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ISA - 201 > Chapter 5: Data + Knowledge Management > Flashcards

Chapter 5: Data + Knowledge Management Flashcards

1
Q

Data —-> Information —-> Knowledge

A
  1. Facts are digitized into data (I.e. numbers like 27, 17, etc)
  2. Data is put into context (analysed?), like 22 mph or 17 mph = speeding numbers makes Information
  3. Information is used + put into context of specific? situations to create knowledge (I.. e find reason for why people speeding on a road = Gov can make solutions for those people like alt routes)
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2
Q

Fact

A

The confirmation or validation of an event or an object (I.e. its a fact that Elephants can’t jump)

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3
Q

Information Age

A

The present time, during which infinite quantities of facts are available to anyone who uses a computer

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4
Q

Data

A

Raw facts that describe the characteristics of an event or object (I.e. Unit price, Product #, etc)

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5
Q

Information

A

Data converted into a menaingful and useful context (based on numbers, who is Jerrys best Cu based on sales, its Target at $272,000)

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6
Q

Business Intelligence

A

Info collected from multiple sources such as:

Suppliers

Cu

Competitors

Partners

+ Industries that analyse patterns, trends + relationships for strategic decision making

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7
Q

Knowledge

A

Skills, Experience + Expertise coupled with Info + Intelligence that creates a person’s intellectual resources

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8
Q

Knowledge Worker

A

Individual valued for their ability to interpret + analyse info

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9
Q

Difficulties in Managing Data

A
  1. Data Inc’s exponentially with time
  2. Multiple sources of data
  3. Data Degradation: I.e. Customers move to new addresses, change their names, etc

4.Data Rot: Refers primarily to the problems with the media that data is stored (I.e in old times, magnets could disrupt phones/data storages? Cause problems)

  1. Data Security (protect data?), Quality (good or bad qula data?), and Integrity (truthful/good data or not?)
  2. Gov regulation (like with Patriot Act, Cookies need permission)
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10
Q

Multiple Sources of Data

A
  1. Internal Sources (Corporate Databases + Comp doc’s)
  2. Personal Sources (Personal Thoughts, Opinions + Experiences)
  3. External Sources (Commercial Databases, Gov Reports + Corporate Web Sites)
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11
Q

Data Governance

A

An approach to managing info across an entire org

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12
Q

Master Data

A

Sum of all data relevant to org operations???

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13
Q

Master Data Mamgement

A

Ways to manage said data of org???

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14
Q

Database Approach include

A
  1. Old: Data Hierarchy
  2. New: Relational Database Model
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15
Q

Data Hierarchy

A
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16
Q

Data Hierarchy Term (DHT) 1. Bit (Binary digit)

A

Represents the smallest unit of data a computer can process + it consists only of a 0 and 1 (0 for no electric current, 1 for a current?)

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17
Q

DHT 2. Byte

A

A small group of eight bits that rep’s a single character (i.e. Letter, #, or a symbol)

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18
Q

DHT 3. Field

A

A column of data containing a logically grouping of characters into a word, a small group of words (e.g Last Name, Social Security #, etc)

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19
Q

DHT 4. Record

A

A logical grouping of related fields in a row (I.e. Students name, courses taken, the date + the grade(

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20
Q

DHT 5. Data File

A

Logical grouping of related records is called a data file or a table similar in appearance to an Excel Spreadsheet consisting of multiple columns + multiple rows

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21
Q

DHT 6. Database

A

Logical Grouping of related data files (aka database tables)

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22
Q

Hierarchy vs Databases

A

Seen after this card.

My thoughts/trying to remember class stuff:

Hierarchies make it difficult to access data sometimes/let specific apps access specific data

Database = more streamlined/efficient?

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23
Q

Database Management Systems Minimize these 3 Problems

A
  1. Data Redundancy: The same data is stored in multiple locations (i.e Photos In Phone, that takes up storage)
  2. Data Isolation:

Apps can’t qccess data associated with other apps (like WhatsApp not accessing contacts, you have to type it then)

  1. Data Inconsistency: Various copies of data don’t agree (with each other?)

2.

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24
Q

Database Management Systems (DBMS)

A

A set of programs that provide users with tools to create + manage a database

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25
Q

DBMS Maximizes these 3 things

A
  1. Data Security: Because data are “put in 1 place,” there’s a risk of losing a lot of data at 1 time

Therefore Databases must have extremely high security measures in place to minimise mistakes + deter attacks

  1. Data Integrity: Data meet certain constraints, I.e. there no Alphabetic characters in Social Security number field
  2. Data Independence: Apps + Data are independent of one another, Apps + Data aren’t linked to eachother, so all apps are able to access the same data
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26
Q

Relational Daatbase Model

A
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27
Q

Entity

A

Things like a Car, A person, an Object?

