Management Information System

Contributed by:

Sujud Alhaj Thu, Sep 01, 2022 08:35 AM UTC

Management Information System visualization. It transforms raw data into meaningful information for managers at all levels to improve efficiency and strategic planning. Key components include hardware, software, data, procedures, and users

1. Management Information Systems:
Managing the Digital Firm
Fifteenth Edition
Chapter 6
Foundations of Business
Intelligence: Databases and
Information Management
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2. Learning Objectives
6.1 What are the problems of managing data resources
in a traditional file environment?
6.2 What are the major capabilities of database
management systems (DBMS), and why is a relational
DBMS so powerful?
6.3 What are the principal tools and technologies for
accessing information from databases to improve
business performance and decision making?
6.4 Why are information policy, data administration, and
data quality assurance essential for managing the firm’s
data resources?
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3. File Organization Terms and Concepts
• Database: Group of related files
• File: Group of records of same type
• Record: Group of related fields
• Field: Group of characters as word(s) or number(s)
• Entity: Person, place, thing on which we store information
• Attribute: Each characteristic, or quality, describing entity
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4. Figure 6.1 The Data Hierarchy
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5. Problems with the Traditional File
• Files maintained separately by different departments
• Data redundancy
• Data inconsistency
• Program-data dependence
• Lack of flexibility
• Poor security
• Lack of data sharing and availability
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6. Figure 6.2 Traditional File Processing
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7. Database Management Systems
• Database
– Serves many applications by centralizing data and controlling
redundant data
• Database management system (DBMS)
– Interfaces between applications and physical data files
– Separates logical and physical views of data
– Solves problems of traditional file environment
▪ Controls redundancy
▪ Eliminates inconsistency
▪ Uncouples programs and data
▪ Enables organization to centrally manage data and data
security
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8. Figure 6.3 Human Resources Database
with Multiple Views
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9. Relational DBMS
• Represent data as two-dimensional tables
• Each table contains data on entity and attributes
• Table: grid of columns and rows
– Rows (tuples): Records for different entities
– Fields (columns): Represents attribute for entity
– Key field: Field used to uniquely identify each record
– Primary key: Field in table used for key fields
– Foreign key: Primary key used in second table as
look-up field to identify records from original table
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10. Figure 6.4 Relational Database Tables
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11. Operations of a Relational DBMS
• Three basic operations used to develop useful sets of
data
– SELECT
▪ Creates subset of data of all records that meet
stated criteria
– JOIN
▪ Combines relational tables to provide user with
more information than available in individual tables
– PROJECT
▪ Creates subset of columns in table, creating tables
with only the information specified
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12. Figure 6.5 The Three Basic Operations of a
Relational DBMS
The SELECT operation retrieves just those parts in the PART table whose part number is 137 or 150.
The JOIN operation uses the foreign key of the Supplier Number provided by the PART table to locate
supplier data from the Supplier Table for just those records selected in the SELECT operation.
Finally, the PROJECT operation limits the columns to be shown to be simply the part number, part name,
supplier number, and supplier name (orange rectangle).
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13. Capabilities of Database Management
• Data definition capability
• Data dictionary
• Querying and reporting
– Data manipulation language
▪ Structured Query Language (SQL)
• Many DBMS have report generation capabilities for
creating polished reports (Microsoft Access)
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14. Figure 6.6 Access Data Dictionary Features
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15. Figure 6.7 Example of an SQL Query
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16. Figure 6.8 An Access Query
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17. Designing Databases
• Conceptual design vs. physical design
• Normalization
– Streamlining complex groupings of data to minimize
redundant data elements and awkward many-to-many
relationships
• Referential integrity
– Rules used by RDBMS to ensure relationships between
tables remain consistent
• Entity-relationship diagram
• A correct data model is essential for a system serving the
