1. Financial Information Management Operations, BI, and Analytics Stefano Grazioli

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

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

4. Financial Information Management Using the SmallBank DB for Business Operations, BI & Analytics

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

6. Enrolling a new customer manages has Bruce Wayne, Gotham, NY

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

8. Selling insurance to a customer manages has Bruce Wayne, Gotham, NY C_id = 7759

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

10. Changing an address manages has Bruce Wayne, Cville, VA

11. The SQL Update customer set city = ‘Cville', state = ‘VA' where c_id = 7759

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

13. The SQL 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. The previous queries implement operational transactions  Directly related to business operations  Single customer, single contract, deal, service…  “Real time”  Often INSERTs  “Small” amount of data  Large numbers of fast, “simple” queries

15. Finding our IP exposure by state manages has

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

17. Finding our top three customers manages has

18. The SQL 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. Finding the average interest rate by city manages has

20. The SQL 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. The previous queries generate reports and answer aggregate questions (BI)  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. Asses the relationship between loan rate and loan size manages has

23. The SQL n/a

24. Analytics requires more sophisticated stats (typically non-SQL)  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. The Big Picture… Transactions / Operations Real time, individual, action Business intelligence Analytics Historical, aggregate, decision Orders Customers Products Data Warehouse Extract Clean Transform Load Query Report Analyze Visualize Technical consultants Data scientists Business consultants Data scientists Managers & Decision makers

26. Financial Information Management WINIT What Is New In Technology?

27. Financial Information Management Homework Demo