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28
Q

Instance

A

A Specific car/object, like a Blue Car?

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29
Q

Attribute

A

Something that describes an object (color?)

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30
Q

Primary Key

A

Specific, unique representation for an entity (I.e Student ID for Students)

31
Q

Foreign Or Secondary Keys

A

Things that describe object that isn’t primary key (2ndary) + Keys that is Primary in one section (I.e. Student ID) but moved under a different section (I.e. Registration)

32
Q

Structured Query Language

A

Most Popular Query Lang used for interacting with a database

SQL allows people to perform complicated searches by using relatively simple statements or key words

33
Q

Query By Example (QBE)

A

The user fills out a grid or template (also known as: form) to construct a sample or a description of the data desired

34
Q

SQL in a nutshell

A

SQL is a standard computer language for accessing + manipulating relational databases

35
Q

What can SQL do?

A

Execute Queries against a database

Retrieve Data from a database

Insert records in a database….

Etc

36
Q

SQL Commands

A
  1. SQL SELECT FROM Statement

Used to select Data from database

Data returned is stored in a result table, called a data set

SELECT column1, column2

From table___name;
I.e SELECT, Customername, City (these 2 = attributes) FROM Customers (entity table )

= SELECT all Cu’s Names + corresponding cities from the Cu tables

  1. SQL WHERE Clause

Is used to filter records

Is used to extract only those records that fulfill a specified condition

SELECT column1, column2

FROM table__name

WHERE condition;

I.e. = SELECT * FROM Customers

WHERE Country= ‘Mexico’;

Count = Attribute (In column)

Instance = ‘Mexico’ (one instance in column)

37
Q

Entity Relationship Modeling

A

A process by which designers plan + create databases using an entity relationship diagram

38
Q

ER Diagrams

A

Consists of: Entities, Attributes, and Relationships

To properly identify Entities, Attributes, + Relatiobships, Database designers 1st identify Bu rules for the particular data model

39
Q

Bu Rules

A

Precise Descriptions of Policies, Procedures, or Principles that stores + uses data to generate info

40
Q

Data Dictionary

A

Provides Info on each attribute, such as: its name, if its a key, part of a key, or a non-key attribute, the type of data expected (Alphanumeric, Numbering, dates, etc) + Valid values

41
Q

Relationships

A

Illustrate an association between entities

42
Q

Degree of a Relationship

A

Indicates the # of Entities associated with a relationship

43
Q

Degree of a Relationship P2

A

Unary Relationship: exists when an association is maintained within a single entity (I.e. you can only have 1 Mother/Professor and Class?)

Binary Relationship: Exists when 2 Entities are associated (Professor and Class?)

Tertiary Relationship:

Exists when 3 Entities are associated (Professor, Class, Classroom?)

44
Q

Cardinality

A

Refers to the # of times an instance of 1 entity can be associated with an instance in a related entity

Cardinality can be:

Mandatory Single (____|__| |Entity|)

Optional Single (____●_| |Entity|)

Mandatory Many (______|/ |Entity|)
\

or Optional Many (______|●/ |Entity|)
\

45
Q

One to One Relationship I.e

A

|Student| ||__●_| |Parking Permt|

May have –>

<— Must Belong to

Student. Parking Permit.

Student ID # Permit #
(Prim Key?)
Foreign Key (¡)
Student Name. Student Name.

Student Address. Car Type

(Last 3 parts = attributes?)

46
Q

One to Many Relationships I.e

A

|Professor|__|_|______●/__ |Class|
\

—->
Can Teach (many classes?)

<—-
Must have (at least 1?)

Professor:

Professor ID # (Prim Key?)

Professor Name

Professor Department (attributes?)

Class:

Class ID (Prim Key?)

Class name

Class Time

Class Place

47
Q

Many to Many Relationships I.e

A

|Student|__|●______●/__ |Registration|\●_____●| |Class|

/ or \ = /_ = Many Symbol
\

Student —> Regis = Can Have

Student <—– Regis = Can Have

Regist —-> Class = Can Have

Regis <—– Class = Can Have

Studnet:

Student ID # (Primary key?)

Student Name

Student Address (Attributes?)

