ITM 102 Study Guide - Final Guide: Insourcing, Item Number, Database Security
9 May 2018
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ITM 102 FINAL EXAM NOTES (CHAPTERS 7-12)
CHAPTER 7 DATABASES AND DATA WAREHOUSE
Data- raw facts that describe the characteristics of an event
• Characteristics for a sales event: date, item number, item description, quantity ordered,
customer name, shipping details
Information- data converted into a meaningful and useful context
• Information from sales events: best selling item, worst selling item, best/worst customer
Organizational data and information come at different levels, formats, and granularities
Granularity- refers to the extent of detail within the data and information (fine & detail vs. coarse &
abstract)
Coarse granularity- highly summarized data/information Fine granularity- data/info contains great detail
Levels, Formats, and Granularities of Organizational Data and Information
Transactional data- encompasses all of the data contained within a single business process or unit of
work, primary purpose: support performing daily operational tasks
• Organizations capture and store transactional data in databases & use it when performing
operational tasks and repetitive decisions (analyzing daily sales figures & production schedules)
(determining how much inventory to carry)
Analytical information- encompasses all organizational information, primary purpose: support the
performance of higher-level analysis tasks
• Analytical information is used when making important ad hoc decisions (whether the
organization should build a new manufacturing plant or hire additional sales personnel)
Transactional Data = airline ticket, sales receipt, packing slip Analytical Information= product statistics,
trends, future growth, sales projections, product statistics
Real-time data- immediate, up-to-date data Real-time information- immediate up-to-date information
Real-time system- provides real-time transactional data and real-time analytical information in response
to query requests
Characteristics of High Quality Information- accuracy, completeness, consistency, uniqueness, timeliness
Four primary sources of low-quality information:
1. Online customers intentionally enter inaccurate information to protect their privacy
2. Data or information from different systems have different entry standards and formats
3. Call center operators enter abbreviated or erroneous information by accident to save time
4. Third party and external information contains inconsistencies, inaccuracies, and errors
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• Operational-based information systems, such as SCM and CRM systems, access and maintain
transactional data stored in databases
Database- maintains data about various types of objects (inventory), events (transactions), people
(employees), and places (warehouses)
Hierarchal database model- data organized into tree-like structure that allows repeating data using
parent/child relationships
Network database model- a flexible way of representing objects and their relationships
Relationship database model- type of database that stores data in the form of logically related two-
dimensional tables
Entity- a person, place, thing, transaction, or event about which data are stored
Entity Class (table)- a collection of similar entities
Entity Classes of Interest- customer, order, order line, product, distributor
Attribute (fields or columns)- are characteristics or properties of an entity class
Attributes for Customer- customer ID, customer name, contact name, phone
Attributes for Product- product id, product description, price
Primary Key- field that uniquely identities a given entity in a table
Foreign key-primary key of one table that appears as an attribute in another table and acts to provide a
logical relationship between the two tables
Database advantages: increased flexibility, data quality, data security, scalability & performance, reduced
data redundancy
Good Database: handle changes quickly/easily, provide flexibility
Physical View- deals with the physical storage of data on a storage device such as hard sick
Logical View- focuses on how users logically access data to meet their particular business needs
Scalability- refers to how well a system, can adapt to increased demands
Performance- measures how quickly a system performs a certain process or transaction
Data Redundancy- the duplication of data/ storing the same data in multiple places
Data Integrity- measure of the high quality of data
Integrity constraints- rules that help ensure the quality of data
DBMS ensures that users never violent integrity constraints: (1)relational integrity constraints (2)business-
critical integrity constraints
Relational integrity constraints- rules that enforce basic and fundamental data constraints
Business-critical integrity constraints- enforces business rules vital to an organizations success and often
require more insight and knowledge than relational integrity constraints
Database security features- password, access level, access control
Data base management system (DBMS)- is software through which users and application programs
interact with a database
2 Primary ways that users interact with a DBMS- (1)directly (2) indirectly
Data-driven web site- an interactive Web site kept constantly updated and relevant to customer needs by
using a database
Integration- allows separate systems to communicate directly with each other
Without Integrations an organization will (1) spend considerable time entering the same data in multiple
