Management Information Systems Chapter 6

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The main challenge of managing ______ involves processing large amounts of data quickly and accurately.

Big Data

The ______ is an open-source framework that allows for the distributed processing of large data sets across clusters of computers.

Hadoop Ecosystem

Analytical tools help organizations to transform raw data into ______ to support decision-making processes.

insights

Business Intelligence Systems enable companies to analyze data and derive ______ that inform business strategies.

intelligence Signup and view all the answers

One potential application of ______ technology is to enhance data security and transparency in transactions.

Blockchain Signup and view all the answers

Big data refers to massive sets of unstructured/semi-structured data from sources like web traffic and social media, and volumes too great for typical ______.

DBMS Signup and view all the answers

A data ______ stores current and historical data from many core operational transaction systems.

warehouse Signup and view all the answers

Hadoop enables distributed parallel processing of big data across inexpensive ______.

computers Signup and view all the answers

In-memory computing uses a computer's main memory (RAM) for data storage to avoid delays in retrieving data from ______.

disk storage Signup and view all the answers

Multidimensional data analysis is also referred to as ______.

OLAP Signup and view all the answers

Data mining and text mining are tools used for consolidating, analyzing, and providing access to vast amounts of ______.

data Signup and view all the answers

______ is a subset of data warehouse that typically focuses on a single subject or line of business.

Data mart Signup and view all the answers

The Hadoop Distributed File System (HDFS) is a key service of the Hadoop ______.

ecosystem Signup and view all the answers

A Database Management System (DBMS) interfaces between applications and physical data ______.

files Signup and view all the answers

In a relational DBMS, data is represented as two-dimensional ______.

tables Signup and view all the answers

The ______ key is used to uniquely identify each record in a relational database table.

primary Signup and view all the answers

The operation that combines relational tables to provide more information than available in individual tables is called a ______.

JOIN Signup and view all the answers

In a relational DBMS, the operation that creates a subset of data of all records that meet stated criteria is referred to as ______.

SELECT Signup and view all the answers

Data manipulation in databases frequently utilizes Structured Query ______ (SQL).

Language Signup and view all the answers

A field in a table used as a lookup field to identify records from the original table is known as a ______ key.

foreign Signup and view all the answers

The capability to create a subset of columns in a table is described as a ______.

