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Chapter 7

Enterprise Databases, Data Warehouses, and Business

Intelligence

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Objectives Advantages of shared databases. Compare relational vs. object oriented

databases. Describe the differences between schemas,

views, and indexes. Shared vs. distributed databases. Data warehouses and Business Intelligence.

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Enterprise Data – Scaling Up Database: A collection of data and information

describing items of interest to an organization.

Enterprise Database: A collection of data designed to be shared by many users within an organization.

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Both Actual Data and Schema are Shared

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Database Mangement The Functions of Database Management:

Integrating Databases Reducing Redundancy Sharing Information Maintaining Integrity Enabling Database Evolution

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DBMS in Systems

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Enterprise Data Model Enterprise Data Model/Entity Relationship: A

graphical representation of the items (the entities) of interest about which data is captured and stored in the database.

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Schema Schema: The structure of a database.

Schema for Relational Database Relational Database: A database in which

the data are structured in a table format consisting of rows and columns.

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Relational Schema

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Object Orientation Schema for Object-Oriented Database

Object-oriented Database: A database that stores data and information about objects.

Object: A component that contains data about itself and how it is to be processed.

Action/Method: An instruction that tells a database how to process an object to produce specific information.

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Object Oriented Schema

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User views

View: A subset of one or more databases, created either by extracting copies of records from a database or by merging copies of records from multiple databases.

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Enterprise Database StructuresViews (Continued)

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Indexing Index: A data file that contains identifying

information about each record and its location in storage.

Record Key: In a database, a designated field used to distinguish one record from another.

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Enterprise Database StructuresIndexes (Continued)

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Integration Web-based Integration: Makes data from

enterprise databases available to users connecting through the Internet (including enterprise intranets and extranets).

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Databases and the Internet

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Distributed Databases Shared Database: A database shared among

many users and applications.

Distributed Database: A database that resides in more than one system in a distributed network. Each component of the database can be retrieved from any node in the network.

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Partitioning and Replication Partitioning: A method of database distribution in

which different portions of the database reside at different nodes in the network. Vertical Horizontal

Replication: A method of database distribution in which one database contains data that are included in another database. Real time Cascade Batch

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Distribution Strategies Geographic Distribution Strategy: A database

distribution strategy in which the database is located in a region where the data and information are used most frequently.

Functional Distribution Strategy: A database distribution strategy in which the database is distributed according to business functions.

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Designing a Distributed Database Database Directory: The component of a shared

database that keeps track of data and information.

Other Design Factors Storage Costs Processing Costs Communication Costs Retrieval and Processing Reliability Frequency of Updates and Queries

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Data Warehouses and OLAP Data Warehouse: A large data store, designed

from inquiries, that combines details of both current and historical operations, usually drawn from a number of sources.

Online Analytical Processing (OLAP): Database processing that selectively extracts data from different points of view.

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Comparison of Enterprise Databases and Data Warehouses

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Data Warehouse

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Data Warehouses and OLAPDefinition

Data Mining: Uses software designed to detect information hidden in the data.

Data Marts: Processed to focus on a specific area of activities or isolated scientific or commercial processes.

Business Intelligence: Supporting Managerial Decision Making

Issues MIS: Reporting Data-Driven DSS: Business Intelligence Model- -Driven DSS: Models and Modeling GDSS and ESS Case Study: MasterCard

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Decision Levels and Application Systems

Business Operations

TacticalManagement

Strategic

Mgt.

DSS

MIS

Tran

sact

ion

Proc

essi

ng

From R.N. Anthony, Planning and Control Systems: A Framework for

Analysis. Harvard University (1965)

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MIS vs. DSS (Data Driven and Model Driven)

MIS: Provides reports based on routine flow

of data. Assists in general control of the organization. Exception reports used to reduce volume and

focus on items that require management attention.

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MIS Reports Paper or online Can includes text, graphs, or both. Batch vs. Real-time Fixed vs. Ad Hoc (a continuum) Summary vs. Detail Types include:

Exception Trend Validation (such as Trial Balance)

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Data-Driven DSS(a.k.a. Business Intelligence) Also known as. Query/inquiry, Data Mining, and OLAP

(Online Analytical Processing).

Goal is to determine where we are or where we’ve been.

“Business Intelligence” has emerged as common term.

Sometimes also called Datamining, though this generally implies using statistical techniques such as correlation analysis and clustering to find patterns and relationships in large databases.

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Goals of BI Enables users to identify and understand the

key trends and events driving their businesses. Allows employees to sift through and analyze

large amounts of data that the company makes available for them.

Helps business managers at all levels make better decisions quicker.

