1. Business Analytics and Decision Making OLTP, OLAP & SAP
Chapter 9 & SAP Materials
Updated 2019

2. OLTP vs. OLAP Online Transaction Processing (OLTP) = relational
database systems
Online Analytical Processing (OLAP)

3. OLAP via Data Warehousing
Predefined
reports
MIS 3500
Interactive
data analysis
Operations’
data
Periodical
transfers
Online Transaction
Processing (OLTP):
Querying Databases
with 3NF tables
Online Analytical
Processing (OLAP);
Data warehousing;
Data Mining.
Usually de-normalized data.
Flat files

4. OLTP & OLAP in Enterprise Systems
Enterprise Systems (Enterprise Resource Planning Systems) support both.
Example: An SAP-based system can be a TPS, MIS and DSS for the entire organization. DSS capability draws on data warehousing & cubing.
Other major software players also offer OLAP: Microsoft, IBM, Oracle…

5. Date Warehousing Goals
Data warehouse (DW):
Integrates data from different sources to get a larger picture of
business
Yields multidimensional view of data by creating data cubes
Allows for statistical analysis on large data sets (test hypotheses on relationships between pieces of data)
Allows for discovering new relationships by querying cubes or by applying data mining software.

6. Extraction, Transformation, and Loading
Preparations performed on data – ETL process
Transform/Transport
Customers
Extract
Load
Convert “Client” to “Customer”
Apply standard product numbers
Convert currencies
Fix region codes
Data warehouse:
All data must be consistent.
Transaction data from diverse systems.

7. Three-Dimensional View of Data: Cube
Created in a datawarehouse
Sales Date
Months in year 1 2 3 P1 P2 P4 P3
Sales at
Location L1 L2 L3
Logic similar to
crosstab query
And pivot table.
Product

8. Data Hierarchy Year Levels Quarter Roll-up To get higher-level totals
Month
Week
Day
Levels
Roll-up
To get higher-level totals
Drill-down
To get lower-level details

9. Datawarehouse Tables: Star Design
Design is:
- Hierarchical (dimension tables have no direct association)
- De-normalized (fact table): Price & Quantity inputted to Fact table;
Dimension
Dimension
Sale
SaleDate
Quantity
Discount
Sale
SaleDate
Quantity
Discount
StoreID
Product
ProductID
Price
Fact Table *
Revenue=Price*Quantity
per customer, product, period
Calculated
fact
Dimension
Inputted from Product, Sale & Customer;
most dimensions replicated in Fact table.
Customer
CustomerID
Detail
*Software vendors may have their own terminology; e.g., in SAP systems the fact table may sometimes be called “key figures” and “measures”.
The term “measure” signifies the measurability of attributes, a quantitative data type.
Location
LocationID
Detail
Revenue broken down by product,
sales location, and desired time period
(time column/s – day of year, or even
smaller; basis for rollup).
New keys, often combined, usually used in the fact table (e.g., SaleTbl#-Row#).

10. Data Warehouse for Tyson Foods
Dimension tables (truncated) provide inputs for “facts” (calculated attributes) in Fact table.
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11. Datawarehouse Tables: Snowflake Design
Design is:
Network-like (dimension tables can connect directly)
Still partly normalized (Sale-Customer-City)
Product
CityID
ZipCode
City
State
ItemID
Description
Price
Category
SaleID
SaleDate
CustomerID
Discount
SalesTax
Sale
CustomerID
Phone
FirstName
LastName
Address
ZipCode
CityID
Customer
OLAPItems
MerchTblRow
SaleTblRow
Price
Quantity
Dimension Tables
Fact Table
Advantage: Design of Fact table simpler (Customer, City out);
Faster processing with a reduced DW schema.

12. SAP Datawarehouse More on SAP Datawarehouse Can also be Dimensions 
Datawarehouse Cube
Details
Can also be
Dimensions 
More on SAP Datawarehouse

13. Multidimensional View of Data – Precursors to DW: Excel Pivot Table
Last Name, ID
Facts (Key Figures,
Measures)
Dimensions:
Quarter, Month
Can place data in rows or columns.
By grouping months, can instantly get quarterly or monthly totals.

