1. Operations, BI, and Analytics
Stefano Grazioli

2. Critical Thinking
Easy meter

3. Using the SmallBank DB for Business
Operations, BI & Analytics

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

5. Different types of business information needs lead to different queries
Business transactions
Business intelligence questions
Analytics questions

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

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
Bruce Wayne,
Gotham, NY
C_id = 7759
Coverage $100K Premium $500
manages
has
has
has

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

10. Changing an address has manages has has cid 7759 Bruce Wayne
Cville, VA
has
manages
has
has

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

12. Granting a new loan manages has has has Bruce Wayne, C_id 7759
L_id = 1070
$10,000,000 5%
Due Dec 31, 2020
Barbara Goodhue
Lo_id 16
manages
has
has
has

13. The SQL Insert into loan (l_id, principal, rate, date_due, lo_id)
Values (1070, , 0.05, '12/31/2020', 16)
Insert into customer_in_loan (c_id, l_id)
values (7759, 1070)

14. The previous queries reflect business transactions
Directly related to business operations
Single customer, single contract, deal, service…
“Real time”
Often INSERTs, sometimes UPDATES
“Small” amount of data
Large numbers of fast, “simple” queries

15. Homework
Demo

16. WINIT
What Is New In Technology?

17. Different types of business information needs lead to different queries
Business transactions
Business intelligence questions
Analytics questions

18. Finding our IP exposure by state

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

20. Finding our top three customers

21. 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

22. Finding the average interest rate by city

23. 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

24. 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

25. Different types of business information needs lead to different queries
Business transactions
Business intelligence questions
Analytics questions

26. Assess the correlation between loan rate and loan size

27. The SQL
n/a

28. Analytics requires more sophisticated stat tools (typically non-SQL)
Questions relate to decision making, more than business operations
More similar to BI queries than operational queries.
SQL provides the input data, but is not sufficient.
Analytics require additional software (EXCEL, SPSS, SAS, R, Data miner…)

29. The Big Picture… Transactions / Operations
Real time, individual, action
Business intelligence Analytics
Historical, aggregate, decision
Orders
Extract
Clean
Transform
Load
Query
Report
Analyze
Visualize
Data Warehouse
Customers
Products
Managers &
Decision makers
Recommended reading: TDWI Smart Companies Report 2003, available at
Data warehousing includes two parts – getting data in, and getting data out. Getting data in is the hard part – it includes taking data from source systems, transforming the data, and loading it into an integrated data store. Getting data in is 80 % of time and resources, and 50% of unexpected costs.
Getting data out is the fun part – it include the BI tools that casual and power users use to access the data warehouse data. When users use the data, they can deliver value to the organization.
The data store in the middle can be an enterprise data warehouse, a data warehouse with dependent data marts, independent data marts, or a federated database environment. Typically, the independent data mart approach is least effective.
The focus of today is on designing the data structures for a dependent or independent data mart that is tuned for on-line analytical processing (OLAP).
Technical consultants
Data scientists
Business consultants
Data scientists

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