Databases: Organizing Data and Information. Files and Databases Data Resource Management a managerial activity that applies information systems technology

Databases:

Organizing Data and

Information

Files and Databases

Data Resource Managementa managerial activity that applies information

systems technology and management tools to manage the company’s data resources

DBA Database Administrator: responsible for the

development and management of the organization’s database

DBAs work with programmers and system’s analysts to design and implement the database

DBAs also work with users and managers of the firm to establish policies for managing an organization’s database

Why is there a demand for more and better data?

Competitive environment powerful workstations that can handle

data quickly More computer literate personnel

Equivalents

DBMS Office

Database File Cabinet

File File Drawer

Record File Folder

Record Attributes

Report

Example of a Data Elements

Hierarchy of Data

Database Files Records Attributes Characters Bits

Hierarchy of Data

DatabaseIntegrated collection of logically related data elementsPersonnel File, Payroll File, Department File

File (eg. personnel file)098-40-1370 Portillo, Talynn 1-20-2010594-39-3948 Baker, Derek 3-24-2009234-34-3483 Fletcher, Kari 2-02-2009457-74-3854 Brown, Peyton 1-23-2009

Hierarchy of Data

Record (Record containing SSN, Last and First name, Hire date)594-41-3955 Bartholomew, Courtney 4-24-07

Attribute (first name)BartholomewCharacters (bytes)01100010 (Letter “B” in Binary Code)

Bits0 or 1

Flat File

A table storing all database information in one large two-dimensional table

Data Management Approaches

Traditional Approach Database Approach

TraditionalFile Processing

Traditional Approach• data are stored, organized and processed in

independent files of data records• Problems:

– data duplication– lack of integration– data dependence

Database Management Approach

Database Approach• files are consolidated into a common pool of

records available to many different application programs

• Requires the use of a database management system (DBMS)

Database Approach Advantages

Minimal data redundancy (data duplication minimized)

Data integration (no lack of integration) Program – data Independence (no data

dependence)

Three models to represent how data can be related or linked

Hierarchical (Tree) Model: Network Model Relational Model

Hierarchical Model

Hierarchical Model

• data is organized in a top-down (inverted tree) structure.

• Parent - child relationships• one-to-many relationship (one parent,

many children)• only one access path to any particular

data element

Network Model

Network Model

an extension of the hierarchical model.

owner - member relationships a member may have several owners more than one path of access

Relational Model

Relational Structure

• Most widely used structure• Data elements are viewed as being stored in

tables• Row represents record• Column represents field• Can relate data in one file with data in

another file if both files share a common data element

Database Development: Data Model A data model is a map or diagram of

entities and their relationships. The entity-relationship diagram is an

abstract and conceptual representation of data

Data modeling usually involves understanding a specific business problem to be solved and analyzing the data and information needed to deliver a solution.

Database Development: E-R Diagrams

• Entity-relationship (ER) diagrams:– Commonly used when designing databases– graphical descriptions of entities and the

relationships between entities.– helps in the design and management of data.

Data in a Database - Entities

Entity is something you collect data about. For example an item, a person, a place, or a thing.

Entities can be thought of as nouns. For example, computer, artist, employee, player.

Entities are represented by rectangles.

Data in a Database - Relationships

Relationships capture how entities are related to one another. For example Entities “artist” and “song” can be related by the Relationship “performs”. Therfore, artist-performs-song.

Relationships can be thought of as verbs and are represented by diamonds. Examples are “performs” “owns” “houses” “supervises”

Entity #2RelationshipEntity #1


Entity #2RelationshipEntity #1

Noun #1 Verb Noun #2

player plays team


• Types of Relationships include:– one-to-one (1:1)– one-to-many (1:N)– many-to-many (N:M)


Entity #2RelationshipEntity #11 1

Entity #2RelationshipEntity #11 M

Entity #2RelationshipEntity #1N M


TeamhousesHome_stadium

Football team and stadium: Big 12

Team has Player

Football team and games: Big 12

player plays game

Football players and games: Big 12

Data in a Database - Representation

Entities and Relationships are represented in a Relational Database in Tables

Each column in a table is called an attribute, which would be a characteristic of an entity (field name)

Each row in a table is called a record or a tuple. Each record consists of many attributes.

Key: a field in a record that is used to uniquely identify the record.

Data in a Database (cont.)