14. Microsoft Platform On Azure Cloud

15. SQL 99: Multidimensional Data Views
SELECT Category, Month, Sum, GROUPING (Category) AS Gc,
GROUPING (Month) AS Gm
FROM …
GROUP BY CUBE (Category, Month...)
Category Month Sum Gc Gm
Bird
Bird …
Bird (null)
Bird (null)
Cat
Cat
Cat (null)
(null)
(null)
(null)
(null) (null)
SQL Server:
More…

16. SQL GROUPING SETS - Hiding Details
SELECT Category, Month, Sum
FROM …
GROUP BY GROUPING SETS
( ROLLUP (Category),
ROLLUP (Month),
( ) )
Category Month Amount
Bird (null)
Cat (null) …
(null)
(null)
(null)
(null) (null)
More…

17. SQL: RANK Functions Jones 18,000 1 1 Smith 16,000 2 2 Blau 16,000 2 2
Calculates and rank orders; useful for analysis
SELECT Employee, SalesValue
RANK() OVER (ORDER BY SalesValue DESC) AS rank
DENSE_RANK() OVER (ORDER BY SalesValue DESC) AS dense
FROM Sales
ORDER BY SalesValue DESC, Employee;
Employee SalesValue rank dense
Jones 18,
Smith 16,
Blau 16,
Whitt 14,
DENSE_RANK does not skip numbers
Another example: find the rank of a particular salesperson:
SELECT RANK(Jones) WITHIN GROUP (ORDER BY Employee) FROM Sales;
Therefore, advances in SQL motivate DBMS vendors to support
OLAP and data warehousing.

18. Data Mining
Goal: To discover unknown relationships in the data that can be used to make better decisions.
Exploratory analysis.
A bottom-up approach that scans the data to find relationships
Some statistical routines, but they are not sufficient
Statistics relies on averages
Sometimes the important data lies in more detailed pairs
Supervised by developer vs. unsupervised (self-organizing artificial neural networks)

20. Common Techniques for Data Mining
1. Classification/Prediction
2. Association Rules/Market Basket Analysis
3. Clustering
Some based heavily on classical statistics (1), others use specialized mining software (2), yet others combine statistics with specialized mining software (3).
Currently, these techniques are considered part of data analytics.

21. 1. Classification (Prediction)
Purpose: “Classify” things that are causes and those that are effects.
Examples
Which borrowers/loans are most likely to be successful?
Which customers are most likely to want a new item?
Which companies are likely to file bankruptcy?
Which workers are likely to quit in the next six months?
Which startup companies are likely to succeed?
Which tax returns are fraudulent?

22. Classification Process
Clearly identify the outcome/dependent variable.
Identify potential variables that might affect the outcome.
Use sample data to test and validate the model.
Run the data through the model.
Regression/correlation analysis, decision trees & tables (below).
Income
Credit History
Job Stability
Credit Success
50000
Good
Yes
75000
Mixed
Bad No

23. 2. Association/Market Basket
Purpose: Determine what events or items go together/co-occur.
Examples:
What items are customers likely to buy together?
(Business use: Consider putting the two together to
increase cross-selling.)

24. Association Challenges
If an item is rarely purchased, any other item bought with it seems important. So combine items into categories.
Some relationships are obvious.
Burger and fries.
Some relationships are puzzling/meaningless/misleading.
Hardware store found that toilet rings sell well only when a new store first opened. But what does it mean?
Caution applies to data analytics: mere relationships without a background knowledge can be misleading.

25. 3. Cluster Analysis
Purpose: Determine groups of people or some entities.
Examples
Are there groups of customers? (If so, we could target them; market segmentation)
Do the locations for our stores have elements in common? (If so, we can search for similar clusters for new locations.)
Do employees have common characteristics? (If so, we can hire similar, or dissimilar, people.)
Large inter-cluster distance
Small intra-cluster distance

26. Summary: From Data Warehousing/Mining to Data Analytics
EXPLANATORY
ANALYTICS
PREDICTIVE
ANALYTICS