1. Business Intelligence : a primer Rev April 2012
Introduction & overview
The paradigm of BI systems
Platforms
Appendix
Review questions

2. Our approach to BI BI systems modelling ES taxonomy BI Architecture
Enterprise
Information
Modeling
SIRE
KPI
Identification /
mapping
HIGO
Aggregate Strategic Level (ASL)
Plan
Exec
Mon
Dash
Rep
DSS
Ctl
Info
ES taxonomy
GUI Modeling
GOA
Analytic Information
Modeling
DFM
Rich Semantic Level (RSL)
BI systems modelling
BI Architecture
Software Engineering Interface (SEI)
Implementation Level

3. Business Intelligence: the role within Enterprise Systems
Management support
Management Information Systems
[Planning & Management Control + Business Intelligence ]
Front-end systems
(Support the life cycle of customers and end products)
Back-end systems
(Support the cycle of production and delivery)
Administrative systems (Finance, HR etc.)
Operations support

4. Acronyms ABC: Activity Base Costing ABM: Activity Based Management
BI: Business Intelligence
BW: Business Warehouse (synonym of DW)
BSC: Balanced Score Card
CPM: Corporate Performance Management (synonym of SEM)
CRM: Customer Relationship Management
CSF: Critical Success Factor
DBMS: Data Base Management System
DSS: Decision Support System
DW: Data Warehouse
EIS: Executive Information System
EPM: Enterprise Performance Management (synonym of SEM)
ERP: Enterprise Resource Planning
ERM: Enterprise Resource Management
ES: Enterprise System
KPI: Key Performance Indicator
MBO: Management By Objectives
MRP: Manufacturing Resource Management
ODS: Operational Data Store
OLAP: On Line Analytical Processing
OLTP: On Line Transaction Processing
SCM: Supply Chain Management
SEM: Strategic Enterprise Management

5. Characteristics of Analytic & Management Information
Information is
Periodical
Output of computation or aggregations
Reflects objectives or actual data
E.g. data of P& L of an imaginative Car Company come from different transaction processing systems
Sales
Purchasing
Accounting
Etc.
Therefore, the design of BI / MIS :
Is top-own
Defines first target data i.e. the variables that BI should process
Identifies corresponding source data
Defines the process to extract and transform source in target data

6. The 4-layer paradigm of BI /MIS systems
Extraction
DATA ENTRY
BASI DATI OPERATIVE
Transactions Data Bases
Tranformation
Loading
DATA WAREHOUSE
Decision support
engines (DSS)
Presentation / reporting
engine
(EIS, reporting)
Mining & other
application engines
DATA MART

7. The 4-layer paradigm of BI /MIS systems
BI/MIS applications are based on 4 layers
Layer 1 contains source data, typically stored in Transaction Data Base
Layer 2 extracts information, and transforms source data into Multi-key & Time-dependent data
Layer 3 stores such transformed information
Layer 4 processes transformed information according various purposes
Support decisions (DSS)
E.g. define the sale budget
Prepare reports and dashboard (Report)
E.g., sales performance
Mine stored data (Mining)
E.g. identify customer who may churn

8. Business Intelligence : a primer Rev April 2012
Introduction & overview
The paradigm of BI systems
Platforms
Appendix
Review questions

9. “Jones” case study CONTEXT REQUIREMENTS
The Supermarket Chain «Jones» includes 300 shops in 3 regions with 60k items on sale
A POS (Point Of Sale) system supports all activities of each shop :
item receiving,
storing,
scrapping,
selling
Specifically, POS terminals record sales transactions and issue receipts
REQUIREMENTS
Management want to analyze sales
Facts : Sales
Measures: amount, quantity, number of tickets
Analysis dimensions
Date
Item
Shop
Time span : 24 months rolling

10. Level 1 (source data) «Jones» case study
Ticket # 2002a23b11
Store #0021MI
Item Des Price Qty Amount
#190 Pen
#69 Mat
#90 Lib
TOTALE
Payment Fidelity P.
Date
Item Master Data
# Item
# Store
Description
Price
Qunatity mesuere
Stock on hand
Stock at the beginning of the day
Average forecasted dayly sale
Receipt Heading
# Store
# Ticket
Amount
Payment
Date
Receipt detail
# Ticket
# Item
Amount
Qty 22

