Data + Science Towards Organizational Excellence. eric Choo
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1 Data + Science Towards Organizational Excellence eric Choo
2 Business Intelligence & Big Data Analytics SoftSource Big Data Solutions Business Intelligence & Data EVA Services D a t a E x p l o r a t i o n, V i s u a l i z a t i o n & A n a l y s i s Data MBA Services Data Mining & Business Analytics V e r t i c a l S o l u t i o n s & H a d o o p - b a s e d A p p l i c a t i o n s Authorized Hadoop T r a i n i n g C e n t r e & B i g D a t a S y s t e m s I n t e g r a t o r Infrastructure - Servers, Storage, Data Protection & Retention Solutions C o n s u l t a t i o n, I m p l e m e n t a t i o n & P r o j e c t M a n a g e m e n t
3 What you will be hearing
4 DATA SCIENCE
5 Data Science What is Data Science Extraction of knowledge from data employing math, statistics and information theory (Probability model, machine learning and etc.) Let the gut feel make the assumptions', and the data be the prove
6 Basic Data Science Categories Classification (Supervised Learning) Clustering (Unsupervised Learning)
7 Classification / Supervised Learning Example I know that it will rain when Sky is dark More moisture in the air Its is near raining session Question: In the current weather will it rain This is Classification Labelling of data according to a labelled training set Type of algorithms Naive Bayes Linear Regression Decision Trees
8 Clustering / Unsupervised Learning Example: I have a set of data collected regarding weather I have multiple other set of data that are non related to the weather. ie. forest fire data from nearby region, etc. Are there any relation between the data set? Type of algorithms K-mean Fuzzy Clustering
9 What you will be hearing
10 TEXT ANALYTICS
11 Text Analytics Text Documents Hadoop MAHOUT (Data Science Tool) MapReduce CLUSTER DOCUMENTS
12 Text Analytics Text Documents Hadoop MAHOUT (Data Science Tool) MapReduce CLUSTER 1 CLUSTER 2 CLUSTER 3 CLUSTER 4
13 Categorizing into Topics/Stories CONSTRUCT STORIES CATEGORY : INNOVATION TOP TERMS CL 1 Technology 3D Printing Steve Jobs Sports Wear CLUSTER 1 CLUSTER 2 CATEGORY : SECURITY TOP TERMS CL 2 United Nations Dogs Camera Internet of Things CLUSTER 3 CLUSTER 4
14 Term Document Matrix Word 1 Word 2 Word 3 Word 4 Word 5 Word 6 Word 7 Word 8 Word 9 Word 10 Word 11 FILE 1 FILE 2 FILE 3 FILE 4 FILE 5 FILE 6 FILE 7 Cluster 1: FILE 1 FILE 6 Cluster 2: FILE 2 FILE 5 Cluster 3: FILE 3 FILE 4 Cluster 4: FILE 7
15 What you will be hearing
16 SENTIMENT ANALYTICS
17 Sentiment Analysis TWITTER (DATA IN JSON FORMAT) Field Value Analyze Tweets For Country United States By individual State Objective : To find out the level of happiness of a State in USA
18 Sentiment Dictionary Sentiment Dictionary AFINN-111
19 Sentiment Score Computation San Francisco Score at tweet level for CA Los Angeles New York Chicago Boston San Diego Score at tweet level for CA Score at tweet level for CA Summing up the tweet level scores for each state
20 Results in Sentiment Analysis Happy States Unhappy States
21 Results in an example of Simple Visualization
22 Results in an example of Complex Visualization
23 What you will be hearing
24 INVESTMENT USE CASE
25 Introduction CASE Discover business insights for global investment opportunities THE PROBLEMS You have a lot of money but you do not know : What to invest in? Which country to invest in? How to choose the criteria? How to measure? Data is available but they are in silos
26 Economic Policy Education Environment Financial Sector Health Infrastructure Property Poverty Private Sector Public Sector Multiple Data Sources For Countries INTERNATIONAL MONETARY FUND Hadoop
27 The International Monetary Fund Data Country Country Code Indicator Name Indicator Code Year Category Analyze Data in MapR using Pig, R, etc.