Registration:

Student ID #
Class ID # (Foregin Keys?)

Registration date (attributes?)

Class:

Class ID # (Prim Key?)

Class Name

Class Time

Class Place (attributes?)

48
Q

Normalization

A

Is a method for Analyzing + Reducing a Relational Database to its Most Streamlined form to ensure Minimum Redundancy, Maximum Data Integrity + Optimal Processing Performance

49
Q

Functional Dependancies

A

A means of expressing that the value of 1 particular Attribute is associated with a specific single value of another attribute

I.e:

A relationship between attributes such that the values in the 1st attribute (or set) ways determine the values in the 2nd attribute (or set)

E->A=/=A->E

a1 + a2 —> b1 Yes

a1 –> b1
\
—> b2 No

50
Q

Normal Forms

A

1st Normal Form:

A table is in 1NF if every field contains only 1 value (Pizza Shop I.e. Each column = one value, like Order #, Pizza Code, etc)

2nd Normal Form:

A Table is in 2NF if it is in 1NF + each Non-Key attribute is only functionally dependant on the entire primary key (this situation only arises with tables that have multiple attribute keys)

(I.e Order #, Pizza Code +
Quantity columns sliced off from 1NF table, put in its own table)

3rd Normal Form:

A table is in 3NF if it’s in 2NF + NO Non-key attribute (or set) is Functionally dependant on any other
non-key attribute (or set)

No FDs among Non-Key Attributes (I.e Order columns in 1 table, Cu info in another, Order - Pizza Info in another, + Just Pizaa info in another)

51
Q

Denormalization

A

Going back from 3NF to 2NF based on Comp resources/need for streamlining

52
Q

Join Process with Tables of 3NF

A

Produce an Order

Step 1:

Order is placed with Order (with info of Order # = Prim Keys, Cu ID = Foreign Key Order Date + Total Price = under Order # + Cu ID)

+ Order Pizza (Order # there as Foreign Key, Pizza Code = Foreign Key, Quantity = under Pizza Code)

S2: From Order - Pizza Table, line is drawn to just Pizza Info Table (Pizza Code there as Prim Key, Pizza Name + Price under that)

S3:

Line from Order to Cu

Cu ID = Prim Key

Cu 1st Name

Cu Last Name

Cu Address

+ Zip Code there under Cu ID

53
Q

Data Warehose

A

A Repository of Historical Data that are organized by Subject to Support Dec makers in the Org

54
Q

Data Mart

A

Low - cost, Scaled down version of Data Warehouse designed for End-user needs in a Strategic Bu Unit (SBU) or Individual department

55
Q

Data Warehouse Framework

A

On Left to Right direction:

Operational Systems/data (i.e. ERP, External Web Docs) get Info? extracted from all of them via Extraction Transformation Load

This puts data in a Federated Data Warehouse

Metadata Repository replicates info to go into Marketing Data Mart,

Supply Chain data goes into Enterprise Data Warehouse

The data (is also replicated/sent into) into Data Marts for diff Bu Functions (i.e Managemnet
Data Mart)

Middle Ware allows for data access (in a language all can understand, like English)

This goes into many diff Business Intelligence things (i.e DSS (Custom - Built Apps (4GL langs), EIS Reporting, Relational Query tools, OLAP/ROLAP, Data Mining, etc)

After going through Internet, data can be put in Web Browser too

56
Q

Basic Characteristics of Data Ware houses + Dt Marts

A
  1. Organized by Bu Dimension or Subject (i.e. Marketing, Finance, etc)
  2. Use Online Analytical Processing (OLAP)
  3. Integrated (not just off in 1 place)
  4. Nonvolatile (doesn’t disappear into thin air?)
  5. Multidimensional (measures +???)
57
Q

Generic Data WaHouse Environment (explanation for written down diagram)

A

Source Systems (ERP, POS, etc)

Data Integration (done via Extraction Transformation Load ?)

Storing the Data (in Data warehouse + Marts)

Metadata (translate data into something understandable?)

Data Quality (must be high quality, low quality dta lead to low qulaity decs)

Data Governance (keeping Dta in good Data security, integrity + independence?)

58
Q

Relational Database

A

i.e. Tables from 2012, 2013 + 2014

Showing Product, Region + Sales in 1 column for each yr

59
Q

Data Cube

A

Organized Data in more 3d way (Region = on the Height/left side, product types on bottom, years at. the left), which can be broken down into smaller cubes for each year

60
Q

Eqivalence between Relational + Multidimensional Databases

A

Data must be the same on either databse

61
Q

Knowledge Management

A

A Process that helps orgs manipulate important knowledge that comprises part of the Org’s ?