11. TRANSACTIONS DATABASES
Level 2
Extraction includes
Select source data
Check and clean source data (data cleaning o data cleansing)
Staging of extracted data (as needed)
Log of extractions
Extraction can be
Automatic: a batch procedure that runs periodically (e.g. daily, weekly, monthly)
Interactive: integrates and fixes automatic data
ETL can use intermediate databases
Staging Area : where extracted data are temporarily parked (e.g. Data of each individual shop)
Operational Data Store (ODS): where granular data are stored and reconciled for future use (e.g. receipt data)
DSS
Report/
dashboard
Mining &
other
DATA MART
DATA WAREHOUSE
LOADING
DATA ENTRY
TRANSFORMATION
EXTRACTION
TRANSACTIONS DATABASES 23

12. TRANSACTIONS DATABASES
Level 3
Data are stored in Data Warehouse and Data Marts
A Data Warehouse is a “subject-oriented, integrated, time-variant (temporal), non volatile collection of summary and detailed data, used to support strategic decision-making process for the enterprise” (Inmon 1996)
Data Mart is a smaller warehouse, often a subset or extraction of a warehouse.
Warehouse e Mart typically adopt different data schemas
DSS
Report/
dashboard
Mining &
other
DATA MART
DATA WAREHOUSE
LOADING
DATA ENTRY
TRANSFORMATION
EXTRACTION
TRANSACTIONS DATABASES 25

13. Level 3 : Data Warehouse
Key table 1
Key 1
Attribute 1
Attribute 2
Attribute ….
Key table …
Key …
Attribute 1
Attribute 2
Attribute ….
Fact table
Key 1
Key 2
Key …
Measure 1
Measure 2
Measure ….
Key table 2
Key 2
Attribute 1
Attribute 2
Attribute ….
The warehouse is typically implemented by relational database, whose schema reflects the corresponding DFM (Dimensional Fact Model).
In relational schemas:
Fact tables:
Store the value of facts (measures)
Are identified by multiple keys (K>= 2)
Key tables
Describe the attributes of dimensions

14. Level 3: Data Warehouse: star schema Jones case study
Shop
Shop#
Description
Shop-class
ZIP-code
Item
Item#
Billing-metric
Item description
Bar-code#
Package qty
Package-class
Supplier-brand
Item-class
Sales
Date#
Item#
Shop#
Sales amount
Sales qty
Number of receipts
Time
Date#
Week-day
Flag work/holyday for local calendar
Date in muslim calendar
Flag work/holyday for muslim calendar
A simple implementation of the DFM is a STAR schema where key tables are implemented only for immediate keys
Further analysis / segmentation is obtained by queries on attributes of key tables

15. Level 3 : Data Warehouse : Snow flake schema Jones Case study
Sales
Shop
ZIP
Province-region
Area
Time
Date
Holiday
Muslim date
Chinese date
Week-day
Item
Class
Super-class
Supplier
A full implementation of the DFM requirements implies a snow flake schema with a key table for every hierarchy node

16. Level 3: design steps
The process from extraction up to data warehouse creation is supported by warehouse building tools that are incorporated in most BI platforms 2
Target Data design 3
Mapping of Source Data into Target Data 4
ETL code generation 5
Creation of Data Warehouse 1
Source Data Base Identification
The easiest way to understand what a tool can do is to apply it to real-life tasks and problems. This slide illustrates some of the typical tasks performed when designing, building and maintaining a data warehouse with Warehouse Builder. The following slides cover the individual steps in more detail. 6
Data extraction

17. Level 3: design steps : detail
The easiest way to understand what a tool can do is to apply it to real-life tasks and problems. This slide illustrates some of the typical tasks performed when designing, building and maintaining a data warehouse with Warehouse Builder. The following slides cover the individual steps in more detail.

