Transcript Chapter Opening Case P104 - MIS315-05

Information Technology Foundations-BIT 112
CHAPTER 4
Data and Knowledge
Management
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Chapter Outline
• 4.1 Managing Data
• 4.2 The Database Approach
• 4.3 Database Management Systems
• 4.4 Data Warehousing
• 4.5 Data Governance
• 4.6 Knowledge Management
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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 governance and how it helps to produce
high-quality data.
• Define knowledge, and describe different types of
knowledge.
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Examples of Data Sources
Credit card swipes
E-mails
RFID tags
Digital video
surveillance
Radiology scans
Blogs
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Chapter Opening Case
Push Model
Products
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Chapter Opening Case
Pull Model
Orders
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4.1 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.
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Difficulties in Managing Data
• An ever-increasing amount of data needs to be
considered in making organizational decisions.
The Data Deluge
http://www.applimation.com/
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Data Life Cycle
(Figure 4.1)
• Businesses run on data that have been processed or
transformed into information and knowledge.
• Figure 4.1 illustrates the processing of data into
information and ultimately knowledge.
Time
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Data, Information, Knowledge, Wisdom
• Putting data, information, knowledge, and wisdom
into perspective.
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What is meaning Data, Information,
Knowledge, and Wisdom ?
• At your tables, take a few minutes and try to define
these terms.
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What is meaning of Data, Information,
Knowledge, and Wisdom ?
• Data Item
– Elementary description of things, events, activities and
transactions that are recorded, classified and stored but are not
organized to convey any specific meaning.
• Information
– Data organized so that they have meaning and value to the
recipient.
• Knowledge
– Data and/or information organized and processed to convey
understanding, experience, accumulated learning and expertise as
they apply to a current problem or activity.
• Wisdom
– The quality or state of being wise; knowledge of what is true or
right coupled with just judgment as to action
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4.2 The Database Approach
• A database management system (DBMS) provides all
users with access to all the data.
• DBMSs minimize the following data management
problems:
– Data redundancy:
• The same data are stored in many places.
– Data isolation:
• Applications cannot access data associated with other
applications.
– Data inconsistency:
• Various copies of the data do not agree.
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Database Approach (continued)
• DBMSs maximize the following issues:
– Data security:
• Keeping the organization’s data safe from theft,
modification, and/or destruction.
– Data integrity:
• Data must meet constraints (e.g., student grade point
averages cannot be negative).
– Data independence:
• Applications and data are independent of one another.
This means that applications and data are not linked to
each other, so application logic can be changed and the
database does not have to be modified. The inverse is
also true.
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Database Management Systems
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Data Hierarchy
(some DBMS Terminology)
• A bit
– a binary digit, or a “0” or a “1”.
• A byte
– eight bits and represents a single character (e.g., a letter, number
or symbol).
• A field
– a group of logically related characters (e.g., a word, small group
of words, or identification number).
• A record
– a group of logically related fields (e.g., student in a university
database).
• A file
– a group of logically related records.
• A database
– a group of logically related files.
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Hierarchy of Data for a Computer-Based
File
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Data Hierarchy (continued)
Bit (binary digit)
Byte (eight bits)
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See Digital Data Representation Handout
• Review Digital Data Representation Handout
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Data Hierarchy (continued)
• Example of Field and Record
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Data Hierarchy (continued)
Example of a Database Form.
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Designing the Database
• Data Model
– A diagram that represents the entities in the database and their
relationships.
• Data Model Components
– Entity
• An entity is a person, place, thing, or event about which
information is maintained.
• A record is a database instance of an entity.
– Attribute
• A particular characteristic or quality of a particular entity.
– Primary Key
• A field that uniquely identifies a record.
– Non-key Attributes
• A property or characteristic of an entity that is not part of the
key
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Entity Example
Entity
Attributes
MOVIE
Movie Number
Instances
Name
Rating
Rental Rate
12345345
Die Hard
PG13
$3
23456781
Wings
PG
$2
65656565
Black Beauty
G
$2
CUSTOMER
Cust Number
Name
Address
Status
Code
123-345
Tom Jones
12 Oak St
OK
789-789
Mary Sullivan
456 Hill Ave
Pend
567-342
Bob Waters
7676 Scutter Rd
OK
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Entity Attribute Try it …
•
Copy #-The sequence number of the item available for rent. Used to differentiate
multiple copies of a Movie.
•
Customer # (Fk2)-Unique identifier of an individual authorized to rent a Movie.