62
Q

Knowledge

A

Info that is Contextual, Relevant + Useful (CoReUs)

Its Info in Action

Intellectual Capital (or Intellectual Assets) is another term for knowledge

63
Q

Explicit Knowledge

A

More Objective, Rational + Technical Knowledge (ObRaTe) (i.e. Procedure to make Dairy Queen Ice Cream)

In an org, Explicit Knowledge consist of:

Policies

Procedural Guides

Reports

Products

Strat’s

Goals

Core Competencies

+ IT Infrastructure of the Enterprise

64
Q

Tacit Knowledge

A

The cumulative Store of Subjective or Experiential (SuEx) Learning (i.e. Car Mechanic knowing problem with the car just by sound of the engine)

Tacit Know consists of:

An Org’s Experiences,

Insights,

Expertise,

Know-how,

Trade Secrets,

Skill Sets,

Understanding,

+ Learning

It’s generally imprecise + Costly to transfer

65
Q

Knowledge Management Systems

A

Refer(s?) to the use of Modern Info technologies (i.e. the Internet, Intranet, Extranets, + Databases)

to Systematize, Enhance, + Expedite (SyEnEx) Intrafirm + Interfirm Know Management

66
Q

KMS Cycle

A
  1. Create Knowledge (via data + info?)
  2. Capture Knowledge (in database????)
  3. Refine Knowledge (based on experiences? Some system?)
  4. Store Knowledge (Database again?)
  5. Manage Knowledge (via KMS???????)
  6. Disseminate Knowledge (to people in org??)

(CreCapRefStoManDis Know)

67
Q

Big Data

A

Difficult to define

But we have 2 descriptions:

Gartner Reserach:

“Diverse, high-volume, high-velocity information assets that require new forms of processing to enable enhanced decision
making, insight discovery, and process optimization.
(www.gartner.com)”

Big Data Institute:

“(BD?) Exhibit Variety

Includes: Structured, Unstructured + Semistructured Data

Are generated at High Velocity with an uncertain pattern

Doesn’t fit neatly into Traditional, Structured, Relational Databases

Can be Captured, Processed, Transformed + Analyzed (CaPrTrAn) in a reasonalble amount of time only by sophisticated Information Systems”

68
Q

Defining BData

A

BData generally consists of:

Traditional Enterprise Data

Machine - generated/sensor data (made by machine calculations?)

Social Data (?)

Images Captured by Billions of devices located around the World (Digital Cameras, Camera Phones, Medical Scanners + Security Cameras)

69
Q

Characteristics of Bdata (5 V’s)

A
  1. Volume:

Much Larger Quantity of data than typical for Relational Databases

  1. Variety:

Lots of Diff Data Types + Formats (PNG?)

  1. Velocity:

Data comes at very Fast Rate (i.e. Mobile Sensors, Web Click stream)

  1. Veracity:

Traditional data quality methods don’t apply;

How to judge the data’s accuracy + relevance?

  1. Value:

BData is valuable to the Bottom Line ($$$)

+ for Fostering good Organizational actions + dec’s

(VolVarVelVerVal)

70
Q

Issues with BData

A
  1. Untrusted Data sources (compu’s?)
  2. BD is Dirty:

Dirty Data refers to: Innacurate, Incomplete, Incorrect, Duplicate or Erroneous data

(Innac,Incom,Incor,Dupli,Erron)

BData Changes, especially in Data Streams:

Orgs must be aware that Data Quality in an Analysis CAN Change, becuase the Conditions under which the Data are captured Can Change

71
Q

Managing BData

A

When properly analyzed, BData can reveal valuable Patterns + Info

Database environment (find suitable one??????????)

Traditional Relational Databases vs NoSQL Databases (?)

Open Source Solutions (?)

72
Q

Putting BData to Use

A

Making BData Avaliable

Enabling Orgs to conduct Experiments

Micro-segmentation of Cu’s

Creating new Bu Models

Orgs Can Analyze Far more Data

73
Q

BData used in Functional Areas of the Org

A

HR (workplace stuff?Empl data?)

Product Develoment (see segments of pop/preferences?_

Operations (make Operation channels?0

Marketing (do more Market research?)

Gov Operations (pay taxes faster?)

ISA - 201 (7 decks)

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