Session#5; data resource managment

Management Information Systems
Data Resource Management
Graduate School of
Management & Economics
Data Resource
Management
1 N.Karami, MIS-Spring 2012

Graduate School of
Learning Objectives
• Recognize the importance of data, issues involved in
managing data and their lifecycle.
• Describe the sources of data and explain how data are
collected.
• Explain the advantages of the database approach.
• Explain the operation of data warehousing and its role
in decision support.
• Explain data mining and how it helps to produce high-quality
data.

Graduate School of
Chapter Opening Case

Graduate School of
Chapter Opening Case (continued)
Products
Orders
Pull Model
Push Model

Graduate School of
Examples of Data Sources
Credit
card
swipes
E-mails
RFID tags Digital video
surveillance
Radiology scans
Blogs

Graduate School of
Managing Data
Difficulties in Managing Data
 Amount of data increases
exponentially.
 Data are scattered and collected
by many individuals using
various methods and devices.
 Data come from many sources.
 Data security, quality and
integrity are critical.
 An ever-increasing amount of
data needs to be considered in
making organizational decisions.
The Data Deluge

Graduate School of
Managing Data
Data Management
Data Management: A structured approach for capturing, storing,
processing, integrating, distributing, securing, and archiving data
effectively throughout Data Life Cycle

Graduate School of
The Database Approach
File Management Systems
File management terms and concepts: Data
Hierarchy
• Bit: Smallest unit of data; binary digit (0,1)
• Byte: Group of bits that represents a single character
• Field (Column): Group of words or complete number
• Record (Row): Group of related fields
• File (Table): Group of records of the same type
• Database: Group of related files

Graduate School of
The Data Hierarchy

Graduate School of
Designing the Database
Data model
• Entity: Person, place, thing, or event about which information
must be kept
• Attribute : A piece of information describing a particular entity
• Key field: Field that uniquely identifies every record in a file
• Primary key
– One field in each table
– Cannot be duplicated
– Provides unique identifier for all information in any row
• Foreign keys: Keys whose purpose is to link two or more tables
together

Graduate School of
Entities & Attributes

Graduate School of
A Database Table
A relational database organizes data in the form of two-dimensional tables. Illustrated
here is a table for the entity SUPPLIER showing how it represents the entity and its
attributes. Supplier_Number is the key field.

Graduate School of
The PART Table
Data for the entity
PART have their own
separate table.
Part_Number is the
primary key and
Supplier_Number is
the foreign key,
enabling users to
find related
information from the
SUPPLIER table
about the supplier
for each part.

Graduate School of
Entity-Relationship Modeling
• Database designers plan the database design in a
process called entity-relationship (ER)
modeling.
• ER diagrams consists of entities, attributes and
relationships.
– Entity classes
– Instance
– Identifiers

Graduate School of
ER Diagram Model

Graduate School of
Database Structures
• Common database structures…
– Hierarchical
– Network
– Relational
– Object-Oriented
– Multi-dimensional

Graduate School of
Hierarchical Structure
• Hierarchical is formed by
data groups, subgroups, and
further subgroups.
– Older system presenting data
in tree-like structure
– Models one-to-many parent-child
relationships
– Found in large legacy
systems requiring intensive
high-volume transactions
(TPS): Banks; insurance
companies
– Examples: IBMs IMS
A hierarchical database for a
human resources system

Graduate School of
Network Structure
• Network allows retrieval of
specific records; allows a
given record to point to any
other record in the database.
– Older logical database
model
– Models many-to-many
parent-child relationships
– Example: Student – course
relationship: Each student
has many courses; each
course has many students
The network data model

Graduate School of
Relational Structure
• Relational organizes data into
two-dimensional tables
(relations) with columns & rows
– Relates data across tables based
on common data element
– Very supportive of ad hoc
requests but slower at
processing large amounts of
data than hierarchical or
network models
– Examples: DB2, Oracle, MS
SQL Server
The relational data model

Graduate School of
Multidimensional Structure
• Variation of relational model
– Uses multidimensional structures to
organize data
– Data elements are viewed as being in cubes
– Popular for analytical databases that support Online Analytical
Processing (OLAP)

Graduate School of
Object-Oriented Structure
• OODM Stores data and
procedures as objects
– Better able to handle graphics
and recursive data
– Data models more flexible
– Slower than RDBMS
– Hybrid: object-relational DBMS

Graduate School of
Database Management Systems
(DBMS)
• Specific type of software for creating, storing,
organizing, and accessing data from a database
• DBMS:
• Provides all users with access to all the data.
• Uncouples programs from data
• Increases access and availability of data
• Allows central management of data, data use, and security
• minimize the following problems
 Data redundancy
 Data isolation
 Data inconsistency
• Examples of DBMS: Microsoft Access, DB2, Oracle
Database, Microsoft SQL Server, MYSQL

Graduate School of
Human Resources Database with
Multiple Views
A single human resources database provides many different views of data, depending on
the information requirements of the user. Illustrated here are two possible views, one of
interest to a benefits specialist and one of interest to a member of the company’s payroll
department.

Graduate School of
Three Basic Operations of a
Relational DBMS
The select, project, and join operations enable data from two different tables to be
combined and only selected attributes to be displayed.

Graduate School of
Business Intelligence (1)
• Business intelligence: Tools for consolidating,
analyzing, and providing access to large amounts of
data to improve decision making
• Software for database reporting and querying (Ad-hoc
query)
• Tools for multidimensional data analysis (online analytical
processing)
• Data mining
• E.g. Harrah’s Entertainment gathers and analyzes customer data
to create gambling profile and identify most profitable customers

Graduate School of
Business Intelligence (2)
A series of analytical tools works with data stored in databases to find patterns and insights for helping
managers and employees make better decisions to improve organizational performance.

Graduate School of
Data Warehouses & Data
mining
• Tools for analyzing, accessing vast quantities of
data:
• Data warehousing
• Data Mart
• Online Analytical Processing (OLAP)
• Data mining

Graduate School of
Data Warehouse & Data Mart
• Data warehouse
• Database that stores current and historical data that may be of interest
to decision makers
• Central source of data that has been cleaned, transformed, and
cataloged
• Data is used for data mining, analytical processing, analysis, research,
decision support
• Consolidates and standardizes data from many systems, operational
and transactional databases
• Data warehouses use online analytical processing.
• Data mart
• Subset of data warehouses that is highly focused and isolated for a
specific population of users

Graduate School of
Components of a Data
Warehouse
The data warehouse extracts current and historical data from multiple operational systems
inside the organization. These data are combined with data from external sources and
reorganized into a central database designed for management reporting and analysis. The
information directory provides users with information about the data available in the
warehouse.

Graduate School of
Online Analytical Processing
(OLAP)
• Supports multidimensional data analysis,
enabling users to view the same data in different
ways using multiple dimensions
• Each aspect of information—product, pricing, cost, region,
or time period—represents a different dimension
• E.g. Comparing sales in East in June vs. May and July
• Enables users to obtain online answers to ad hoc
questions such as these in a fairly rapid amount
of time

Graduate School of
Data Mining
• Finds hidden patterns and relationships in large
databases and infers rules from them to predict future
behavior
• Types of information obtainable from data mining
• Associations: Occurrences linked to single event
• Sequences: Events linked over time
• Classifications: Patterns describing a group an item
belongs to
• Clusters: Discovering as yet unclassified groupings
• Forecasting: Uses series of values to forecast future values

Session#5; data resource managment

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