Session#5; data resource managment
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Transcript of Session#5; data resource managment
1 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
Data Resource Management
Data Resource Management
2 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
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.
3 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
Chapter Opening Case
4 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
Chapter Opening Case (continued)
Pull Model
Orders
Push Model
Products
5 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
Examples of Data Sources
E-mails
Credit card swipes
RFID tags Digital video surveillance
Radiology scans
Blogs
6 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
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
7 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
Data Management: A structured approach for capturing, storing, processing, integrating, distributing, securing, and archiving data effectively throughout Data Life Cycle
Managing Data
Data Management
8 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
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
9 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
The Data Hierarchy
10 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
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
11 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
Entities & Attributes
12 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
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.
13 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
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.
14 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
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
15 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
ER Diagram Model
16 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
Database Structures
• Common database structures…– Hierarchical– Network– Relational– Object-Oriented– Multi-dimensional
17 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
• 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
The Database Approach
Hierarchical Structure
A hierarchical database for a human resources system
18 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
• 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 Database Approach
Network Structure
The network data model
19 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
• 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 Database Approach
Relational Structure
The relational data model
20 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
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)
21 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
The Database Approach
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
22 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
• 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)
23 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
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.
24 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
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.
25 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
• 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)
26 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
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)
27 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
Data Warehouses & Data mining
• Tools for analyzing, accessing vast quantities of data:
• Data warehousing
• Data Mart
• Online Analytical Processing (OLAP)
• Data mining
28 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
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
29 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
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.
30 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
• 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)
31 N.Karami, MIS-Spring 2012
Management Information SystemsData Resource Management
Graduate School of Management & Economics
• 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