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What is BI Used For? To perform trend analyses on product, sales,

event (i.e. promotions and advertising campaigns) and financial information. Sales per office or region and then drill down to lower

level details to uncover what is driving the trends. It is also used for exception-reporting and for

budgeting, planning, and forecasting.

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BI Tool Capabilities Support large volumes of data and an unlimited

number of dimensions Can aggregate data

Sums, averages, maximums, minimums, percentage of total, and user-defined functions or rules.

Can contain analytical engines that perform computations. Rankings, ratios, or variances (i.e., This-year-to-last-

year or actual-versus-budget comparisons), Revenue or expense allocations, Currency conversions, etc.

Raises

0500

1000150020002500300035004000

Caulkins Jihong Louganis Naber Spitz Weissmuller

dolla

rs

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

Raise Raise pct Performance

Most BI Tools also include graphics capabilities

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Data Sources for BI Include Relational Data Bases (including Data

Warehouses) Data Marts

Star Schemas Facts and Dimensions

Cubes (Facts and Dimensions)

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Data Warehouse

OLTP Database3NF tables

Operationsdata

Predefinedreports

Data warehouseStar configuration

Daily datatransfer

Interactivedata analysis

Flat files

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Data Warehouses Contain Data from Many Sources (a.k.a. Domains)

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Cube Example: Sales Information

Sales information can be represented in the cube below. You will be able to derive many measures based on the dimensions below

Region

Dep

artm

ent

Time

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Some Leading BI Vendors Enterprise Query/Reporting (RDBMS Based):

Actuate Crystal Reports Information Builders / WebFocus

OLAP (Data Mart and Cube Based): MicroStrategy Hyperion Oracle Business Objects (also includes reporting tools) Cognos (also includes reporting tools)

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Demo Sites Cognos PowerPlay:

http://naade02.msfc.nasa.gov/workforce/index.html

http://www.cognosdemo.com/temple/

Information Builders Web FOCUS: www.informationbuilders.com/test_drive/inde

x.html

www.nyc.gov/html/doh/html/rii/index.html

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For more information . . . Bill Inmon:

http://www.billinmon.com/

Ralph Kimball: http://www.rkimball.com/

Data Management Review: http://www.dmreview.com/

Data Warehouse: http://www.datawarehouse.com

DSS: Decision Support System Models

sales revenueprofit prior154 204.5 45.32 35.72163 217.8 53.24 37.23161 220.4 57.17 32.78173 268.3 61.93 47.68143 195.2 32.38 41.25181 294.7 83.19 67.52

Sales and Revenue 1994

Jan Feb Mar Apr May Jun0

50

100

150

200

250

300

LegendSalesRevenueProfitPrior

Database

Model

Output

data

to a

nalyz

e

results

Optimization

1 2 3 4 5 6 7 8 9 101

3

5

0

5

10

15

20

25

Ou

tpu

t

Input Levels

Maximum

Model: definedby the data pointsor equation

Control variables

Goal or outputvariables

Why Build Models? Understanding

the Process Optimization Prediction Simulation or

"What If"

Scenarios

Prediction

0

5

10

15

20

25

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

Time/quarters

Ou

tpu

t

Moving AverageTrend/Forecast

Economic/regressionForecast

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Marketing Sales ForecastGDP and Sales

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Quarter

GD

P

30

40

50

60

70

80

90

100

Sal

es

GDP

Sales

Forecast

forecast

Note the fourth quarter sales jump.

The forecast should pick up this cycle.

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Time Series Components

time

sales

Dec Dec Dec Dec1. Trend2. Seasonal3. Cycle4. Random

Trend

Seasonal

A cycle is similar to the seasonal pattern,but covers a time period longer than a year.

Collect data over timeIdentify trendsIdentify seasonal effectsForecast based on patterns

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Forecasting Uses Marketing

Future sales Consumer

preferences/trends Sales strategies

Finance Interest rates Cash flows Financial market

conditions

HRM Labor costs Absenteeism Turnover

Strategy Rivals’ actions Technological change Market conditions

Simulation

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Input Levels

Ou

tpu

t

Goal or outputvariables

Results from alteringinternal rules

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Group Decision Support Systems (GDSS)

Interactive computer-based system. Facilitates solution to unstructured problems. Set of decision makers working together as a

group.

EIS: Enterprise Information System (aka Executive Information System and Executive Support System)

Easy access to data

Graphical interface Non-intrusive Drill-down

capabilities

EIS Software from Lightship highlights ease-of-use GUI for data look-up.

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Digital Dashboard

http://www.microsoft.com/business/casestudies/dd/honeywell.asp

Stock market

Exceptions

Plant or management variables

Equipment details

Products

Quality control

Plant schedule