PROJECT Signup and view all the answers

Study Notes

Management Information Systems: Managing the Digital Firm - Chapter 6

Chapter 6 covers the Foundations of Business Intelligence, specifically focusing on Databases and Information Management.
The chapter's learning objectives cover issues with traditional file environments, the capabilities and power of relational database management systems (DBMS), the tools for using database information for business improvement and decision making, and the value of policies, data administration, and data quality assurance in firm-level data management.
File Organization Terms and Concepts:
- Database: A collection of related files.
- File: A collection of records of the same type.
- Record: A collection of related fields.
- Field: A collection of characters (words or numbers).
- Entity: A person, place, thing on which information is stored.
- Attribute: A characteristic or quality that describes an entity.
Figure 6.1: The Data Hierarchy illustrates the structure within a database, showing how bytes and bits make up fields, records, and files within a database. Examples of table data are included.
Problems with Traditional File Environments include:
- Files maintained separately by different departments.
- Data redundancy.
- Data inconsistency.
- Program-data dependence.
- Lack of flexibility.
- Poor security.
- Lack of data sharing and availability.
Figure 6.2: Traditional File Processing illustrates how data in different applications (i.e., accounting, human resources, etc.) is not centrally managed.
Database Management Systems (DBMS):
- Centralize data and control redundant data,
- Interface between applications and physical data files,
- Separate logical and physical views of data,
- Control redundancy and eliminate inconsistencies.
- Uncouples programs and data,
- Enables central data and data security management.
Figure 6.3: Human Resources Database with Multiple views illustrates various views of the same database.
Relational DBMS represents data as two-dimensional tables, with each table storing data on entities and attributes.
- Table: A grid of columns (fields) and rows (records).
- Rows (tuples): Records for different entities.
- Fields (columns): Attributes for each entity..
- Key field: Used to uniquely identify each record.
- Primary key: Uniquely identifies each record in a table.
- Foreign key: Used in a different table to link records to the original table.
Figure 6.4 illustrates examples of relational database tables, including the SUPPLIER and PART tables, highlighting key fields and foreign keys.
Operations of a Relational DBMS:
- SELECT: Creates a subset of data based on stated criteria.
- JOIN: Combines relational tables for expanded data.
- PROJECT: Creates a subset of columns in a table.
Figure 6.5 illustrates SQL query examples for SELECT and JOIN statements to retrieve specific data from the PART and SUPPLIER tables.
Figure 6.8 shows an Access Query.
Capabilities of DBMS:
- Data definition capability,
- Data dictionary,
- Querying and reporting, including Structured Query Language (SQL)
- Report generation capabilities for creating polished reports (like Microsoft Access),
Figure 6.6 illustrates Access Data Dictionary Features.
Figure 6.7 shows an example of an SQL Query that uses SELECT, FROM, and WHERE clauses to filter data from multiple related tables.
Figure 6.9 illustrates an unnormalized database relation.
Figure 6.10 illustrates normalized tables.
Figure 6.11 illustrates an Entity-Relationship Diagram (ERD).
Non-Relational Databases (NoSQL):
- More flexible data models.
- Data stored across distributed machines.
- Easier to scale,
- Handle unstructured/semi-structured data.
- Databases in the cloud appeal to start-ups and small businesses.
Blockchain:
- Distributed ledgers in peer-to-peer distributed databases
- Maintains a growing list of records,
- Uses encryption to identify participants,
- Support financial, supply chain, and medical data.
- Foundation of cryptocurrencies like Bitcoin.
Figure 6.12 illustrates how Blockchain works.
The Challenge of Big Data:
- Massive sets of unstructured/semi-structured data from web traffic, social media, sensors, etc. Huge data volumes like petabytes and exabytes.
- Tools and technologies to manage and analyze.
Business Intelligence Infrastructure (3 parts):
- Array of tools for big data/separate systems insights
- Data warehouse (stores current/historical data, standardizes information, analytical tools)
- Data marts (subset of data warehouses, focus on business areas), Hadoop (distributed parallel processing), In-Memory Computing, Analytic Platforms
Figure 6.13 illustrates Contemporary Business Intelligence Infrastructure, showing different data sources (operational, historical, IoT, etc.) feeding a data warehouse, data marts, and data lake.
Analytical Tools:
- Tools for consolidating and analyzing, multidimensional data analysis (OLAP), data mining, text mining, and web mining.
Online Analytical Processing (OLAP):
- Supports multidimensional data analysis.
- Viewing data from multiple dimensions.
Figure 6.14 illustrates a multidimensional data model.
Data Mining:
- Finds hidden patterns/relationships in data,
- Example customer buying patterns,
- Inference rules to predict future behavior.
Text Mining and Web Mining:
- Extracts key elements from unstructured data sets, e.g., sentiment analysis software.
- Discovering patterns and information.
Databases and the Web:
- Companies using websites to provide database access to customers/partners,
- Configuration typically involves web servers, application servers, and database servers.
Figure 6.15 illustrates linking internal databases to the web, showcasing the client, web server, and application server components.
Establishing an Information Policy:
- Rules, procedures, roles for sharing, managing, and standardizing data.
- Data administration policies and procedures for data management.
- Data governance - managing data availability, usability, integrity, and security.
- Database administration for creating and maintaining.
Ensuring Data Quality:
- Data quality audit, cleansing, identifying inaccuracies.

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Description

This quiz focuses on Chapter 6 of 'Management Information Systems: Managing the Digital Firm', which discusses the Foundations of Business Intelligence. It delves into databases, information management, and the significance of relational database management systems (DBMS) in business improvement and decision-making.