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Business Intelligence Week 2

Discussion: Please refer to the attachment Discussion#2_Question file for the question. The discussion should be a minimum of 300 words and should meet all the requirements given by the professor.
Paper: Please refer to the attachment paper#2_Question file for the question. The paper should be a minimum of 4 pages excluding references and should meet all the requirements given by the professor. Especially in the conclusion section, I got the below comments from the professor please try to follow the guidelines and make sure to rectify the comments below.

Comments: Thank you for taking the time to complete your assignment and based on my review, you did a good job on this assignment. For example, you used multiple sources of research to support your case analysis and your conclusions were also good; however, do not forget to visually represent your ideas through the use of created diagrams which may represent any process or operation to support a potential solution to the cases. As a suggestion, use the “Smart Art” tools in Microsoft Word and if you have never used these tools, you can find demonstrations at

www.youtube.com

using a keyword search “Microsoft Word Smart Art Tools.”

9/1/2020 Graded Assignment: Knowledge and Skills Paper – Fall …

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Graded Assignment: Knowledge and Skills Paper

Fall 2020 – Business Intelligence (ITS-531-A05) – First Bi-Term Content Week 2: 08/31/2020 – 09/06/2020

Graded Assignment: Knowledge and Skills Paper

W2: Paper (Click Here to Submit)

1. Research some data warehouse vendors and obtain information about their
products. Give special attention to vendors that provide tools for multiple
purposes, such as Cognos, Software A&G, SAS Institute, and Oracle. Free
online demos are available from some of these vendors. Download a demo or
two and try them and describe your learning experience and review of this
products.

2. Download an information visualization tool, such as Tableau, QlikView, or
Spotfire. If your school does not have an educational agreement with these
companies, then a trial version would be sufficient for this exercise. Use your
own data (if you have any) or use one of the data sets that comes with the
tool (they usually have one or more data sets for demonstration purposes).
Study the data, come up with a couple of business problems, and use data and
visualization to analyze, visualize, and potentially solve those problems.

3. Important Note: With limited time for a college class, perfection is not
expected but effort to be exposed to various tools with attempts to learn
about them is critical when considering a career in information technology
associated disciplines.

W2: Paper

Graded Assignment: Knowledge and Skills Paper

Paper Section 1: Reflection and Literature Review

Using Microsoft Word and Professional APA format, prepare a professional written
paper supported with three sources of research that details what you have learned
from chapters 3 and 4. This section of the paper should be a minimum of two pages.

Paper Section 2: Applied Learning Exercises

In this section of the professional paper, apply what you have learned from chapters
3 and 4 to descriptively address and answer the problems below. Important Note:
Dot not type the actual written problems within the paper itself.

Important Note: There is no specific page requirement for this section of the
paper but make sure any content provided fully addresses each problem.

Paper Section 3: Conclusions

After addressing the problems, conclude your paper with details on how you will use
this knowledge and skills to support your professional and or academic goals. This
section of the paper should be around one page including a custom and original
process flow or flow diagram to visually represent how you will apply this knowledge

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going forward. This customized and original flow process flow or flow diagram can
be created using the “Smart Art” tools in Microsoft Word.

Paper Section 4: APA Reference Page

The three or more sources of research used to support this overall paper should be
included in proper APA format in the final section of the paper.

Paper Review and Preparation to submit for Grading

Please make sure to proof read your post prior to submission. This professional
paper should be well written and free of grammatical or typographical errors. Also
remember not to plagiarize!!!!!!!!!!!!

Important Reminder: Assessment of discussion boards and other writing
assignments account for 75% of overall grading and below are how grades will be
assessed for this assignment:

Assessment Criteria Possible
Points

Points
Earned

Student included a minimum of “2” body pages of
written content supported with “3” academic sources
of research offering a detailed reflection and
literature review of learning from chapters 3 and 4

18.75

Student in scholarly detail addressed and answered
all exercises or problems demonstrating application
of knowledge and skills learned from chapters 3 and
4.

