Session#5; data resource managment

1 N.Karami, MIS-Spring 2012

Management Information SystemsData Resource Management

Graduate School of Management & Economics

Data Resource Management

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.

Chapter Opening Case

Chapter Opening Case (continued)

Pull Model

Orders

Push Model

Products

Examples of Data Sources

E-mails

Credit card swipes

RFID tags Digital video surveillance

Radiology scans

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

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

Managing Data

Data Management

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

The Data Hierarchy

Designing the DatabaseData 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

Entities & Attributes

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.

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.

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

ER Diagram Model

Database Structures

• Common database structures…– Hierarchical– Network– Relational– Object-Oriented– Multi-dimensional

• 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

Hierarchical Structure

A hierarchical database for a human resources system

• 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

Network Structure

The network data model

• 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

Relational Structure

The relational data model

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)

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

• 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

Database Management Systems (DBMS)

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.

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.

• 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

Business Intelligence (1)

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.

Business Intelligence (2)

Data Warehouses & Data mining

• Tools for analyzing, accessing vast quantities of data:

• Data warehousing

• Data Mart

• Online Analytical Processing (OLAP)

• Data mining

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

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.

• 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

Online Analytical Processing (OLAP)

• 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

Data Mining

Session#5; data resource managment

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