•
Late Status-A status code identifying if the rental item has not been returned by the
Return Date.
•
Length-The running time in minutes of the item available for rent.
•
Movie #-Unique identifier of the item available for rent.
•
Movie Rental-An instance of a Movie being rented by a customer.
•
Movie Type-The genre or classification associated with the items available for rent.
•
Movie-An item that is available to rent, a motion picture or television production.
•
MPAA Rating-Motion Picture Association of America evaluation. Valid values are:
G, PG, PG-13 R, and NC-17.
•
Rent Date-The date a Movie is rented by a Customer.
•
Return Date-The date a rented Movie is to be returned to the store for restocking.
•
Title-The name of the item available for rent.
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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.
• Other concepts
– Entity classes
• Groups of entities of a certain type.
– Instance
• The representation of a particular entity.
– Identifiers
• Attributes that are unique to that entity instance.
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Sample Information Model
(Relational - IDEF 1X)
(SET TYPE)
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Entity-Relationship Diagram Model
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4.3 Database Management Systems Key
Definitions
• Database management system (DBMS)
– A set of programs that provide users with tools to add, delete,
access, and analyze data stored in one location.
• Relational database model
– A popular type of DBMS that is based on the concept of twodimensional tables.
• Structured Query Language (SQL)
– SQL is a standard interactive and programming language for
querying and modifying data and managing databases.
– The core of SQL is formed by a command language that allows
the retrieval, insertion, updating, and deletion of data, and
performing management and administrative functions.
• Query by Example (QBE)
– allows users to fill out a grid or template to construct a filter or
description of the data one wants.
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Example of a Relational Database Table
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Normalization
• A set of rules for analyzing the attributes of an
information model
–
–
–
–
Eliminate model redundancy
Ensure model consistency
Verify structural correctness
Maximize stability
• However, normalization cannot validate a
model's accuracy in reflecting the business
meaning of the information
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Normal Forms
• Sequential steps for achieving an optimized and
logically desirable information model
• Provides a common foundation from which an
efficient physical database design can be created
• There are six degrees of normal form - the first
three are usually sufficient for most modeling
applications
•
•
•
•
•
•
First normal form
Second normal form
Third normal form
Boyce/Codd normal form
Fourth normal form
Fifth normal form
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First Normal Form - (1NF)
• Every key and non-key attribute of an entity
must be single valued
• No entity instance can have multiple values for
a given attribute
• i.e., The No Repeat Rule
• A violating entity is corrected by removing
repeating or multivalued attributes to another,
dependent (child) entity
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First Normal Form - Example
RESTAURANT
REST NAME
ADDRESS
PHONE #
EMPLOYEE NAME
REST NAME
ADDRESS
PHONE #
BURGER KING 123 NORTH ST
123-2345
TACO HOUSE
345 126TH PLACE 765-8907
FISH COMPANY 77 SUNSET AVE
395-5682
RESTAURANT
REST NAME
ADDRESS
PHONE #
EMPLOYEE NAME
JOHN, SUE, LISA
MARY, BILL
ED, SAM, JOSE, RICK
EMPLOYEE
employs
EMPLOYEE NAME
REST NAME
POSITION
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Second Normal Form - (2NF)
• An entity that is in first normal form and each
non-key attribute is dependent on the entire
primary key
• No non-key attribute instance can be
determined by knowing just part of an entity
instances key
• A violating entity is corrected by removing to
a parent entity any attributes that depend on
only a subset of the primary key
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Second Normal Form - Example
RESTAURANT ORDER
REST NAME
SUPPLIER NAME
ORDER ITEM
SUPPLIER PHONE #
REST NAME
SUPPLIER NAME ORDER ITEM
BURGER KING SAM'S PRODUCE
TACO HOUSE
SALSA INC.