18. TRANSACTIONS DATABASES
Level 3: Data Mart
DSS
Report/
dashboard
Mining &
other
DATA MART
DATA WAREHOUSE
LOADING
DATA ENTRY
TRANSFORMATION
Data mart store frequently accessed information
From a same warehouse multiple data marts can be created
Data marts are typically implemented by hypercube (OLAP technology)
EXTRACTION
TRANSACTIONS DATABASES 30

19. From a same warehouse multiple data marts can be created
Level 3: Data Mart
Customer History
Sales Analysis
Accounting
Marketing
From a same warehouse multiple data marts can be created
Data Warehouse
Shop

20. Level 3: Data Mart : Hyper-cube : display
Columns
Pages
Facts

21. Level 3: Data Mart : Hyper-cube : logic
Fact
An hypercube is a matrix of tables
A Fact (e.g. Sales) is identified in a multidimensional space whose axes are Analysis Dimensions (e.g. Shop, Time, Item)
An hypercube enables to instantly retrieve complex information e.g. :
Sales in last Year (aggregation of Time)
by Region (=aggregation of Shops)
by Category (= aggregation of Product)
Sales
Shop
Item
Time
Event
Quantity = 20
Amount= 100
Dimension

22. Level 3: Data Mart : Hyper-cube : logic
Shops
Item
MB MB MB41000
Date
Jan Feb Mar Apr
Shop
Item
Month
BUDGET
MB21000
MB31000
MB41000
0601
0602
Jan
Feb 50 55 60 65 45 70 75
ITEM’
SHOP
OLAP dimensions = warehouse key
MONTH

23. Level 3: Data Mart : Hyper-cube : logic
Dimension
Hierarchy
Dimensions are arranged in «aggregation hierarchies» (roll-up)
Levels of hierarchies are called «dimensional attributes»
A multidimensional analysis is performed by navigating trough aggregation levels of dimensions
Product
Type
Category
Svelto ….
Washing
powder
Ajax
House
Cleaning
Dash …
Soap
Palmolive
Dairy
All Products
Bread &
Biscuit
Food
Drinks
Tools
Hardware
Nuts &
bolts

24. Level 3: Data Mart : Hyper-cube : implementation
A wise approach to implement multidimensional information is to have an hyper-cube for each measure
This easies arithmetic operations and keeps hyper-cubes light
FACT
TIME
Date
Item
Shop
Sales-amount
Sales-qty
Receipt-number
ITEM
Shop
Prodotto (ch)
Prodotto attributi (da def.)
PuntoVendita (ch)
PuntoVendita attributi (da def.)
Tempo (ch)
Tempo attributi (da def.)
Time
Time
Time
Sales-qty
Sales-amount
Shop
Shop
Shop
Receipt-number
Item
Item
Item 32

25. TRANSACTIONS DATABASES
Level 4
It processes information for management from various perspectives
Define / assess decisions and program (DSS)
Present information with a friendly navigation that enables roll up and drill down (EIS & dashboard)
Produce structured reports (reporting)
Identify trends an pattern in stored information (mining and profiling)
DSS
Report/
dashboard
Mining &
other
DATA MART
DATA WAREHOUSE
LOADING
DATA ENTRY
TRANSFORMATION
EXTRACTION
TRANSACTIONS DATABASES 30

26. Leve 4 : reporting Information distribution and privileges handling
Format editing
Semantic Layer
Data Marts
Data Bases
Data warehouse

27. Level 4: reporting : semantic layer
Purpose: to map data from heterogeneous sources
Generally semantic layer includes a set of types e.g.:
Dimensions (= warehouse keys)
Dimensions attributes ( = key attributes)
Measures and Facts

28. Level 4: reporting : format editing
Includes editing functions by which report pages are defined.
He content of the report is obtained by dragging an dropping information item from the catalogue of the semantic layer
Further activities manage the layout of pages

