chapter 4

Information Technology Foundations-BIT 112

CHAPTER 4

Data and Knowledge Management


2

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


3

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.


5

Examples of Data Sources

E-mails

Credit card swipes RFID tags Digital video surveillance

Radiology scans

Blogs


6

Chapter Opening Case

Push Model

Products


7

Chapter Opening Case

Pull Model

Orders


8

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.


9

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/


10

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


11

Data, Information, Knowledge, Wisdom

• Putting data, information, knowledge, and wisdom into perspective.


12

What is meaning Data, Information, Knowledge, and Wisdom ?• At your tables, take a few minutes and try to define

these terms.


13

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


14

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.


15

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.


16

Database Management Systems


17

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.


18

Hierarchy of Data for a Computer-Based File


19

Data Hierarchy (continued)

Bit (binary digit)

Byte (eight bits)


20

See Digital Data Representation Handout

• Review Digital Data Representation Handout


21


• Example of Field and Record


22


Example of a Database Form.


23

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


24

Entity Example

Entity Attributes

Instances

MOVIEMovie Number Name Rating Rental Rate

12345345 Die Hard PG13 $3

23456781 Wings PG $2

65656565 Black Beauty G $2

CUSTOMER

Cust Number Name AddressStatus 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


25

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.


26

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.


27

Sample Information Model (Relational - IDEF 1X)

(SET TYPE)


28

Entity-Relationship Diagram Model


29

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 two-

dimensional 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.


30

Example of a Relational Database Table


31

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


32

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


33

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


34

First Normal Form - ExampleRESTAURANT

REST NAME ADDRESS PHONE # EMPLOYEE NAME

BURGER KING TACO HOUSE FISH COMPANY

123 NORTH ST 345 126TH PLACE 77 SUNSET AVE

123-2345 765-8907 395-5682

JOHN, SUE, LISA MARY, BILL ED, SAM, JOSE, RICK

REST NAME ADDRESS PHONE # EMPLOYEE NAME

RESTAURANT

REST NAME ADDRESS PHONE #

EMPLOYEE

EMPLOYEE NAMEREST NAME POSITION

employs


35

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


36

Second Normal Form - ExampleRESTAURANT ORDER

REST NAME SUPPLIER NAME ORDER ITEM SUPPLIER PHONE #


SAM'S PRODUCE SALSA INC. SAM'S PRODUCE

BEEF PEPPERS SNAPPER

123-2345 765-8907 123-2345

REST NAME SUPPLIER NAME ORDER ITEM SUPPLIER PHONE #

fills

RESTAURANT ORDERREST NAME ORDER ITEM SUPPLIER NAME (FK1)

SUPPLIERSUPPLIER NAME PHONE #


37

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 non-key attributes)


38

Third Normal Form - ExampleRESTAURANT RESERVATIONREST NAME RESERVATION # CUSTOMER NAME CUSTOMER PHONE # TIME # IN PARTY


12 234 88

11:00 AM 2:30 PM 8:15 PM

123-2345 765-8907 123-2345

REST NAME RES # CUST NAME CUST PH # TIME # IN PARTY

makes

F. JONES R. SMITH F. JONES

4 4 6

CUSTOMER

CUSTOMER NAME PHONE #

RESTAURANT RESERVATIONREST NAME RESERVATION # CUSTOMER NAME (FK1) TIME # IN PARTY


39

Example #2

Non-Normalized Relation


40

Normalizing the Database (part A)


41

Normalizing the Database (part B)


42

Summary: Normalization Produces Order


43

Database that Catches Plagiarists P116

A Turnitin originality report

http://www.turnitin.com

http://www.turnitin.com/

http://www.turnitin.com/


44

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.


45

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.


46

Data Warehouse Framework & Views

• Process of building and using a data warehouse.


47

Relational Databases

• First slide of five showing the relationship between relational databases and a multidimensional data structure (or data cube).


48

Multidimensional Database View


49

Equivalence Between Relational and Multidimensional Databases


50



51



52

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.


53

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.


54

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.


55

Relationship Among Executive Management, IT Governance, and Data Governance

• Shows the relationship between data governance and data management.

Master Data Management


56

Data Governance (continued)


57

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)


58

Knowledge Management (continued)

Tacit Knowledge(below the waterline)• subjective or experiential learning.• Examples: experiences, insights,

expertise, know-how, trade secrets, understanding, skill sets, and learning.

Explicit Knowledge (above the waterline)• objective, rational, technical

knowledge that has been documented.• Examples: policies, procedural guides,

reports, products, strategies, goals, core competencies.


59

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.


60

Knowledge Management System Life Cycle

Six steps1.Create knowledge2.Capture knowledge3.Refine knowledge4.Store knowledge5.Manage knowledge6.Disseminate knowledge


61

Chapter Closing Case P. 131

High CVM passengerstravel in style

http://www.continental.com/

chapter 4

Documents

sources of data

data governance4

data warehousing4

wisdomputting data

data needs

data security

importance of data

processing of data