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

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

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

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

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

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

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

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

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

13. 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

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

15. Finding our IP exposure by state
manages
has
has
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
has
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
has
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. What Is New In Technology?
WINIT
What Is New In Technology?

23. Asses the relationship between loan rate and loan size
manages
has
has
has

24. The SQL
n/a

25. 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.

26. The Big Picture… Products 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

27. Homework
Demo