics Driving Business Performance with People Data, CIPD, 2018
Always/Often Rarely/Never

ORGANIZATIONS ARE KEEN TO EXPLORE WHAT DRIVE
PRODUCTIVITY, QUALITY AND IMPROVE ATTRACTION
Predictive Relationship
Of the following predictive relationships, which are being
explored now and which do you plan to explore in the future?
62
49 48 48
24
39
23
39
18
35
16
15 15
34
11
24
6
11
6
5 5
16
65
now
future
Job satisfaction vs. Retention
Engagement vs. Productivity
Engagement vs. Quality
Culture vs. Productivity
Job satisfaction vs. Customer Satisfaction
Engagement vs. Safety
Job satisfaction vs. Attraction
Stress vs. Productivity
Ethic vs. Profit
Compensation
vs. Retention
Conflict vs.
Productivity
Performance
vs. Retention
%

ANALYTIC IS NOT ONE -TIME PROJECT BUT A JOURNEY
THAT REQUIRE LONG -TERM COMMITMENT TO SUCCESSS
Source : Secrets of Analytic Leaders, Wyne Eckerson, 2013
A R C H I T
E C T U R E
Top-down
Bottom-up
Sandboxes
P R O C E S S
Development Method
Project Management
Cross-functional
Collaboration
O R G A N I
Z AT I O N
Embedded Analysts
Analytical CoE
Business-oriented BI
P E O P L E
Data Developer
Analyst
Power Users
C U L T U R E
Fact-based Decision
Performance
Measurement
Data Treated as
Corporate asset
D AT A
Structured
Unstructured
Internal
External

H R M U S T H AV E C L E A R S T R AT E G Y F O R T H E U S E O F
PEOPLE ANALYTICS IN LINE WITH BUSINESS STRATEGY
36
31
27
22
18
17
12
12
10
9
8
7
6
3
%Finance
IT
Marketing
Operations
Sales
Executive
Customer Services
Product Development
Risk , Compliance
Others
Supply Chain
e-commerce
HR
Logistics
HR is lagged behind others
Source : What it Takes to be Data Driven ,Fern Halper & David Stodder, 2017
Which department are the most advanced
in their ability to use data and analytics ?
Involve senior management
in people analytics initiatives
Business leader must understand the
potential of people analytics and is
convinced that the analysis would help
them achieve their strategic objectives
Perform people analytics
with proven business impact
Statistical analysis from people data
combined with business data must
enable HR to predict possible business
improvement or lead to disruptive idea.
Articulate strategy for
people analytics
It must be made clearly in HR strategic
intent that data-driven HR is a key
factor for creating business impact.
Source : HR Analytics and Reports Study, Top Employer Institutes and Bright & Company, 2016

READILY AVAILABLE AND HIGH QUALITY OF DATA IS
THE PREREQUISITE OF SUCCESSFUL PEOPLE ANALYTICS
Front runnerPractitionerStarter
Data basics a challenge
for most of organization
41 52 87 33 44 80% %
41 69 73 % 64 81 80 %
HR data is
highly accurate
HR data is
highly accessible
Easy access to
business data
Policy for use
personal data
Source : HR Analytics and Reports Study, Top Employer Institutes and Bright & Company, 2016
Properly governed
Process of data privacy and governance
are consults with all stakeholders
involved and documented clearly before
start collecting and analyzing data
Easily accessible
HR data and business data must be
easily accessible by all parties that
need access. Data infrastructure must
be linked to one another
Has a good quality of Data
Data both HR and business related
is of good quality and be aware of
the time it takes to obtain accurate
and reliable data

THE RIGHT MIX OF TEAM MEMBERS AND PRACTICAL
STRUCT UR E IS VITAL TO REALIZE THE EXECUTI ON
Analytics
Leader
Data
Expert
Analyst Business
Champion
Source : adapted from How to set up your workforce analytic function, Visier, 2016
Drive analytic
value to business
Pull and model
the right data
Tell a compelling
story about the data
Link between business
and PA team
3 + 1 KEY ROLES OF PEOPLE ANALYTICS TEAM
External
experts
CHRO
Center of
Expertise
Shared
Services
Business
Partner
People
Analytics
SPONSORED BY CEO
CHRO
Center of
Expertise
Shared
Services
Business
Partner
Analyst
PIONEERED BY CHRO

