CIS 3

Entities

stores info about a person, place, thing, transaction, or event. also referred to as a table

Attributes

data elements associated with an entity

Record

collection of related data elements

Metadata

provides details ab data i.e metadata for an imagine could include its size, resolution, & date created. metadata ab a text doc could contain doc length, data cerated, author's name, & summary

Tools used to retrieve information from a database management system

query-by-example (QBE) structured by example (SQL)

Advantages of relational databases

increased flexibility. increased salability & performance.increased info security. increased info integrity. reduced info redundancy.

Features of relational databases

entities, attributes, keys, relationships

Integrity Constraints

rules that help ensure the quality of info

Business Rules

define how a company performs certain aspects of its business & typically results in either a yes/no or true/false answer i.e stating that merchandise returns are allowed within 10 days of purchase

Database

maintains info ab various types of objects (inventory), events (transactions), people (employees), & places (warehouses)

Data Dictionary

compiles all of the metadata ab the data elements in the data model

Data Element (or data field)

smallest or basic unit of info

Data Models

logical data structures that detail relationships among data elements using graphics or pictures

Foreign Key

primary key of one table that appears as an attribute in another table & acts to provide a logical relationship btwn the 2

Identity Management

deals w identifying individuals in a system such as a country, network, or enterprise & controlling their access to resources within that system by associating user rights & restrictions w the established identity

Information Redundancy

duplication of data or the storage of the same data in multiple places

Primary Key

field or group of fields that uniquely identifies a given record in a table

Redundant Data

can cause storage issues along w data integrity issues, making it difficult to determine which values are the most current or most accurate

Query-by-Example (QBE)

helps users graphically design the answer to a question against a database.

Query-by-Example (QBE)

managers typically interact with this tool

Structured Query Language (SQL)

asks users to write lines of code to answer questions against a database

Structured Query Language (SQL)

MIS professionals have the skills required to code this

Blockchain components

digital ledger

hash & digital signature

miners

complete complex math problems

decentralized

Advantages of Blockchain

prevents double-spend of digital currency

Bitcoin

the most popular and fastest-growing cryptocurrency

Blockchain

distributed ledger that provides a way for info to be recorded & shared by a community

Cryptocurrency

digital $

Digital Ledger

a bookkeeping list of assets, identified ownership, & transactions that record the transfer of ownership among participants. linear list to which info can only be added & older records retained to preserve the full history of each asset

Double-Spend

given set of coins are spent in more than one transaction

Hash

number generated by an algorithm from a text string. also known as a message digest

Miners

complete complex mathematical problems. when all miners arrive at the same solution, a transaction is verified & recorded as a new "block"

Peer-to-Peer

each computer can act as a server for the others, allowing shared access to files and peripherals without the need for a central server

Internal Databases

marketing, sales, inventory, billing

External Databases

competitor info, industry info, mailing lists, stock market analysis

Dirty Data

erroneous or flawed data i.e duplicate, misleading, incorrect, non-formatted, violates business rules, non-integrated, inaccurate

4 Data Mining Techniques

estimation analysis, affinity grouping analysis, cluster analysis, classification analysis

Tradeoffs Fixing Dirty Data

Affinity Grouping Analysis

reveals the relationship between variables along w the nature & frequency of the relationships

Analytics

science of fact-based decision making

Classification Analysis

process of organizing data into categories/groups for its most effective & efficient use

Cluster Analysis

technique used to divide an info set into mutually exclusive group such that the members of each group are as close together as possible & the different groups are as far apart as possible

Data Artist

business analytics specialist who uses visual tools to help people understand complex data

Data Mart

contains a subset of data warehouse info. marketing, inventory, sales

Data Mining

process of analyzing data to extract info not offered by the raw data alone

Data Scientist

extracts knowledge from data by performing statistical analysis, data mining, & advances analytics on big data to identify trends, market changes, & other relevant info

Data Visualization

describes technologies that allow users to see or visualize data to transform info into a business perspective

Data Warehouse

collects info from both internal & external databases in a common location that uses a universal querying tool

Extraction, Transformation, & Loading (ETL)

process that extracts info from internal & external databases, transforms the info using a common set of enterprise definitions, & loads the info into a data warehouse

Estimation Analysis

determines values for an unknown continuous variable behavior or estimated future value

Information Cube

representation of multidimensional info

Information Scrubbing

process that weeds out & fixes or discards inconsistent, incorrect, or incomplete info

Repository

central location in which data is stored & managed

Business Intelligence

info collected from multiple sources such as suppliers, customers, competitors, partners, & industries that analyzes patterns, trends, and relationships for strategic decision making

Data Analytics

combination of high tech systems & math which together are capable of analyzing large amounts of data

Predictive Analytics

goes through data in order to predict what could happen most useful for companies

Prescriptive Analytics

identify the best alternatives to minimize or maximize some objective

Numbers

integer (counting #s) & real (any #)

String

series of characters in order (word, phrase, or message)

Boolean

T/F or ON/OFF value

List

collection of other variables i.e 1) GA, 2) FL, 3) TN type of vector

Methods for assigning variables in R

variable-name = value variable-name

Assignment

operation in R studio

Conditions

if, then

Data Types

Functions

existing programming logic to do a specific activity w data

Loops

running the same code multiple times in a program

Vector

lists & arrays

c()

built-in function that combines data into a list or vector

c(" ", " ", " ")

seq()

built-in function that generates

a numeric vector.

you choose the count-by value.

seq(1, 10, by=2)

Types of Attributes

#s, names, addresses, dates, titles, prices, contact info

Format

way in which something is arranged or set out

Business Rule

statement that defines an aspect of a business

Table

composed of rows & columns that represent an entity

Role of Composite Entities to eliminate redundancy

represent relationships btwn 2 other entities. breaks up many-to-many relationships

Field

characteristic of a table

Common SQL Commands

SELECT, FROM, WHERE, DISTINCT, JOIN, AND

FROM

some table or data frame

JOIN

combining data tables

SELECT

extracts from some list of fields

SQL

small set of commands that let you access & manipulate databases

WHERE

select columns to create a new table

Phases of the System Development Life Cycle

planning, analysis, design, development, testing, implementation, maintenance

Planning Phase

establishes a high-level plan of the intended project & determines project goals.1st & most critical phase

Analysis Phase

firm analyzes its end-user business requirements & refines project goals into defined functions & operations of the intended system

Design Phase

establishes descriptions of the desired features & operations of the system includes screen layouts, business rules, process diagrams & other docs

Development Phase

talks all the detailed design docs from the design phase & transforms them into the actual system

Testing Phase

brings all the project pieces together into a special testing environment to eliminate errors & bugs & verify that the system meets all the business requirements defined in the analysis phase

Implementation Phase

organization places the system into production so users can begin to perform actual business operations w it

Maintenance Phase

organization performs changes, corrections, additions, & upgrades to ensure the system continues to meet business goals

Growth curve for cost of finding & fixing errors

cost exponentially increases the later the error is found in the SDLC; lowest at planning, highest at maintenance

5 Primary Reasons Projects Fail

unclear/missing business requirements, skipped SDLC phases, changing technology, cost of finding errors, balance of the triple constraints

3 Primary Constraints on all Projects

time, cost of resources, scope

Bugs

defects in the code of an info system