1. © Stefano Grazioli - Ask for permission for using/quoting: grazioli@virginia.edu Stefano Grazioli

2.  Doing well  Deadline moved to Friday  Easy meter

3.  Name, major  Learning objectives  Things you like about the class  Things that can be improved  Attitude towards the Tournament

4. © Stefano Grazioli - Ask for permission for using/quoting: grazioli@virginia.edu Operations, BI & Analytics

5. Primary key: a unique identifier used to retrieve the record One Many manages has

6. manages has Bruce Wayne, Gotham, NY

7. Insert into Customer (c_id, f_name, l_name, city, state) values (7759, 'Bruce', 'Wayne', 'Gotham', 'NY')

8. manages has Bruce Wayne, Gotham, NY C_id = 7759

9. Insert into insurance_plan (c_id, coverage, premimum) values (7759, 1000000, 500)

10. manages has Bruce Wayne, Cville, VA

11. Update customer set city = ‘Charlottesville', state = ‘VA' where c_id = 7759

12. manages has Bruce Wayne, C_id 7759 L_id = 1070 $10,000,000 4% Due Dec 31 Barbara Goodhue Lo_id 16

13. Insert into loan (l_id, principal, rate, date_due, lo_id) Values (1070, 10000000, '12/31/2016', 16) Insert into customer_in_loan (c_id, l_id) values (7759, 1070)

14.  Directly related to business operations  Single customer, single contract, deal, service…  Often INSERTs  Small amount of data  Large numbers of fast, simple queries

15. manages has

16. select customer.state, sum(coverage) from customer, insurance_plan where customer.c_id = insurance_plan.c_id group by customer.state

17. manages has

18. select top 3 customer.c_id, customer.l_name, sum(loan.principal) from customer, customer_in_loan, loan where customer.c_id = customer_in_loan.c_id and customer_in_loan.l_id = loan.l_id group by customer.c_id, customer.l_name order by sum(loan.principal) desc

19. manages has

20. select customer.city, avg(loan.rate) from customer, customer_in_loan, loan where customer.c_id = customer_in_loan.c_id and customer_in_loan.l_id = loan.l_id group by customer.city order by avg(loan.rate) desc

21.  Relate to decision making more than business operations  Aggregate customers, contracts, deals, services…  Mostly Selects, often Joins  Larger amount of data  Small number of larger, complex queries

22. manages has

23. n/a

24.  Questions relate to decision making, more than business operations  SQL provides the input data, but is not sufficient  Analytics require additional software (SPSS, SAS, R, Data miner…)  More similar to BI queries than operational queries.

25. Transactions / Operations Real time, individual, action Business intelligence Analytics Historical, aggregate, decision Orders Customers Product s Data Warehouse Extract Clean Transform Load Query Report Analyze Visualize Technical consultants Data scientists Business consultants Data scientists Managers & Decision makers

26. © Stefano Grazioli - Ask for permission for using/quoting: grazioli@virginia.edu What Is New In Technology?

27. © Stefano Grazioli - Ask for permission for using/quoting: grazioli@virginia.edu Demo