EXPLORING THE BEST POSSIBLE OPTION IN BUILDING
NEW HR ANALYTIC CAPABILITIES THAT FIT THE NEEDS
Source : adapted from Cross-Functional HR Analytics Project Team, CEB/Gartner, 2017
Hire all new skills
Build new expertise in current HR staff
Outsources analytics
Leverage existing experts within
High cost to find, hire and get them on boarded
Need to conceptualize HR and Business knowledge
Risk of cultural unfit and mismatch hiring
Relatively short-lead time
Do not guarantee the efficiency gained due to individual gap
and fundamental knowledge to be further built upon
No immediate capacity
Sustainable capability in the long-term
Require significant investment in training
High and long-term cost
Data privacy and security concern
Gain headcount optimization
A quick solution
Bring non-HR and multi-disciplinary perspectives into HR
Technical Acumen
Business Acumen
HR Acumen
Consultancy Acumen
Database management
Analytic research process
Statistical expertise
Business & Industry knowledge
Business analytic capabilities
Stakeholder management
Project management
storytelling
HR disciplines
Organizational knowledge
Analytics
Leader
Data
Expert
Analyst
Breadth
and depth of
skills required
depends on
the role

© 2018 TAS Consulting Partner I All Rights Reserved Source : Story Telling Canvas, www. brucey.com.au, 2018
Subject Goal Audience
Before Set the Scene Make a point Conclusion After
What’s the story about? What do you want to achieve with
this story?
What is your story’s audience?
What are their needs?
What does your
audience think,
feel, know, want
before they have
experienced your
story?
What do you need
to introduce?
What should be set
up or explained?
The audience’s
A-HA moment
What’s the
conclusion at the
end of your story?
What is your call
to action?
What does your
audience think,
feel, know, want
after they have
experienced your
story?
Know the
audience
Go deep into
data & analytics
Visualize
the data
Build
compelling story
Move
forward
Source : Power Your Analytics with Storytelling, Lynn Russell, 2016
CRAFTING WITH CLEAR PURPOSE HOW TO USE DATA &
ANALYTIC TO BUILD STORY THAT MAKE A DIFFERENCE

CULTURE AND STYLE OF LEADERS DIFFERENTIATE
D ATA S AV V Y O R G A N I Z AT I O N S F R O M T H E O T H E R S
Source : Global leadership Forecast 2018 , DDI, Conference Board, EYGM, 2018
Strength
of Culture
Experimental
mindset
Digital Tech
influence
Focus on
future vision
Organization
agility
Influence-based
leader power
Engagement
overexecution
IQ over EQ
Cultural Factors that Make
Data-savvy organization Unique
Characteristic of Agile Leaders
Engage
Humble
Adaptable
Careless
Driving
Visionary
Hyper
Awareness
Informed
Decision Making
Fast
Execution
Slow
driving
Wrong
direction
Source : Redefining Leadership for a Digital Age, IMD, 2017
Data-savvy
The rest

ORGANIZATION PARADIGM IS SHIFTING FROM
ORGANIZATION “AS MACHINE” TO “ A LIVING ORGANISM ”
Source : The Five Trademarks of Agile organization, Mckinsey & Company, 2018
Silos
Bureaucracy
Top-down
hierarchy
Detailed
instruction
FROM
ORGANIZATION AS
“MACHINES”
Quick changes,
flexible
resources
“Box & Line”
less important,
focus on action
Teams built
around
end-to-end
accountability
Leadership shows
direction and
enable action
TO
ORGANIZATION AS
“LIVING ORGANISM”

START UP
TRAPPED BUREAUCRACY
AGILE
Risk-averse
Slow
Efficient
Bureaucratic
Standard ways of working
Siloed
Decision escalation
Reliable
Centralized
Established
Quick to mobilize
Nimble
Collaborative
Easy to get thing done
Responsive
Free flow of information
Quick decision-making
Empowered to act
Resilient
Learning from failures
Start-up
Ad-hoc
No boundaries
Unpredictable
Chaotic
Creative
Frenetic
“Free for all”
Reinventing the wheel
Constantly shifting focus
Uncoordinated
Stuck
Empire-building
Fighting fires
Local tribes
Finger-pointing
Under attach
Rigid
Politics
Protecting “turf”
ABILITY TO DRIVE SPEED AND CREATE STABILITY
THE ESSENCE OF TRUE AGILE ORGANIZATION
Source : Agility : The Rhymes with Stability, Mckinsey Quarterly, 2015
S t a b l e B a c k b o n e
DynamicCapability
HL
H

ASKING THE RIGHT QUESTIONS AIMING TO ACHIEVE
O R G A N I Z AT I O N A L A G I L I T Y A N D P O S I T I V E R E T U R N
HR FOCUS BUSINESS FOCUS NOW WHAT?
How complicate are the
company’s HR processes
and practices?
How does ineffective HR
practices impact time to
market of new product?
How can HR simplify processes and
practices to accelerate new idea
generation and prototyping?
What is the company’s level of
employee engagement?
How does engagement correlate
with customer experiences?
What drivers should the company
focus in order to improve customer
experience?
What were the measurable
outcomes achieved from each
HR initiative in the past 3 years?
How does the company optimize
people investment to enhance its
competitive edge?
How did the budget allocation
pertaining to human capital
correlate to the business risk ?
What are the capability both quality
and quantity that company requires
to realize its 3 year-business
aspiration?
What are the best mix of workforce
including contingent workers and
machine that match evolving skillsets
and business needs and what’s the
best source to recruit each of them?
What is the current state of
employee in all aspects e.g.
demographics, type of employment,
skill gap, mobility etc.?