12.5

Student in scholarly detail offered conclusions
detailing how knowledge and skills learned from
chapters 3 and 4 will support continued professional
and academic growth. Student also prepared process
flow or flow diagrams to visualize these conclusions.

12.5

Student included a paper professionally formatted
using APA and free of grammar and spelling issues

6.25

Student successfully completed and successfully
submitted this paper by the Sunday due date.

12.5

Total Earned points 62.5
Comments:

9/1/2020 Graded Discussion: Chapter 1, 2, 3, and 4 Case Studies –…

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Graded Discussion: Chapter 1, 2, 3, and 4 Case Studies

Fall 2020 – Business Intelligence (ITS-531-A05) – First Bi-Term Content Week 2: 08/31/2020 – 09/06/2020

Graded Discussion: Chapter 1, 2, 3, and 4 Case Studies

W2: Discussion (Click Here To Post

)

W2: Discussion Case Studies

Graded Assignment: Case Studies – (Follow all steps below)

For students having last names beginning with A through M carefully review and
read both end of chapter application cases from chapters 1 and 2 from the following
required book for this class.

For students having last names beginning with N through Z carefully review and read
both end of chapter application cases from chapters 3 and 4 from the following
required book for this class.

Sharda, R., Delen, D., & Turban, E. (2015) Business intelligence and analytics:
Systems for decision support (10th ed.). Boston: Pearson.

Digital: ISBN-13: 978-0-13-340193-6 or Print: ISBN-13: 978-0-13-305090-5

(

After reading and analyzing both case studies, address all case study questions
found within the case studies in scholarly detail. In addition to answering all case
study questions, put yourself in these situational cases and what ideas would you
have to make any operational processes or process flows better where associated in
the decision-making process?

Discussion Expectations

Please make sure to proof read your post prior to submission. They should be well
written and free of grammatical or typographical errors.

Initial postings are due by Wednesday. You are encouraged to post your follow-up
comments to different students, and you can do more than two if you want and these

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9/1/2020 Graded Discussion: Chapter 1, 2, 3, and 4 Case Studies –…

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peer replies are due by Saturday. Postings should include scholarly detail with
research support where appropriate

Important Reminder: Assessment of discussion boards and other writing
assignments account for 75% of overall grading and below are how grades will be
assessed for this discussion board:

Assessment Criteria
Possible
Points

Points
Earned

Student included an initial posting

18.75

Student included at least two peer replies. 12.5
Content in student postings is of rich descriptive
details supported with research to support and move
discussion topics forward in a collaborative
environment.

18.75

Student posted an initial posting by the Wednesday
due date.

6.25

Student posted at least two peer replies by the
Saturday date.

6.25

Total Earned points 62.5
Comments:

Chapter 3:

Data Warehousing

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)

3-‹#›

Learning Objectives
(Continued…)
Understand the basic definitions and concepts of data warehouses
Learn different types of data warehousing architectures; their comparative advantages and disadvantages
Describe the processes used in developing and managing data warehouses
Explain data warehousing operations
…

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Learning Objectives
Explain the role of data warehouses in decision support
Explain data integration and the extraction, transformation, and load (ETL) processes
Describe real-time (a.k.a. right-time and/or active) data warehousing
Understand data warehouse administration and security issues

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Opening Vignette…
“Isle of Capri Casinos Is Winning with Enterprise Data Warehouse”
Company background
Problem description
Proposed solution
Results
Answer & discuss the case questions.

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Questions for the
Opening Vignette
Why is it important for Isle to have an EDW?
What were the business challenges or opportunities that Isle was facing?
What was the process Isle followed to realize EDW? Comment on the potential challenges Isle might have had going through the process of EDW development.
What were the benefits of implementing an EDW at Isle? Can you think of other potential benefits that were not listed in the case?
Why do you think large enterprises like Isle in the gaming industry can succeed without having a capable data warehouse/business intelligence infrastructure?