business well
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18. Figure 6.9 An Unnormalized Relation for
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19. Figure 6.10 Normalized Tables Created
from Order
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20. Figure 6.11 An Entity-Relationship
Diagram
This diagram shows the relationships between the entities SUPPLIER, PART,
LINE_ITEM, and ORDER that might be used to model the database in Figure 6.10.
This graphic shows an example of an entity-relationship diagram. It shows that
one ORDER can contain many LINE_ITEMs. (A PART can be ordered many
times and appear many times as a line item in a single order.) Each LINE ITEM
can contain only one PART. Each PART can have only one SUPPLIER, but many
PARTs can be provided by the same SUPPLIER.
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21. Non-Relational Databases and Databases in
the Cloud
• Non-relational databases: “NoSQL”
– More flexible data model
– Data sets stored across distributed machines
– Easier to scale
– Handle large volumes of unstructured and structured
data
• Databases in the cloud
– Appeal to start-ups, smaller businesses
– Amazon Relational Database Service, Microsoft SQL
Azure
– Private clouds
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22. The Challenge of Big Data
• Big data
– Massive sets of unstructured/semi-structured data
from web traffic, social media, sensors, and so on
• Volumes too great for typical DBMS
– Petabytes (1,000,000 GBs), Exabyte's (1 Exabyte's =
1073741824 Gigabytes) of data
• Can reveal more patterns, relationships and anomalies
• Requires new tools and technologies to manage and
analyze
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23. Business Intelligence Infrastructure (1 of 3)
• Array of tools for obtaining information from separate
systems and from big data
• Data warehouse
– Stores current and historical data from many core
operational transaction systems
– Consolidates and standardizes information for use
across enterprise, but data cannot be altered
– Provides analysis and reporting tools
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24. Business Intelligence Infrastructure (2 of 3)
• Data marts
– Subset of data warehouse
– Typically focus on single subject or line of business
• Hadoop
– Enables distributed parallel processing of big data across
inexpensive computers
– Key services
▪ Hadoop Distributed File System (HDFS): data storage
▪ MapReduce: breaks data into clusters for work
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25. Business Intelligence Infrastructure (3 of 3)
• In-memory computing
– Used in big data analysis
– Uses computers main memory (RAM) for data
storage to avoid delays in retrieving data from disk
storage
– Can reduce hours/days of processing to seconds
– Requires optimized hardware
• Analytic platforms
– High-speed platforms using both relational and non-
relational tools optimized for large datasets
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27. Analytical Tools: Relationships, Patterns,
• Tools for consolidating, analyzing, and providing access
to vast amounts of data to help users make better
business decisions
– Multidimensional data analysis (OLAP)
– Data mining
– Text mining
– Web mining
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28. Online Analytical Processing (OLAP)
• Supports multidimensional data analysis
– Viewing data using multiple dimensions
– Each aspect of information (product, pricing, cost,
region, time period) is different dimension
– Example: How many washers sold in the East in June
compared with other regions?
• OLAP enables rapid, online answers to ad hoc queries
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30. Data Mining
• Finds hidden patterns, relationships in datasets
– Example: customer buying patterns
• Infers rules to predict future behavior
• Types of information obtainable from data mining:
– Associations: Occurrences linked to single event
– Sequences: Events linked over time
– Classification: Recognizes patterns that describe group to
which item belongs
– Clustering: Similar to classification when no groups have been
defined; finds groupings within data
– Forecasting: Uses series of existing values to forecast what
other values will be
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31. Text Mining and Web Mining
• Text mining
– Extracts key elements from large unstructured data
sets
– Sentiment analysis software
• Web mining
– Discovery and analysis of useful patterns and
information from web
– Web content mining
– Web structure mining
– Web usage mining
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32. Databases and the Web
• Many companies use the web to make some internal
databases available to customers or partners
• Typical configuration includes:
– Web server
– Application server/middleware/C GI scripts
– Database server (hosting D BMS)
• Advantages of using the web for database access:
– Ease of use of browser software
– Web interface requires few or no changes to database
– Inexpensive to add web interface to system
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