Key Field

Attributes (fields)

Entity: Employee

005-10-6322

534-34-6321

776-54-4525

342-43-6378

12-13-09

2-23-10

11-10-09

01-15-09

Gray Rachel

Hughey Abigail

Morris Kyle

Cobb Cade

Table: player (QB)

001

002

003

004

Brees

Romo

Rodgers

Kaepernick

Table: team

001 001

Table: plays

Team_IDPlayer_ID

002 003003 004004 002

player plays team

001

002

003

004

49ers

Cowboys

Saints

Packers

Jets005

Football Team: Entities

Team

Player

Home_stadium

Games

Conference

Football Team - ER example

Team

N

has

Player

1

Home_stadium

house1

1

GameM

participate

2

partake

Conference

M

1

Data in a Database - Representation

Entities and relationships have attributes which are represented as ovals and are connected with a line to their respected entity

Primary keys (attributes that are unique identifiers) are underlined

Customer

SSN

Name

Football Conference Example

Team

partake

Conference

M

1

Data in a Database (cont.)

Key Field

Attributes (fields)

Table: Team

001

002

003

004

Texas

Texas Tech

USC

Auburn

005 Georgia

001 001

Table: Partake

Conf_IDTeam_ID

001

002

003

004

Big 10

Big 12

SEC

PAC 10

Table: Conference

002 001003 004004 003005 003

Video Rental - ER example

Customer

Movie

Actors

ISBN

SSNDueDate


Customer

N

Rents

Movie

M

SSN

Name

Gender

ISBN

Title

Has

N

M

Actor_ID

Name Actors

ISBN

Actor_ID

100200300400

Keane Stanton MSplawn Reid MUrias Andrea FKitten Kayleen F

Customer

600 Towles Keely F700 Seal Samantha F

200 1-101-2 04/12/15300 1-607-9 10/11/14400 1-702-5 11/28/14

Rents

700 1-699-4 11/26/14

100 1-101-1 04/10/15

1-101-2 Chariots of Fire1-607-91-699-4 Navy SealsMovie1-702-5 The Cat in the Hat

1-101-1 Rudy

Space Jam

1-799-2 Dumb and Dumber


Actor_ID

MovieISBN

Title

Has

N

M

Name Actors

ISBN

Actor_ID

0101010201030104

Carrey JimDiaz Cameron

Dusnt KristenMaguire Tobey

Actor

0105 Stiller Ben0106 Sandler Adam

0001 01020002 01020002 01050003 0103

0001 0101

Has

Movie_ID Actor_ID

0005 01060005 01050006 01030006 0104

0004 0105

0007 0102

Movie

0002 Something About Mary

00030004 Dodge Ball0005 Happy Gilmore

0001 The Mask

Bring it On!

0006 Spider-Man

1998200020041996

1994

20020007 Vanilla Sky 2001

0008 0104

DBMS

Problem with the last tables?

DBMS

• DBMS: software that provides for the creation, implementation, usage, and updating of a database.

• schema: a general description of the entire database that shows all of the record types and their relationships to each other.

• QBE – Query By Example. Used for Query Design• SQL – Structured Query Language• Natural Language Query

Source: Courtesy of Microsoft Corp.

SELECT [Customers].[Company Name],[Customers].[Contact Name]

FROM [Customers]

WHERE not Exists [SELECT [Ship Name] FROM [Orders]

WHERE Month {[Order Date]}=1 and Year{[Order Date]}=2008 and [Customers].[Customer ID]=Orders].{[Customer ID]}

Non-relational databases: “NoSQL”More flexible data modelData sets stored across distributed machines Easier to scaleHandle large volumes of unstructured and structured data (Web, social media, graphics)

Databases in the cloudTypically, less functionality than on-premises DBsAmazon Relational Database Service, Microsoft SQL AzurePrivate clouds

The Database Approach to Data Management

• Capabilities of database management systems– Data definition capability: Specifies structure of database

content, used to create tables and define characteristics of fields

– Data dictionary: Automated or manual file storing definitions of data elements and their characteristics

– Data manipulation language: Used to add, change, delete, retrieve data from database • Structured Query Language (SQL)• Microsoft Access user tools for generating SQL

– Many DBMS have report generation capabilities for creating polished reports (Crystal Reports)


Microsoft Access has a rudimentary data dictionary capability that displays information about the size, format, and other characteristics of each field in a database. Displayed here is the information maintained in the SUPPLIER table. The small key icon to the left of Supplier_Number indicates that it is a key field.