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Main Data Warehousing Topics
DW definition
Characteristics of DW
Data Marts
ODS, EDW, Metadata
DW Framework
DW Architecture & ETL Process
DW Development
DW Issues

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What is a Data Warehouse?
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format
“The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”

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A Historical Perspective to
Data Warehousing

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Characteristics of DWs
Subject oriented
Integrated
Time-variant (time series)
Nonvolatile
Summarized
Not normalized
Metadata
Web based, relational/multi-dimensional
Client/server, real-time/right-time/active…

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Data Mart
A departmental small-scale “DW” that stores only limited/relevant data
Dependent data mart
A subset that is created directly from a data warehouse
Independent data mart
A small data warehouse designed for a strategic business unit or a department

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Other DW Components
Operational data stores (ODS)
A type of database often used as an interim area for a data warehouse
Oper marts – an operational data mart.
Enterprise data warehouse (EDW)
A data warehouse for the enterprise.
Metadata: Data about data.
In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use

3-‹#›

Application Case 3.1
A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry
Questions for Discussion
What are the main challenges for TELCOs?
How can data warehousing and data analytics help TELCOs in overcoming their challenges?
Why do you think TELCOs are well suited to take full advantage of data analytics?

3-‹#›

A Generic DW Framework

3-‹#›

Application Case 3.2
Data Warehousing Helps MultiCare Save More Lives
Questions for Discussion
What do you think is the role of data warehousing in healthcare systems?
How did MultiCare use data warehousing to improve health outcomes?

3-‹#›

DW Architecture
Three-tier architecture
Data acquisition software (back-end)
The data warehouse that contains the data & software
Client (front-end) software that allows users to access and analyze data from the warehouse
Two-tier architecture
First two tiers in three-tier architecture is combined into one
… sometimes there is only one tier?

3-‹#›

DW Architectures

3-tier
architecture
2-tier
architecture
1-tier
Architecture
?

3-‹#›

Data Warehousing Architectures
Issues to consider when deciding which architecture to use:
Which database management system (DBMS) should be used?
Will parallel processing and/or partitioning be used?
Will data migration tools be used to load the data warehouse?
What tools will be used to support data retrieval and analysis?

3-‹#›

A Web-Based DW Architecture

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Alternative DW Architectures

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Alternative DW Architectures
Each architecture has advantages and disadvantages!
Which architecture is the best?

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Ten factors that potentially affect the architecture selection decision
Information interdependence between organizational units
Upper management’s information needs
Urgency of need for a data warehouse
Nature of end-user tasks
Constraints on resources
Strategic view of the data warehouse prior to implementation
Compatibility with existing systems
Perceived ability of the in-house IT staff
Technical issues
Social/political factors

3-‹#›

Teradata Corp. DW Architecture

3-‹#›

Data Integration and the Extraction, Transformation, and Load Process
ETL = Extract Transform Load
Data integration
Integration that comprises three major processes: data access, data federation, and change capture.
Enterprise application integration (EAI)
A technology that provides a vehicle for pushing data from source systems into a data warehouse
Enterprise information integration (EII)
An evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, Web services, etc.

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Data Integration and the Extraction, Transformation, and Load Process

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ETL (Extract, Transform, Load)
Issues affecting the purchase of an ETL tool
Data transformation tools are expensive
Data transformation tools may have a long learning curve
Important criteria in selecting an ETL tool
Ability to read from and write to an unlimited number of data sources/architectures
Automatic capturing and delivery of metadata
A history of conforming to open standards
An easy-to-use interface for the developer and the functional user

3-‹#›

Data Warehouse Development
Data warehouse development approaches
Inmon Model: EDW approach (top-down)
Kimball Model: Data mart approach (bottom-up)
Which model is best?
Table 3.3 provides a comparative analysis between EDW and Data Mart approach
One alternative is the hosted warehouse

3-‹#›

Application Case 3.5
Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing
Questions for Discussion
How big and complex are the business operations of Starwood Hotels & Resorts?
How did Starwood Hotels & Resorts use data warehousing for better profitability?
What were the challenges, the proposed solution, and the obtained results?