28 Decision with Analytics Support DECISION SQL Structured Query Language Data Science Text Analytics IMF Data Hadoop
29 GDP Related Indicators ENVIRONMENT PUBLIC SECTOR INFRASTRUCTURE FINANCIAL SECTOR ECONOMIC POLICY AND DEBT NY.GDP.PETR.RT.ZS - Oil rents (% of GDP) NY.GDP.COAL.RT.ZS - Coal rents (% of GDP) NY.GDP.FRST.RT.ZS - Forest rents (% of GDP) NY.GDP.MINR.RT.ZS - Mineral rents (% of GDP) NY.GDP.NGAS.RT.ZS - Natural gas rents (% of GDP) EN.ATM.CO2E.PP.GD - CO2 emissions (kg per PPP $ of GDP) EN.ATM.CO2E.KD.GD - CO2 emissions (kg per 2005 US$ of GDP) NY.GDP.TOTL.RT.ZS - Total natural resources rents (% of GDP) EN.ATM.CO2E.PP.GD.KD - CO2 emissions (kg per 2011 PPP $ of GDP) GC.XPN.TOTL.GD.ZS - Expense (% of GDP) GC.TAX.TOTL.GD.ZS - Tax revenue (% of GDP) GC.BAL.CASH.GD.ZS - Cash surplus/deficit (% of GDP) MS.MIL.XPND.GD.ZS - Military expenditure (% of GDP) GB.XPD.RSDV.GD.ZS - Research and development expenditure (% of GDP) FM.LBL.BMNY.GD.ZS - Broad money (% of GDP) FS.LBL.LIQU.GD.ZS - Liquid liabilities (M3) as % of GDP FS.LBL.QLIQ.GD.ZS - Quasi-liquid liabilities (% of GDP) FM.LBL.MQMY.GD.ZS - Money and quasi money (M2) as % of GDP FS.AST.PRVT.GD.ZS - Domestic credit to private sector (% of GDP) CM.MKT.LCAP.GD.ZS - Market capitalization of listed companies (% of GDP) FD.AST.PRVT.GD.ZS - Domestic credit to private sector by banks (% of GDP) FS.AST.DOMS.GD.ZS - Domestic credit provided by financial sector (% of GDP) FS.AST.DOMO.GD.ZS - Claims on other sectors of the domestic economy (% of GDP) NE.TRD.GNFS.ZS - Trade (% of GDP) NY.GNS.ICTR.ZS - Gross savings (% of GDP) BG.GSR.NFSV.GD.ZS - Trade in services (% of GDP) ACADEMIC R&D
30 Selected Indicators ENVIRONMENT PUBLIC SECTOR INFRASTRUCTURE FINANCIAL SECTOR ECONOMIC POLICY AND DEBT EDUCATION EN.ATM.CO2E.PP.GD.KD - CO2 emissions (kg per 2011 PPP $ of GDP) GC.TAX.TOTL.GD.ZS - Tax revenue (% of GDP) GC.BAL.CASH.GD.ZS - Cash surplus/deficit (% of GDP) MS.MIL.XPND.GD.ZS - Military expenditure (% of GDP) GB.XPD.RSDV.GD.ZS - Research and development expenditure (% of GDP) CM.MKT.LCAP.GD.ZS - Market capitalization of listed companies (% of GDP) NY.GNS.ICTR.ZS - Gross savings (% of GDP) SE.XPD.TERT.PC.ZS - Expenditure per student, tertiary (% of GDP per capita)
31 World Wide Indicators Average And Maximum Indicator Code Avg Max Criteria GB.XPD.RSDV.GD.ZS - R&D Expenditure > 0.5 SE.XPD.TERT.PC.ZS Expenditure per Student > 70 CM.MKT.LCAP.GD.ZS Market Capitalization > 400 EN.ATM.CO2E.PP.GD.KD - CO2 Emission < 0.2 GC.BAL.CASH.GD.ZS Cash Surplus / Deficit > 10 GC.TAX.TOTL.GD.ZS Tax Revenue > 30 MS.MIL.XPND.GD.ZS - Military Expenditure to 20
32 Countries Ranked By Score And Criteria Country Fulfilled Qty Avg Score Hong Kong SAR, China 5 63 Malaysia 6 29 Singapore 6 27 Luxembourg 5 27 United Kingdom 6 23 Jordan 5 23 Denmark 6 21 United States 6 19 Morocco 6 19 Sweden 5 19 Canada 5 19 Finland 5 18 Chile 5 18 Netherlands 5 17 Cyprus 5 17 Israel 6 16 India 6 16 Country Fulfilled Qty Avg Score France 6 15 Spain 5 14 Botswana 5 14 Korea, Rep Norway 5 13 Belgium 5 13 Ireland 5 11 Brazil 5 11 Russian Federation 5 10 Greece 5 10 Portugal 6 9 Germany 5 9 Egypt, Arab Rep. 5 9 Italy 5 8 Croatia 5 8 Lebanon 5 6 Turkey Countries Ranked by Average Score (based on Indicator Values)
33 Advanced Analysis With MapR Hadoop & R Country Fulfilled Qty Avg Score Hong Kong SAR, China 5 63 Malaysia 6 29 Singapore 6 27 Luxembourg 5 27 United Kingdom 6 23 Jordan 5 23 Denmark 6 21 United States 6 19 Morocco 6 19 Sweden 5 19 Canada 5 19 Finland 5 18 Chile 5 18 Netherlands 5 17 Cyprus 5 17 Israel 6 16 India 6 16 Gross Saving Education Expenditures Military Expenditures CO2 Emission Tax Revenue Cash Surplus / Deficit Market Capitalization DECISION TIME SERIES LINEAR REGRESSION BAYESIAN NETWORK DECISION LIST
34 What you will be hearing
35 DATA SCIENCE AND THE HR CHALLENGES
36 Data Science & the HR Challenge Areas Added to the HR Function's Scope: 2013 Use of technology 15% Social networking/web % Employer branding 17% Measurement and metric 18% None 22% Strategic workforce planning 24% Organizational Re-structuring 26% 0% 5% 10% 15% 20% 25% 30% Source:
37 Data Science & the HR Challenge HR Areas the Organization Considers Most Important: 2013 Planning for staff succession 21% Managing staff performance 25% Managing change 25% Attracting and recuiting staff 26% Increasing employee engagement 28% Increasing leadership capability 30% 0% 5% 10% 15% 20% 25% 30% 35% Source:
38 HR Scorecards Low 25% 25% - 50% 50% - 75% Top 75% Company's position Best practice Revenue per FTE (PLN) % 75% - 100% Cost per FTE (PLN) % 0% - 25% Profit per FTE (PLN) % 75% - 100% Wealth Created per FTE (PLN) % 75% - 100% Remuneration/Revenue (%) 8% 14% 0% - 25% Average Remuniration (PLN) % 25% - 50% Human Capital ROI (PLN) % 75% - 100% ENGAGEMENT & BEHAVIORS Performance Related Pay (%) 7.0% 20.1% 32% 75% - 100% Absence Rate (%) 3.1% 6.2% 59% 0% - 25% Resignation Rate (%) 3.0% 15.0% 85% 0% - 25% FUNCTION & STRUCTURE L & D Hours per FTE 2.5 h 12.0 h 39% 75% - 100% Learning Penetration (%) 21.0% 80.0% 17% 75% - 100% FTEs per HR Department FTE 92 89% 75% - 100% HR Department Costs per FTE (PLN) % 0% - 25% Current company's result Best practice Areas of improvement Areas for immediate improvement Distance to best practice Source:
39 Dynamism of HR Functions Human Resource Strategy: Within a diverse and evolving environment we aim to engage our people through effective leadership, the promotion of well being and the provision of continuous development Diverse and Conducive Environment Adapted Leadership Communication Informed Management Team/Partnership Development Learning from our Staff Management of Communication Networks Consultation Processes Opportunities & Development Policies & Procedures Resource Management Style & Traits Informed Decision Making Written Verbal IT Feedback Mechanism Realistic Appraisals / Eds Realism Staffing Appropriate Equipment Finance Appointing the Right Person Consultation with Staff Training Continual Assessment of Competencies Rewards and Recognition Work/Life Balance Support Services Management of Abstraction Health & Safety Principles Source:
40 Analytics & Organization Excellence Analytics is Definitely Coming to HR The Evolution of Business Analytics in other Functions The Waves of Business Analytics Finance & Logistics Customer & Marketing Talent & Leadership Integrated ERP and Financial Analytics Predictive Customer Behavior - CRM Predictive Talent Models HR Analytics Integrated Supply Chain Web Behavior Analytics Business-driven Talent analytics Logistics and Supply Chain analytics 1980s Financial and Budget Analytics Customer Analytics CRM (Data Warehouse) Customer Segmentation Shopping Basket Integrated Talent Management Workforce Planning Recruiting, Learning, Performance Measurement The Industrial Economy The Financial Economy The Customer Economy and Web The Talent Economy Steel, Oil, Railroads Conglomerates Financial Engineering Customer Segmentation Personalized Products Source: Globalization, Demographics Skills and Leadership Shortages Early 1900s 1950s-60s 1970s-80s Today
41 External Sources Of Information On Employees DECISION SQL Structured Query Language Data Science Text Analytics Social Medias Social Blogs Company Feedback Forms Hadoop
42 What you will be hearing
43 ORGANIZATION EXCELLENCE
44 Organization Excellence Organization Objective Decrease operational cost (Operation Analytics) Increase sales (Predictive Analytics / Collaborative Filtering) Employee Satisfaction Employee Sentiment (Sentiment Analytics) Behavioural Pattern (Behavioural Analytics) Customer Delight Customer sentiment (Sentiment Analytics) Behavioural Pattern (Behavioural Analytics)
45 Organizational Excellence Achieving Strategic Balance and Sustainability Financial/Strategic Results And we will keep/get more business. Clients and Partners Then our client will be delighted Processes Doing the right things in the most effective way People If we have focused, skilled and engaged staff
46
47 Big Data Implementation Road Map PLAN BUILD MANAGE Plan, Design, Implement Support / Managed Services Validation Assess Understand Proof of Concept Discovery Workshop Model Explore
48 Please take some time to fill up the feedback form and the Question Sheet
41 T Korea, Rep. 52.3. 42 T Netherlands 51.4. 43 T Japan 51.1. 44 E Bulgaria 51.1. 45 T Argentina 50.8. 46 T Czech Republic 50.4. 47 T Greece 50.
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