Leading Business with Data. LEE Wee Leong Associate Professor of Information Systems (Education) & Programme Director, MITB(Analytics)

Transcription

1 Leading Business with Data LEE Wee Leong Associate Professor of Information Systems (Education) & Programme Director, MITB(Analytics) 1

2 Master of IT in Business (Analytics) 2

3 How Do We Make Decisions? 10% - Hard Facts 90% - Gut Feel * Economist Intelligence Unit, 2014 *40% of major decision making are based on gut feeling! * Accenture, % - Hard Facts 88% - Gut Feel * Aspect Consulting,

4 What is Data Analytics? DATA Statistical & Quantitative Analyses Fact-based Decisions & Actions Explanatory & Predictive Modeling Analytics is the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. ~ Thomas H. Davenport ~ Competing on Analytics: The New Science of Winning 4

5 Why Data Analytics is HOT Now? Oracle Big Data Intel Inside Penti um Intel Inside Penti um 4 Intel Inside Core Duo 2 Intel Inside Core Quad 2 Intel Inside Core i5 Weka The University of Waikato SPSS Software IBM SAS Rapidminer IBM Watson KNIME Explosion of data Big Data Increase in Processing Power & Cheaper Storage Revolution Analytics Ease of Analyzing Data 5

6 What is BIG DATA? Volume Scale of Data Variety Different Forms of Data The FOUR V s of Big Data Velocity Analysis of Streaming Data Veracity Uncertainty of Data 6

7 Analytics Financial Industry Telco Consultancy Healthcare Retail Transportation & Logistics 7

8 MITB (Analytics) Curriculum Foundation Courses Analytics Framework & Business Context Data Analytics Lab Elective Courses Customer Analytics Operations Analytics Big Data: Tools & Techniques *(new) Visual Analytics Hands-on & Industry Linkage Internship/ Capstone Project Monthly industry seminars Full time : 1 year Part time : 2 years Introduction to Statistics Introduction to R Programming Text Analytics Social Analytics Applied Machine Learning *(new) Predictive Analytics using Simulation *(new) General and IT & Project courses

9 The Curriculum in a Nutshell B. Analytics Technology & B.1 Analytics Framework & Business Context B.2 Data Analytics Lab B.3 Customer Analytics & B.4 Operations Analytics & B.5 Big Data: Tools & Techniques B.6 Visual Analytics & B.7 Text Analytics & Equip B.8 Social with Analytics innovation, & IT and project management skills B.9 Applied Machine Learning B.10 Predictive Analytics using Simulation C. Information Technology C.1* Innovation C.2 Spreadsheet Modeling for Technology & Operations Decisions C.3 IT Project & Vendor C.4 Global Sourcing of Technology & Processes D. General for Technology & Operations D.1A* Financial Accounting D.1C* Accounting for T&O Managers D.2 Strategy & Organisation D.3 Finance for T&O Managers D.4* HRM for Technology & Operations Managers E. Internship / Capstone Project E.1 Internship Internship Job description definition Resume writing, internship application & interviews Industry attachment E.2 Capstone Project Project definition, development & critique workshops Industry expert seminars & company site visits Project Delivery 9

10 The Curriculum in a Nutshell B. Analytics Technology & B.1 Analytics Framework & Business Context B.2 Data Analytics Lab B.3 Customer Analytics & B.4 Operations Analytics & B.5 Big Data: Tools & Techniques B.6 Visual Analytics & B.7 Text Analytics & B.8 Social Analytics & B.9 Applied Machine Learning B.10 Predictive Analytics using Simulation C. Information Technology C.1* Innovation C.2 Spreadsheet Modeling for Technology & Operations Decisions C.3 IT Project & Vendor C.4 Global Sourcing of Technology & Processes Develop business management skills D. General for Technology & Operations D.1A* Financial Accounting D.1C* Accounting for T&O Managers D.2 Strategy & Organisation D.3 Finance for T&O Managers D.4* HRM for Technology & Operations Managers E. Internship / Capstone Project E.1 Internship Internship Job description definition Resume writing, internship application & interviews Industry attachment E.2 Capstone Project Project definition, development & critique workshops Industry expert seminars & company site visits Project Delivery 10

11 The Curriculum in a Nutshell B. Analytics Technology & B.1 Analytics Framework & Business Context B.2 Data Analytics Lab B.3 Customer Analytics & B.4 Operations Analytics & B.5 Big Data: Tools & Techniques B.6 Visual Analytics & B.7 Text Analytics & B.8 Social Analytics & B.9 Applied Machine Learning B.10 Predictive Analytics using Simulation C. Information Technology C.1* Innovation C.2 Spreadsheet Modeling for Technology & Operations Decisions C.3 IT Project & Vendor C.4 Global Sourcing of Technology & Processes D. General for Technology & Operations D.1A* Financial Accounting D.1C* Accounting for T&O Managers D.2 Strategy & Organisation D.3 Finance for T&O Managers D.4* HRM for Technology & Operations Managers Acquire hands-on industry experience, with internship opportunities for full-time students. E. Internship / Capstone Project E.1 Internship Internship Job description definition Resume writing, internship application & interviews Industry attachment E.2 Capstone Project Project definition, development & critique workshops Industry expert seminars & company site visits Project Delivery 11

12 MITB (Analytics) At a Glance First Master Program in Analytics in Asia Launched in Jan th intake (Aug 2016) Our case won TUN teaching innovation award 2013 Our simulation games shortlisted for Wharton-QS Stars Reimagine Education Awards 2015 Largest ecosystem in Data & Decision Analytics related research Wide selection of internship opportunity

13 Summary Benefits for commuters: With real-time commuters data, model developed will be able to give commuters a good gauge of the average waiting time at the train stations. New or existing mobile applications can be developed to tap into this useful information. Benefits for train operators: The simulation model can be used as a test bed to test various scenarios by varying some operational conditions like: Train capacity (new models or add seats) Inter-station travelling time (change speed of trains)