FISH COMPANY SAM'S PRODUCE
SUPPLIER
SUPPLIER NAME
PHONE #
fills
BEEF
PEPPERS
SNAPPER
SUPPLIER PHONE #
123-2345
765-8907
123-2345
RESTAURANT ORDER
REST NAME
ORDER ITEM
SUPPLIER NAME (FK1)
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Third Normal Form - (3NF)
• An entity that is in second normal form and each
non-key attribute is only dependent on the entire
primary key and nothing other than the key
• No non-key attribute instance can be determined
by knowing the value of another non-key attribute
for the same instance
• A violating entity is corrected by removing to a
parent entity any attributes exhibiting transitive
dependencies (non-key attributes that not only
depend on the whole key but also on other nonkey attributes)
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Third Normal Form - Example
RESTAURANT RESERVATION
REST NAME
RESERVATION #
CUSTOMER NAME
CUSTOMER PHONE #
TIME
# IN PARTY
REST NAME
RES # CUST NAME
F. JONES
BURGER KING 12
R. SMITH
TACO HOUSE
234
F. JONES
FISH COMPANY 88
CUSTOMER
CUSTOMER NAME
PHONE #
makes
CUST PH #
TIME
123-2345
765-8907
123-2345
11:00 AM
2:30 PM
8:15 PM
# IN PARTY
4
4
6
RESTAURANT RESERVATION
REST NAME
RESERVATION #
CUSTOMER NAME (FK1)
TIME
# IN PARTY
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Example #2
Non-Normalized Relation
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Normalizing the Database (part A)
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Normalizing the Database (part B)
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Summary: Normalization Produces Order
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Database that Catches Plagiarists P116
A Turnitin
originality
report
http://www.turnitin.
com
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4.4 Data Warehousing
• Data warehouse
– A repository of historical data organized by subject to
support decision makers in an organization.
– Organized by business dimension or subject.
– Data warehouses are multidimensional.
A Data Cube with
three dimensions:
• customer,
• product, and
• time.
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Data Warehousing (continued)
• Data warehouses are historical.
– Historical data in data warehouses can be used for
identifying trends, forecasting, and making comparisons
over time.
• Data warehouses use Online Analytical Processing
(OLAP).
– OLAP involves the analysis of accumulated data by end
users (usually in a data warehouse).
– In contrast, Online Transaction Processing (OLTP) typically
involves a database, where data from business transactions
are processed online and as soon as they occur.
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Data Warehouse Framework & Views
• Process of building and using a data warehouse.
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Relational Databases
• First slide of five showing the relationship between
relational databases and a multidimensional data
structure (or data cube).
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Multidimensional Database View
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Equivalence Between Relational
and Multidimensional Databases
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Equivalence Between Relational
and Multidimensional Databases
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Equivalence Between Relational
and Multidimensional Databases
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Benefits of Data Warehousing
• End users can access data quickly and easily via Web
browsers because they are located in one place.
• End users can conduct extensive analysis with data in
ways that may not have been possible before.
• End users have a consolidated view of organizational
data.
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Data Marts
• A data mart is a small data warehouse, designed for
the end-user needs in a strategic business unit (SBU)
or a department.
• Are far less costly than an enterprise Data Warehouse.
Typically by at least an order of magnitude.
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4.5 Data Governance – An enterprise
wide approach to managing data
• Data governance definition
– An approach to managing data and information across an entire
organization.
• Master Data Management
– A method that organizations use in data governance.
– Comprises a set of processes and tools for collecting,
aggregating, matching, consolidating, quality-assuring, persisting
and distributing data throughout an organization in such a way as
to ensure consistency and control in the ongoing maintenance and
application use of this information.
• Master data
– The set of core data, non transactional data, such as customer,
product, employee, and location, that spans all enterprise
information systems.
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Relationship Among Executive Management,
IT Governance, and Data Governance
• Shows the relationship between data governance and
data management.
Master Data
Management
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Data Governance (continued)
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4.6 Knowledge Management
• Knowledge management
(KM)
– process that helps organizations
manipulate important
knowledge that is part of the
organization’s memory, usually
in an unstructured format.
• Knowledge
– Is something that is contextual,
relevant, and actionable.
– a.k.a., Intellectual capital (or
intellectual assets)
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Knowledge Management (continued)
Explicit Knowledge
(above the waterline)
• objective, rational, technical
knowledge that has been documented.
• Examples: policies, procedural guides,
reports, products, strategies, goals,
core competencies.
Tacit Knowledge
(below the waterline)
• subjective or experiential learning.
• Examples: experiences, insights,
expertise, know-how, trade secrets,
understanding, skill sets, and
learning.
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Knowledge Management (continued)
• Knowledge management
systems (KMSs)
– Systems that use
information technologies
to systematize, enhance,
and expedite intra and
inter-organization
knowledge management.
• Best practices
– The most effective and
efficient ways/processes of
doing things.
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Knowledge Management System Life
Cycle
Six steps
1.Create knowledge
2.Capture knowledge
3.Refine knowledge
4.Store knowledge
5.Manage knowledge
6.Disseminate knowledge
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Chapter Closing Case
P. 131
High CVM passengers
travel in style
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