FIGURE 6-6

MICROSOFT ACCESS DATA DICTIONARY FEATURES

Illustrated here are the SQL statements for a query to select suppliers for parts 137 or 150. They produce a list with the same results as Figure 6-5.

FIGURE 6-7

EXAMPLE OF AN SQL QUERY

Illustrated here is how the query in Figure 6-7 would be constructed using Microsoft Access query building

tools. It shows the tables, fields, and selection criteria used for the query.

FIGURE 6-8

AN ACCESS QUERY

• Designing Databases– Conceptual (logical) design: abstract model from business

perspective– Physical design: How database is arranged on direct-access storage

devices

• Design process identifies:– Relationships among data elements, redundant database elements– Most efficient way to group data elements to meet business

requirements, needs of application programs

• Normalization– Streamlining complex groupings of data to minimize redundant data

elements and awkward many-to-many relationships


An unnormalized relation contains repeating groups. For example, there can be many parts and suppliers for each order. There is only a one-to-one correspondence between Order_Number and Order_Date.

FIGURE 6-9

AN UNNORMALIZED RELATION FOR ORDER

After normalization, the original relation ORDER has been broken down into four smaller relations. The relation ORDER is left with only two attributes and the relation LINE_ITEM has a combined, or concatenated, key consisting of Order_Number and Part_Number.

FIGURE 6-10

NORMALIZED TABLES CREATED FROM ORDER

Referential integrity rules• Used by RDMS to ensure relationships between tables

remain consistent

Entity-relationship diagramUsed by database designers to document the data modelIllustrates relationships between entities

– Caution: If a business doesn’t get data model right, system won’t be able to serve business well


This diagram shows the relationships between the entities SUPPLIER, PART, LINE_ITEM, and ORDER that might be used to model the database in Figure 6-10.

FIGURE 6-11

AN ENTITY-RELATIONSHIP DIAGRAM

• Big data• Massive sets of unstructured/semi-structured data

from Web traffic, social media, sensors, and so on• Petabytes, exabytes of data

• Volumes too great for typical DBMS• Can reveal more patterns and anomalies

Using Databases to Improve Business Performance and Decision Making

Business intelligence infrastructureToday includes an array of tools for separate systems,

and big data

Contemporary tools:Data warehousesData martsHadoopIn-memory computingAnalytical platforms


• Data warehouse: – Stores current and historical data from many core

operational transaction systems– Consolidates and standardizes information for use across

enterprise, but data cannot be altered– Provides analysis and reporting tools

• Data marts: – Subset of data warehouse– Summarized or focused portion of data for use by specific

population of users– Typically focuses on single subject or line of business


A contemporary business intelligence infrastructure features capabilities and tools to manage and

analyze large quantities and different types of data from multiple sources. Easy-to-use query and

reporting tools for casual business users and more sophisticated analytical toolsets for power users

are included.

FIGURE 6-12

COMPONENTS OF A DATA WAREHOUSE

HadoopEnables distributed parallel processing of big data

across inexpensive computersKey services

○ Hadoop Distributed File System (HDFS): data storage○ MapReduce: breaks data into clusters for work○ Hbase: NoSQL database

Used by Facebook, Yahoo, NextBio


In-memory computingUsed in big data analysisUse computers main memory (RAM) for data storage

to avoid delays in retrieving data from disk storageCan reduce hours/days of processing to secondsRequires optimized hardware

Analytic platformsHigh-speed platforms using both relational and non-

relational tools optimized for large datasets


• Analytical tools: Relationships, patterns, trends– Tools for consolidating, analyzing, and providing

access to vast amounts of data to help users make better business decisions• Multidimensional data analysis (OLAP)• Data mining• Text mining• Web mining


• Online analytical processing (OLAP)– Supports multidimensional data analysis

• Viewing data using multiple dimensions• Each aspect of information (product, pricing, cost,

region, time period) is different dimension• Example: How many washers sold in East in June

compared with other regions?– OLAP enables rapid, online answers to ad hoc queries


Data mining:Finds hidden patterns, relationships in datasets

○ Example: customer buying patternsInfers rules to predict future behaviorTypes of information obtainable from data mining:

○ Associations○ Sequences○ Classification○ Clustering○ Forecasting


Text miningExtracts key elements from large unstructured data

sets ○ Stored e-mails○ Call center transcripts○ Legal cases○ Patent descriptions○ Service reports, and so on

Sentiment analysis software○ Mines e-mails, blogs, social media to detect opinions


• Web mining– Discovery and analysis of useful patterns and

information from Web– Understand customer behavior– Evaluate effectiveness of Web site, and so on

– Web content mining• Mines content of Web pages

– Web structure mining• Analyzes links to and from Web page

– Web usage mining• Mines user interaction data recorded by Web server


Read the Interactive Session and discuss the following questions

Interactive Session: Technology

Describe the kinds of big data collected by the organizations described in this case.

List and describe the business intelligence technologies described in this case.

Why did the companies described in this case need to maintain and analyze big data? What business benefits did they obtain?

Identify three decisions that were improved by using big data.

What kinds of organizations are most likely to need big data management and analytical tools?

Big Data, Big Rewards

• Databases and the Web– Many companies use Web to make some internal

databases available to customers or partners– Advantages of using Web for database access:

• Ease of use of browser software• Web interface requires few or no changes to database• Inexpensive to add Web interface to system


Users access an organization’s internal database through the Web using their desktop PCs and Web browser software.

FIGURE 6-14

LINKING INTERNAL DATABASES TO THE WEB

Establishing an information policyFirm’s rules, procedures, roles for sharing, managing,

standardizing dataData administration

○ Establishes policies and procedures to manage dataData governance

○ Deals with policies and processes for managing availability, usability, integrity, and security of data, especially regarding government regulations

Database administration○ Creating and maintaining database

Managing Data Resources

• Ensuring data quality – More than 25% of critical data in Fortune 1000

company databases are inaccurate or incomplete– Redundant data– Inconsistent data– Faulty input

– Before new database in place, need to:• Identify and correct faulty data • Establish better routines for editing data once

database in operation


• Data quality audit:– Structured survey of the accuracy and level of

completeness of the data in an information system• Survey samples from data files, or• Survey end users for perceptions of quality

• Data cleansing– Software to detect and correct data that are incorrect,

incomplete, improperly formatted, or redundant– Enforces consistency among different sets of data

from separate information systems


Google, UPS and Visa are using new kinds of data and new tools to improve their operations

Google: analyzes connections between web pages UPS: uses regression to read handwritten zip codes

from envelopes Visa: uses anomaly detection to identify fraud (they

now look at all credit card data instead of sampling)

BIG DATA: How Business Intelligence is Transforming the World

Billions of computers (smartphones, tablets, laptops, supercomputrs, etc.) are part of our lives.

BIG DATA: How Business Intelligence is Transforming the World

In 2010 the global volume of digital data stored and managed was over one trillion gigabytes (about one terabyte per person in the whole world)

By 2020 the number is estimated to be 40 trillion gigabytes.

Therefore, the way that data was analyzed traditionally is no longer valid.

Big Data forces us to come up with new methods to analyze huge data sets

BIG DATA

Amazon and Netflix gather millions of ratings that customers use.

Data is transformed and used in the areas of politics, sports, health care, finance, entertainment, science, industry, etc.

Collecting data like never before creates new opportunities and challenges.

BIG DATA

Volume Velocity and Variety

BIG DATA: 3 V’s

Volume: Can all the works of Shakespeare be stored in a DVD?

All the works of Shakespeare = 10 Mb or 10 million bytes

A DVD can hold 4 Gb or 4 billion bytes Answer: A DVD can hold the equivalent of

400 complete works of Shakespeare

BIG DATA - Volume

Data is accessed multiple times Google alone processes 20 Petabytes every

day. 20 Pb = 20,000 Tb = 20,000,000 Gb. Google processes 1 Exabyte every 50 days. All words ever spoken by mankind is 5

Exabytes Bank of America manages petabytes of data

for advanced analytics

BIG DATA – Volume (continued)

Eight bits = 1 byte. Ten bytes = average word

1 Kb = 1,000 bytes = short paragraph

1 Mb = 1,000 Kb = short novel

1 Gb = 1,000 Mb = 7 minutes HD video

DVD = 4 Gb and Blu-ray = 50 Gb

1 Tb = 1,000 Gb and 10 Tb = all text info in the US Library of Congress

400 Tb = all books ever written

1 Exabyte = 1,000 Petabytes = 1,000,000 Tb. All words ever spoken = 5 Exabytes

BIG DATA – Volume (continued)

YouTube users upload 72 hours of new video content per minute

There are 100,000 credit card transactions per minute in the USA alone

Google receives 2 million search queries per minute 200 million emails are sent per minute in the USA

alone Global banks handle trillions of messages in a single

day’s trading.