3-‹#›

Additional DW Considerations Hosted Data Warehouses
Benefits:
Requires minimal investment in infrastructure
Frees up capacity on in-house systems
Frees up cash flow
Makes powerful solutions affordable
Enables solutions that provide for growth
Offers better quality equipment and software
Provides faster connections
… more in the book

3-‹#›

Representation of Data in DW
Dimensional Modeling
A retrieval-based system that supports high-volume query access
Star schema
The most commonly used and the simplest style of dimensional modeling
Contain a fact table surrounded by and connected to several dimension tables
Snowflakes schema
An extension of star schema where the diagram resembles a snowflake in shape

3-‹#›

The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)
Multidimensional presentation
Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry
Measures: money, sales volume, head count, inventory profit, actual versus forecast
Time: daily, weekly, monthly, quarterly, or yearly
Multidimensionality

3-‹#›

Star versus Snowflake Schema

3-‹#›

Analysis of Data in DW
OLTP vs. OLAP…
OLTP (online transaction processing)
Capturing and storing data from ERP, CRM, POS, …
The main focus is on efficiency of routine tasks
OLAP (Online analytical processing)
Converting data into information for decision support
Data cubes, drill-down / rollup, slice & dice, …
Requesting ad hoc reports
Conducting statistical and other analyses
Developing multimedia-based applications
…more in the book

3-‹#›

OLAP vs. OLTP

3-‹#›

OLAP Operations
Slice – a subset of a multidimensional array
Dice – a slice on more than two dimensions
Drill Down/Up – navigating among levels of data ranging from the most summarized (up) to the most detailed (down)
Roll Up – computing all of the data relationships for one or more dimensions
Pivot – used to change the dimensional orientation of a report or an ad hoc query-page display

3-‹#›

OLAP
Slicing Operations on a Simple Tree-Dimensional
Data Cube

3-‹#›

Variations of OLAP
Multidimensional OLAP (MOLAP)
OLAP implemented via a specialized multidimensional database (or data store) that summarizes transactions into multidimensional views ahead of time
Relational OLAP (ROLAP)
The implementation of an OLAP database on top of an existing relational database
Database OLAP and Web OLAP (DOLAP and WOLAP); Desktop OLAP,…

3-‹#›

Technology Insights 3.2
Hands-On DW with MicroStrategy
A wealth of teaching and learning resources can be found at TUN portal

www.teradatauniversitynetwork.com
The available resource includes scripted demonstrations, assignments, white papers, etc…

3-‹#›

DW Implementation Issues
Identification of data sources and governance
Data quality planning, data model design
ETL tool selection
Establishment of service-level agreements
Data transport, data conversion
Reconciliation process
End-user support
Political issues
… more in the book

3-‹#›

Successful DW Implementation
Things to Avoid
Starting with the wrong sponsorship chain
Setting expectations that you cannot meet
Engaging in politically naive behavior
Loading the data warehouse with information just because it is available
Believing that data warehousing database design is the same as transactional database design
Choosing a data warehouse manager who is technology oriented rather than user oriented
… more in the book

3-‹#›

Failure Factors in DW Projects
Lack of executive sponsorship
Unclear business objectives
Cultural issues being ignored
Change management
Unrealistic expectations
Inappropriate architecture
Low data quality / missing information
Loading data just because it is available

3-‹#›

Massive DW and Scalability
Scalability
The main issues pertaining to scalability:
The amount of data in the warehouse
How quickly the warehouse is expected to grow
The number of concurrent users
The complexity of user queries
Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouse

3-‹#›

Real-Time/Active DW/BI
Enabling real-time data updates for real-time analysis and real-time decision making is growing rapidly
Push vs. Pull (of data)
Concerns about real-time BI
Not all data should be updated continuously
Mismatch of reports generated minutes apart
May be cost prohibitive
May also be infeasible

