Making a Difference through Analytics

2. © 2018 TAS Consulting Partner I All Rights Reserved 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

3. © 2018 TAS Consulting Partner I All Rights Reserved 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

4. © 2018 TAS Consulting Partner I All Rights Reserved 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

5. © 2018 TAS Consulting Partner I All Rights Reserved 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

6. © 2018 TAS Consulting Partner I All Rights Reserved 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

7. © 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

8. © 2018 TAS Consulting Partner I All Rights Reserved 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

9. © 2018 TAS Consulting Partner I All Rights Reserved 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

10. © 2018 TAS Consulting Partner I All Rights Reserved 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

11. © 2018 TAS Consulting Partner I All Rights Reserved 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

12. © 2018 TAS Consulting Partner I All Rights Reserved 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

13. © 2018 TAS Consulting Partner I All Rights Reserved 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

14. © 2018 TAS Consulting Partner I All Rights Reserved 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

15. © 2018 TAS Consulting Partner I All Rights Reserved 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

16. © 2018 TAS Consulting Partner I All Rights Reserved 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

17. © 2018 TAS Consulting Partner I All Rights Reserved 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

18. © 2018 TAS Consulting Partner I All Rights Reserved 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

19. © 2018 TAS Consulting Partner I All Rights Reserved 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

20. © 2018 TAS Consulting Partner I All Rights Reserved 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

21. © 2018 TAS Consulting Partner I All Rights Reserved 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

22. © 2018 TAS Consulting Partner I All Rights Reserved 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

23. © 2018 TAS Consulting Partner I All Rights Reserved 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

24. © 2018 TAS Consulting Partner I All Rights Reserved 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

25. © 2018 TAS Consulting Partner I All Rights Reserved 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

26. © 2018 TAS Consulting Partner I All Rights Reserved 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

27. © 2018 TAS Consulting Partner I All Rights Reserved 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

28. © 2018 TAS Consulting Partner I All Rights Reserved 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

29. © 2018 TAS Consulting Partner I All Rights Reserved 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.

30. © 2018 TAS Consulting Partner I All Rights Reserved 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

31. © 2018 TAS Consulting Partner I All Rights Reserved 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

32. © 2018 TAS Consulting Partner I All Rights Reserved READINESS AND IMPORTANCE Source : Global Human Capital Trends, Deloitte University Press, 2018 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

33. © 2018 TAS Consulting Partner I All Rights Reserved 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 Source : Global Human Capital Trends, Deloitte University Press, 2018 ASIA IS SLIGHTLY BEHIND THE CURVE BUT CATCHING UP WHILE SIZE DOES MATTER IN ADOPTION THE ANALYTICS 2018 Human Capital Trends ORGANIZATION SIZE 2018 Human Capital Trends 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

34. © 2018 TAS Consulting Partner I All Rights Reserved 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

35. © 2018 TAS Consulting Partner I All Rights Reserved HR PRIMARILY APPLY BASIC DATA ANALYSIS TECHNIQUES TO PLAN, DEVELOP AND RETAIN THEIR CRITICAL TALENT Source : Human Capital Analytics Survey, i4cp, 2016 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

36. © 2018 TAS Consulting Partner I All Rights Reserved ORGANIZATIONS ARE KEEN TO EXPLORE WHAT DRIVE PRODUCTIVITY, QUALITY AND IMPROVE ATTRACTION Source : Human Capital Analytics Survey, i4cp, 2016 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 %

37. © 2018 TAS Consulting Partner I All Rights Reserved 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

38. © 2018 TAS Consulting Partner I All Rights Reserved 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

39. © 2018 TAS Consulting Partner I All Rights Reserved 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

40. © 2018 TAS Consulting Partner I All Rights Reserved 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

41. © 2018 TAS Consulting Partner I All Rights Reserved 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

42. © 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

43. © 2018 TAS Consulting Partner I All Rights Reserved 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

44. © 2018 TAS Consulting Partner I All Rights Reserved 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”

45. © 2018 TAS Consulting Partner I All Rights Reserved 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

46. © 2018 TAS Consulting Partner I All Rights Reserved 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.?

47. © 2018 TAS Consulting Partner I All Rights Reserved 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

48. © 2018 TAS Consulting Partner I All Rights Reserved 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?

49. © 2018 TAS Consulting Partner I All Rights Reserved 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

50. © 2018 TAS Consulting Partner I All Rights Reserved www.pixabay.com www.tas-consultingpartner.com tas@tas-consultingpartner.com All information contained in this presentation has 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 t r u s t w o r t h y a g i l i t y s i m p l i c i t y © 2018 TAS Consulting Partner I All Rights Reserved

Making a Difference through Analytics

Tas Chantree

Making a Difference through Analytics