BIG DATA – Velocity

Data today is very different than data from yesterday

Jet engines transmit data Smartphones talk to us and answer us GPS information from Cameras, phones,

tablets

BIG DATA – Variety

Data analysis gives you “an answer” not “the answer.” There is so much data that it’s impossible to optimize and get the absolute best answer.

However, data analysis of big data can predict a much better answer than not using it at all.

There is no best tool or method

You probably will not get the data in the way you need it. It may be incomplete, or incorporated in different locations (may need to be merged).

Data may not be easily available. May need to search for it.

BIG DATA – Misconceptions

What data is created when: you rate songs? you use a credit card? you make a cell phone call? you update your status on Facebook?

BIG DATA – Questions

What data is created when: you rate songs? you use a credit card? you make a cell phone call? you update your status on Facebook?

BIG DATA – Questions

Data Collection is for Big Companies and for Individuals

Medical data and History => Personalized medicine

Financial details

BIG DATA - Collecting

Collecting data of every day life Eating Sleep Activity levels Moods Habits Communications


Digital DevicesExercise data: how far you runHow far you bikedHow fast you swamBreak down information by mile or minuteHow many calories you burnedIdle time during the dayWhether you are climbingHeart rate


Does Big Data mean insight? Not necessarily “Haystacks without Needles” (Darian Shirazi)

= not knowing what you are looking for and thinking that Big Data will solve the problem

Having a clear goal or question to be answered will lead to creativity when collecting data


List of contacts (addresses, phone numbers, email addresses), recipes, etc.

20% of all data Computer generated Human generated Combination (doctor enters information into

system and appears in combination with scanned data)

BIG DATA – Structured Data

Billy Beane’s method of data analysis led the Oakland A’s to victory while cutting costs

2011 Moneyball Building rosters with conventional wisdom

vs. thinking outside the box With Data Analysis, Beane bought players

with the lowest payroll in baseball

BIG DATA – Sports

(Runs2)/(Runs2+Runs allowed2) = Win % Example: 2002 Oakland A’s scored 800 runs and

allowed 654 => (8002)/(8002+6542) = 59.94 percent of the time will win

Out of 167 games, 59.94% = 97.1 games (they won 103 games)

To win 99 games => 99/167 = (Runs2)/(Runs+6452) Solve for Runs = 808 runs needed

BIG DATA – Pythagorean Expectation

Two thirds of Tech firms are keeping more than one year worth of data on-line

43% have more than 3 years worth of data on-line

BIG DATA – Usage

Burst into the headlines in December 2011 offering iPhone and Android users $5 off for sharing in-store prices while shopping.

Result: increase Amazon’s Price Check App bar-code scanning application AND collecting comparative intelligence on store prices.

Using the Amazon app shoppers scan a barcode, take a picture of the item or conduct a text search to find the lowest price

BIG DATA – Usage: Amazon

Measuring influence across the social web by storing, processing, and analyzing real time social media data streams.

BIG DATA – Usage: Social Media

Recommending complementary products based on predictive analytics for cross selling.

Result: increase an average order size

BIG DATA – Usage: Cross-selling

Using behavioral patterns, credit card companies are detecting fraud (including tax and claims) in online systems in real time

BIG DATA – Usage: Fraud

Can collect tweets from million of users and analyze terms to identify who to follow to measure conversation, news, interest, and activity.

BIG DATA – Usage: Human Behavior

Collect information on keywords that work best to influence surfers into shopping.

They can collect information on their Tweets or Facebook posts in addition to the key word entered on the site search window.

BIG DATA – Usage: Keyword Campaigns

Management Information Systems by Laudon and Laudon

Big Data: How Data Analytics is Transforming the World by Chartier

For Big Data Analytics There’s No Such Things as Too Big

Bibliography

Documents

Databases: Organizing Data and Information. Files and Databases Data Resource Management a managerial activity that applies information systems technology