3-‹#›

Enterprise Decision Evolution and Data Warehousing

3-‹#›

Real-Time/Active DW at Teradata

3-‹#›

Traditional versus Active DW

3-‹#›

DW Administration and Security
Data warehouse administrator (DWA)
DWA should…
have the knowledge of high-performance software, hardware and networking technologies
possess solid business knowledge and insight
be familiar with the decision-making processes so as to suitably design/maintain the data warehouse structure
possess excellent communications skills
Security and privacy is a pressing issue in DW
Safeguarding the most valuable assets
Government regulations (HIPAA, etc.)
Must be explicitly planned and executed

3-‹#›

The Future of DW
Sourcing…
Web, social media, and Big Data
Open source software
SaaS (software as a service)
Cloud computing
Infrastructure…
Columnar
Real-time DW
Data warehouse appliances
Data management practices/technologies
In-database & In-memory processing New DBMS
Advanced analytics
…

3-‹#›

Free of Charge DW Portal
for Teaching & Learning
www.TeradataStudentNetwork.com
Password to signup:

3-‹#›

End of the Chapter

Questions, comments

3-‹#›

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

3-‹#›

1970s1980s1990s2000s2010s
üMainframe computers
üSimple data entry
üRoutine reporting
üPrimitive database structures
üTeradata incorporated
üMini/personal computers (PCs)
üBusiness applications for PCs
üDistributer DBMS
üRelational DBMS
üTeradata ships commercial DBs
üBusiness Data Warehousecoined
üCentralized data storage
üData warehousing was born
üInmon, Building the Data Warehouse
üKimball, The Data Warehouse Toolkit
üEDW architecture design
üExponentially growing data Web data
üConsolidation of DW/BI industry
üData warehouse appliances emerged
üBusiness intelligence popularized
üData mining and predictive modeling
üOpen source software
üSaaS, PaaS, Cloud Computing
üBig Data analytics
üSocial media analytics
üText and Web Analytics
üHadoop, MapReduce, NoSQL
üIn-memory, in-database
Data
Sources
ERP
Legacy
POS
Other
OLTP/wEB
External
data
Select
Transform
Extract
Integrate
Load
ETL
Process
Enterprise
Data warehouse
Metadata
Replication
A

M
i
d
d
l
e
w
a
r
e
Data/text
mining
Custom built
applications
OLAP,
Dashboard,
Web
Routine
Business
Reporting
Applications
(Visualization)
Data mart
(Engineering)
Data mart
(Marketing)
Data mart
(Finance)
Data mart
(…)
Access
No data marts option
Tier 2:
Application server
Tier 1:
Client workstation
Tier 3:
Database server
Tier 1:
Client workstation
Tier 2:
Application & database server
Web
Server
Client
(Web browser)
Application
Server
Data
warehouse
Web pages
Internet/
Intranet/
Extranet
Source
Systems
Staging
Area
Independent data marts
(atomic/summarized data)
End user
access and
applications
ETL
Source
Systems
Staging
Area
End user
access and
applications
ETL
Dimensionalized data marts
linked by conformed dimensions
(atomic/summarized data)
Source
Systems
Staging
Area
End user
access and
applications
ETL
Normalized relational
warehouse (atomic data)
Dependent data marts
(summarized/some atomic data)
(a) Independent Data Marts Architecture
(b) Data Mart Bus Architecture with Linked Dimensional Datamarts
(c) Hub and Spoke Architecture (Corporate Information Factory)
Source
Systems
Staging
Area
Normalized relational
warehouse (atomic/some
summarized data)
End user
access and
applications
End user
access and
applications
Logical/physical integration of
common data elements
Existing data warehouses
Data marts and legacy systems
ETL
Data mapping / metadata
(d) Centralized Data Warehouse Architecture
(e) Federated Architecture
Packaged
application
Legacy
system
Other internal
applications
Transient
data source
ExtractTransformCleanseLoad
Data
warehouse
Data mart
Fact Table
SALES
UnitsSold
…
Dimension
TIME
Quarter
…
Dimension
PEOPLE
Division
…
Dimension
PRODUCT
Brand
…
Dimension
GEOGRAPHY
Country
…
Fact Table
SALES
UnitsSold
…
Dimension
DATE
Date
…
Dimension
PEOPLE
Division
…
Dimension
PRODUCT
LineItem
…
Dimension
STORE
LocID
…
Dimension
BRAND
Brand
…
Dimension
CATEGORY
Category
…
Dimension
LOCATION
State
…
Dimension
MONTH
M_Name
…
Dimension
QUARTER
Q_Name
…
Star SchemaSnowflake Schema
Product
T
i
m
e
G
e
o
g
r
a
p
h
y
Sales volumes of
a specific Product
on variable Time
and Region
Sales volumes of
a specific Region
on variable Time
and Products
Sales volumes of
a specific Time on
variable Region
and Products
Cells are filled
with numbers
representing
sales volumes
A 3-dimensional
OLAP cube with
slicing
operations