Leaving
Learning
IT’S AN IMPERATIVE TO CREATE SEAMLESS EMPLOYEE
EXPERIENCE ALONG EMPLOYEE LIFE CYCLE
G r e a t
a m b a s s a d o r
M e a n i n g f u l
c o n t r i b u t i o n
P e r f o r m i n g
a n d g r o w t h
S m o o t h
a s s i m i l a t i o n
W a r m
w e l c o m e
Seeking
opportunity
Sourcing
Screening
On
boarding
Offering
Employing
Seeking
information
Adapting
to culture
Connecting
to people
Applying
Knowing
the role
Remunerating
Performing
Networking
Rewarding
Developing
Mobilizing
Improving
Seeking
new
challenge
Contributing
Innovating
Advocating
Source : adapted from Patrick Coolen, HR is hitting a second wall, on LinkedIn, 2018
Growing

ASKING THE RIGHT QUESTIONS AIMING TO ENHANCE
E M P L O Y E E E X P E R I E N C E A N D T H E I R W E L L B E I N G
HR FOCUS BUSINESS FOCUS NOW WHAT?
What’s the attrition rate of
employee and reason of their
leaving?
Is there different impact on customer
satisfaction when different group of
employee leaving?
How to reduce attrition rate of
employee group who has high impact
to customer when they are leaving?
Is the quality of life of expatriate
employee working in different
location different?
What’s the total cost when
assigning an employee to other
location outside the home country?
What should be the criteria applied
when company assign employee to
international assignment that could
minimize cost and provide peace of
mind to employee?
How long does it take when a
prospect candidate submit their
application until they start their
day-one with the company and
what’s their experience?
What’s the total financial investment
including opportunity lost that
company spent in recruiting a
mid-career employee ?
What factors should company focus
when recruiting mid-career employee
in order to provide candidate best
experience, shorten lead time and
has high predictive validity?

TA S
C H A N T R E E
Managing Director
MA, Communication Research
Thammasat University
MPA, Human Resources Management
National Institute of Development Administration
BA, Social Work
Thammasat University
HR Transformation
Digital Transformation
Strategic Management
Executive Coaching
Change Management
Organization Development
Assessment Center
Leadership Development
Visual Communication
The essence of Tas’s current work is to help people discover meaning in their works and lives and to help organization find the way to
create environment that enables people to work at their full potential, which results in self-motivation, engaging team members, high
performing team, customer satisfaction and bottom-line performance.
As a result of nearly 30 years of his first-hand experience as executive management, internal organizational consultant, HR strategist and
HR practitioner in various sectors and industries e.g. Public Sector, Automotive, Electrical, Chemical, Building Materials etc. Tas has
acquired expertise not only in human capital management and organization development but also strategic management and cross
cultural management. This wide range of exposure also provides him access to an extensive network of leaders and professionals with
complementary skills an expertise. Tas was as a member of executive committee for Siam City Cement PCL (SCCC) where he worked
for 17 years prior to found TAS Consulting Partner.
A B O U T S P E A K E R
tas@tas-consultingpartner.com
Advanced Management Program #183, Harvard Business School, USA
Managing of People , INSEAD, France
Senior Management Program, IMD, Switzerland
Senior Management Program, University of St. Gallen, Switzerland
Certified Executive Coach : Berkeley Executive Coaching Institute, USA
Certified Assessor: Myers-Briggs Type Indicator® (CPP)
Certified Assessor: Hogan Assessment, Singapore
Certified Assessor: DISC Profile (Thomas International)
Certified Facilitator : 360 Profiler (PDI , now Korn Ferry)
Certified Facilitator: Targeted Selection (DDI)
Certified Facilitator: Interaction Management (DDI)
Certified Facilitator : Cart Sort (DDI)
ATD Excellence in Practice Citation (with SCCC), USA, 2014
ATD Excellence in Practice Award (with SCCC), USA, 2016
Thailand Top 100 HR, Human Resource Institute, Thammasat University
E D U C A T I O N C E R T I F I C A T I O N S & A W A R D S
E X P E R T I S E

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www.tas-consultingpartner.com
tas@tas-consultingpartner.com
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been produced base on publicly available
information from various sources.
Should you have any comment to make regarding
topic presented and their content, please contact
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