systems (2) suffer from the low quality and inconsistency typically embedded in redundant data
Forward integration- takes data entered into a given system and sends it automatically to all downstream
systems and processes
Backward integration- takes data entered into a given system and sends it automatically to all upstream
systems and processes
Forward and Backward Customer Data Integration- sales system. Order entry system, order fulfillment
system, billing system
Data warehouse- is a logical collection of analytical information- gathered from many different
operational databases- that supports business analysis activities and decision making tasks ( Bill Inmon
1990 Father of Data Warehousing)
Purpose: aggregate transactional data into analytical information through an organization into a single
repository in such a way that employees can make decisions and undertake business analysis activities
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Extraction, transformation, and loading (ETL)- process that extracts data from internal and external
databases, transforms that data into information using a common set of enterprise definitions. And loads
the information into a data warehouse
Data mart- contains a subset of data warehouse information
• Databases contain information in a series of two-dimensional tables
• Information is multi dimensional in a data warehouse and data mart (layers of columns and rows)
Dimension- attribute of information
Cube-representation of multi-dimensional information
Information Cleansing (scrubbing)- process that weeds out and fixes or discards inconsistent, incorrect, or
incomplete information
• Specialized software tools exist that use sophisticated algorithms to parse, standardize, correct,
match, and consolidate data warehouse information
Business intelligence (BI)- refers to applications and technologies that are used to gather, provide, acces
to, and analyze information to support peoples decision making efforts
BI can help with better decisions, a more agile, intelligent enterprise:
Retail and sales: predicting sales, determining correct inventories levels
Banking: forecasting levels of bad loans and fraudulent credit card use
Operations Management: predicting machinery failure, optimizing manufacturing capacity
Characteristics of BI systems: reliable, consistent, understandable, easily manipulated
• Strategic BI, Operational BI, and Tactical BI must work towards a common goal
Data mining- the process of analyzing information to extract insights not necessarily evident from the
information alone
Data-mining tools- use a variety of techniques to find patterns and relationships in large columns of
information and infer rules from them that predict future behavior and guide decision making
Cluster analysis- technique used to divide an information set into mutually exclusive groups so that the
members of each group are as close together as possible to one another, and the different groups are as
far apart as possible
Association detection- reveals the degree to which variables are related and the nature and frequency of
these relationships in the information
Market basket analysis – analyzes such items as Web sites and checkout scanner information to detect
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of products and services
Business Benefits of BI:
1. Single point access for all users
2. BI across organizational departments
3. Up to the minute information for everyone
• Direct quantifiable benefits
• Indirect quantifiable benefits
• Unpredictable benefits
• Intangible benefits
CHAPTER 8 HELPING ORGANIZATIONS ACCESS, SHARE, AND USE INFORMATION
Knowledge management (KM)- involves capturing, classifying, evaluating, retrieving, and sharing
information assets in a way that provides context for effective decisions and actions
Knowledge management systems (KMS)- supports capturing, organizing, and disseminating knowledge
throughout an organization
Explicit knowledge- consists of anything that can be documented, archived, and codified, often with the
help of information systems ( examples: assets like patents, trademarks, business plans, marketing
research, customer lists)
Tacit knowledge- the knowledge contained in peoples heads
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Document Summary
Data- raw facts that describe the characteristics of an event. Characteristics for a sales event: date, item number, item description, quantity ordered, customer name, shipping details. Information- data converted into a meaningful and useful context. Information from sales events: best selling item, worst selling item, best/worst customer. Organizational data and information come at different levels, formats, and granularities. Granularity- refers to the extent of detail within the data and information (fine & detail vs. coarse & abstract) Coarse granularity- highly summarized data/information fine granularity- data/info contains great detail. Levels, formats, and granularities of organizational data and information. Transactional data = airline ticket, sales receipt, packing slip analytical information= product statistics, trends, future growth, sales projections, product statistics. Real-time data- immediate, up-to-date data real-time information- immediate up-to-date information. Real-time system- provides real-time transactional data and real-time analytical information in response to query requests. Characteristics of high quality information- accuracy, completeness, consistency, uniqueness, timeliness.