Chapter 4:

Business Reporting,

Visual Analytics, and Business

Performance Management

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)

4-‹#›

Learning Objectives
Define business reporting and understand its historical evolution
Recognize the need for and the power of business reporting
Understand the importance of data/information visualization
Learn different types of visualization techniques
Appreciate the value that visual analytics brings to BI/BA
…
(Continued…)

4-‹#›

Learning Objectives
Know the capabilities and limitations of dashboards
Understand the nature of business performance management (BPM)
Learn the closed-loop BPM methodology
Describe the basic elements of balanced scorecards

4-‹#›

Opening Vignette…
Self-Service Reporting Environment Saves Millions For Corporate Customers
Background
Business Challenge
Solution
Results
Answer & discuss the case questions.

4-‹#›

Questions for the
Opening Vignette
What does Travel and Transport, Inc., do?
Describe the complexity and the competitive nature of the business environment in which Travel and Transport, Inc., functions.
What were the main business challenges?
What was the solution? Implementation?
Why do you think a multi-vendor, multi-tool solution was implemented?
List and comment on three main benefits of the implemented system.

4-‹#›

Business Reporting
Definitions and Concepts
Report = Information  Decision
Report?
Any communication artifact prepared to convey specific information
A report can fulfill many functions
To ensure proper departmental functioning
To provide information
To provide the results of an analysis
To persuade others to act
To create an organizational memory…

4-‹#›

What is a Business Report?
A written document that contains information regarding business matters.
Purpose: to improve managerial decisions
Source: data from inside and outside the organization (via the use of ETL)
Format: text + tables + graphs/charts
Distribution: in-print, email, portal/intranet
Data acquisition  Information generation  Decision making  Process management

4-‹#›

Business Reporting

4-‹#›

Key to Any Successful Report
Clarity …
Brevity …
Completeness …
Correctness …
Report types (in terms of content and format)
Informal – a single letter or a memo
Formal – 10-100 pages; cover + summary + text
Short report – periodic, informative, investigative

4-‹#›

Application Case 4.1
Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting
Questions for Discussion
How did Delta Lloyd Group improve accuracy and efficiency in financial reporting?
What were the challenges, the proposed solution, and the obtained results?
Why is it important for Delta Lloyd Group to comply with industry regulations?

4-‹#›

Types of Business Reports
Metric Management Reports
Help manage business performance through metrics (SLAs for externals; KPIs for internals)
Can be used as part of Six Sigma and/or TQM
Dashboard-Type Reports
Graphical presentation of several performance indicators in a single page using dials/gauges
Balanced Scorecard-Type Reports
Include financial, customer, business process, and learning & growth indicators

4-‹#›

Components of
Business Reporting Systems
Common characteristics
OLTP (online transaction processing)
ERP, POS, SCM, RFID, Sensors, Web, …
Data supply (volume, variety, velocity, …)
ETL
Data storage
Business logic
Publication medium
Assurance

4-‹#›

Application Case 4.2
Flood of Paper Ends at FEMA
Questions for Discussion
What is FEMA and what does it do?
What are the main challenges that FEMA faces in delivering its services?
How did FEMA improve its inefficient reporting practices?