29. Level 4: reporting : information distribution

30. Level 4 : DSS
A DSS is a computer based application designed to support semi-structured management decisions by
Searching and analyzing information on a collection of sources
Compute and assess results (e.g. sensitivity analysis)
Typical application fields are:
Planning
Budgeting
Optimization
Funding and Investment Decisions
ERP / CRM vendors offer DSS suites for corporate planning as Oracle’s EPM and SAP’s BO

31. Level 4 : DSS : an example (budgeting)
The control system produces monthly a financial report and a report with physical performance indicators (KPI)
Financial report and KPI report are on 5 dimensions:
Time
Cost centers
Item
Sales channel
Activity
Sales data come from the Sales systems and are stored in a data mart; the same approach is also for sales budget, actual costs and budget costs
Data marts are merged in two hyper-cubes, respectively KPI and Financial.
Over hyper-cubes a software processes reports on P&L, A&L, Cashflow, KPI

32. Level 4 : Analysis Engines
Data mining applications for research and marketing are designed for
Discover in a data base relations and associations previously unknown (“data mining helps end user extract useful business information from large databases” (Berson 1997)).
Mining software is a key in marketing to calculate predictive indicators as
Churning,
Fraud risk,
Saving attitude,
Economic potential etc.
Customer Profiling systems (Analytic CRM)

33. Business Intelligence : a primer Rev April 2012
Introduction & overview
The paradigm of BI systems
Platforms
Appendix
Review questions

34. BI solutions are offered by all main vendors
BI is 5-10% of the ES market
Main vendors offer BI products & applications
ES vendors
Oracle: the largest DB vendor
products on Warehousing and applications from vendors acquired (Essbase, Hyperion )
Applications: EPM analogous of SAP’s SEM
SAP: the largest ERP vendor
Applications: Strategic Enterprise Management (SEM) to support the entire management and analysis life cycle
Products : Crystal report, Business Object (founder of reporting paradigm)
Microsoft : Office products , SQL server family
BI vendors
SAS: founder of BI and the largest BI independent vendor, offers a wide range of applications by industry and business area, and specific solutions
Microstrategy
Open source platforms: e.g. Pentaho

35. Business Intelligence Platforms : SAS
By industry …
Education
Financial Services
Government
…..
By solution
Analytics
Business Analytics
Business Intelligence
Customer Intelligence
Data Management
Fraud & Financial Crimes
High-Performance Analytics
IT & CIO Enablement
On Demand Solutions
Performance Management
Risk Management
SAS® 9.3
Supply Chain Intelligence
Sustainability Management
Featured solutions
SAS® 9.3
SAS® Clinical Data Integration
SAS® Curriculum Pathways®
SAS® Enterprise Guide®
SAS® Enterprise Miner™
SAS Fraud Framework for Government
SAS® High-Performance Analytics
SAS® Inventory Optimization
SAS® OnDemand for Academics
SAS® Social Media Analytics
SAS® Text Analytics
SAS® Visual Data Discovery

36. Business Intelligence : a primer Rev April 2012
Introduction & overview
The paradigm of BI systems
Platforms
Appendix
Review questions

37. Data Warehouse and Data Mart vs Database
Conceptual modeling (Rich Semantic Layer)
ERA
DFM
Information type (Master, Event, Analysis)
Master + Event
Analysis
Information organization
Normalized (e.g. 3NF)
Star or snowflake
Hypercube
Data schema
Relational
OLAP or Relational
Processing orientation
Create + Update
Read
Typical data operations
Insert one individual record or modify one or multiple records
Access a vector of records Roll-up, Drill down, Dice
Access one ore multiple a vector of records
Roll-up, Drill down, Dice
Transaction example
Enter a customer order
Segment customer in Italy with a degree of loyalty >70% by age and region

38. Business Intelligence : a primer Rev April 2012
Introduction & overview
The paradigm of BI systems
Platforms
Appendix
Review questions

39. Review questions
Illustrate the input, process and output of the four layers of BI systems
What is an Hyper-cube ?
What is a data mart?
What is a data warehouse? Compare data warehouse versus classic database in terms of
Conceptual modeling (Rich Semantic Layer)
Implementation (DB schema)
Information type (Master, Event, Analysis)
Processing orientation