Big Data Analytics Initiative

Transcription

1 Big Data Analytics Initiative

2 Walker School of Business and Technologies SAP System Integration Group (SAP SIG) Presents Big Data and Analytics Seminar Webster University August 2016

3 SAP System Integration Group (SAP SIG) Members Run Niu Rich Dippel Ali Ovlia (Contact Person) Zeliang Zhang (Frank) Zhe Liu (Jeff)

4 Agenda Topic Speaker Time 1. Events Introduction Ali Ovlia 14: 00 14:10 2. Big Data Analysis System Architecture 3. Introduce of Big Data Association and Actions Frank Zhang 14: : 00 Jeff Liu 15: : Q/A 15: : 00

5 Biography of Speakers Frank Zhang MBA student of Webster University Shanghai Data CVG Co., Ltd. o Director of Fourth Department BI System Architect Director of Fourth Department Shanghai University of Finance and Economics & Business Analyst o Vice president of the Tenth MBA Association o President of Big Data Research Club Jeff Liu MBA student of Webster University SAP China Co., Ltd. Senior Engineer of Success Factor Shanghai University of Finance and Economics Vice president of Big Data Research Club HANA Specialist HCM Consultant

6 Contents 1 Big Data Introduction 2 Big Data System Architecture 3 Relate Technology and Software 4 Business Model 5 Big Data and Analyst Association 6 Training and Practices

7 Big Data Introduction The data from internet is External Data unstructured, such as finance.yahoo.com, and Facebook Not only from ERP, but also CRM, WMS, MES, and so on Internal Data Difficulty of Big Data Analysis Business Model Need to be proficient in finance, marketing and statistics

8 Contents 1 Events Background 2 Big Data System Architecture 3 Relate Technology and Software 4 Business Model 5 Big Data and Analyst Association 6 Training and Practices

9 Big Data Analysis Architecture ERP PLM WMS SCM Enterprise Business Bus Enterprise Data Center SSO Custom Product ODS DW Portal Master Data Supplier Data Warehouse DM Enterprise Business Bus Internet Excel Webservice etc

10 Contents 1 Events Background 2 Big Data System Architecture 3 Relate Technology and Software 4 Business Model 5 Big Data and Analyst Association 6 Training and Practices

11 Relate Technology & Software Outline Data Warehouse Data Source Internal Data External Data Data Collect ODS DW DM Hadoop BW, HANA, Teradata Data Mining Data Analysis Report Databoard Kettle Python Statistics Model SAP BO Tableau SAS SPSS R Python

12 Excel VS Specially Software Performance, easy to use, difficult VS.

13 Data Collection-Kettle Data come from Database, SAP ERP, Excel, etc.

14 Data Collection-Python Data come from finance.yahoo.com

15 Data Analysis Introduce SAP BO Dashboard.

16 Data Warehouse

17 Data Mining

18 Contents 1 Events Background 2 Big Data System Architecture 3 Relate Technology and Software 4 Business Model 5 Big Data and Analyst Association 6 Training and Practices

19 Financial Model DuPont Model: Enterprise Finance Status Analysis

20 Marketing Analysis Model RFM Model: Customer Value Analysis R- Recently F- Frequency M- Monetary.

21 Contents 1 Events Background 2 Big Data System Architecture 3 Relate Technology 4 Business Model 5 Big Data and Analyst Association 6 Training and Practices

22 Student Analyst Association Required Foundations Academic Undergraduate Degree: Data Analytics, Finance, Computer Science, Mathematics, and Engineering. Required additional improvement in relate skills. Alumni Many alumni engaged in data analysis, can bring a lot of data analytics cases and practice opportunities Resource Frank & Jeff have 20+ years experience of IT industry, 3 years experience of Data Analytics and Business Intelligence

23 Student Association Organization Structure President Support Frank Zhang Webster University SAP SIG Tech Group Business Group Community Back Office Data Collect Finance Analysis On campus events Member enroll Data Warehouse Data Mining Marketing Analysis Logistics Analysis Alumni contact Treasure Data Analysis Supply Chain Analysis

24 Association Mission Goal: Enhance Data Analytics ability, improve employment; Mission:Let the Webster student, alumni and university get benefits Student To deepen classroom learning Handle advanced technology To develop practices ability Alumni Learn from each other Common problem can be overcome by practice team University To improve the teaching effect To promote the employment of students

25 Contents 1 Events Background 2 Big Data System Architecture 3 Relate Technology and Software 4 Business Model 5 Big Data and Analyst Association 6 Training and Practices

26 Training Plan Step 1: Collect common training requirement Step 2: Make training plan, usually at 2:00-4:00 PM Sunday. Kettle Python SAP BO SAP HCM Data Collect Internet Spider Dashboard Successfactors Extract from SAP ECC Extract from Database Extract from Excel Financial data collect Stock data collect Dupont Model Sale forecast etc Human Capital Management

27 Practices Possibly Step 1: Collect common requirement Step 2: Evaluation Step 3: Acquire team Step 4: Make project plan, sign the agreement (about information security, achievement attribution and cooperation requirements).

28 Question and Answer

29 The End Contact Information