4-‹#›

Data and Information Visualization
“The use of visual representations to explore, make sense of, and communicate data.”

Data visualization vs. Information visualization
Information = aggregation, summarization, and contextualization of data
Related to information graphics, scientific visualization, and statistical graphics
Often includes charts, graphs, illustrations, …

4-‹#›

Application Case 4.3
Tableau Saves Blastrac
Thousands of Dollars with
Simplified Information Sharing
Questions for Discussion
How did Blastrac achieve significant cost saving in reporting and information sharing?
What were the challenge, the proposed solution, and the obtained results?

4-‹#›

A Brief History of
Data Visualization
Data visualization can date back to the second century AD
Most developments have occurred in the last two and a half centuries
Until recently it was not recognized as a discipline
Today’s most popular visual forms date back a few centuries

4-‹#›

The First Pie Chart
Created by William Playfair in 1801
William Playfair is widely credited as the inventor of the modern chart, having created the first line and pie charts.

4-‹#›

Decimation of Napoleon’s Army During the 1812 Russian Campaign
Arguably the most popular multi-dimensional chart
By Charles Joseph Minard

4-‹#›

A Brief History of Data Visualization
1900s –
more formal attitude toward visualization
focus on color, value scales, and labeling
Publication of the book Semiologie Graphique
2000s –
Emergence of Internet as the medium for information visualization  raising visual literacy
Incorporate interaction, animation, 3D graphics-rendering, virtual worlds, real-time data feed
2010s and beyond – ?

4-‹#›

Application Case 4.4
TIBCO Spotfire Provides Dana-Farber Cancer Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials
Questions for Discussion
How did Dana-Farber Cancer Institute use TIBCO Spotfire to enhance information reporting and visualization?
What were the challenges, the proposed solution, and the obtained results?

4-‹#›

Different Types of
Charts and Graphs
Which one to use? Where and when?
Specialized Charts and Graphs
Histogram
Gantt Chart
PERT Chart
Geographic Map
Bullet Graph
Heat Map / Tree Map
Highlight Table
Basic Charts and Graphs
Line Chart
Bar Chart
Pie Chart
Scatter Plot
Bubble Chart

4-‹#›

A Gapminder Chart
Wealth and Health of Nations
See gapminder.org for
interesting animated examples

4-‹#›

The Emergence of Data Visualization And Visual Analytics
Magic Quadrant for Business Intelligence and Analytics Platforms (Source: Gartner.com)
Many data visualization companies are in the 4th quadrant
There is a move toward visualization

4-‹#›

The Emergence of Data Visualization And Visual Analytics
Emergence of new companies
Tableau, Spotfire, QlikView, …
Increased focus by the big players
MicroStrategy improved Visual Insight
SAP launched Visual Intelligence
SAS launched Visual Analytics
Microsoft bolstered PowerPivot with Power View
IBM launched Cognos Insight
Oracle acquired Endeca

4-‹#›

Visual Analytics
A recently coined term
Information visualization + predictive analytics
Information visualization
Descriptive, backward focused
“what happened” “what is happening”
Predictive analytics
Predictive, future focused
“what will happen” “why will it happen”
There is a strong move toward visual analytics

4-‹#›

Visual Analytics by SAS Institute
SAS Visual Analytics Architecture
Big data + In memory + Massively parallel processing + ..

4-‹#›

Visual Analytics by SAS Institute
At teradatauniversitynetwork.com, you can learn more about SAS VA, experiment with the tool

4-‹#›

Performance Dashboards
Performance dashboards are commonly used in BPM software suites and BI platforms
Dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that information can be digested at a single glance and easily drilled in and further explored

4-‹#›

Performance Dashboards

4-‹#›

Performance Dashboards
Dashboard design
The fundamental challenge of dashboard design is to display all the required information on a single screen, clearly and without distraction, in a manner that can be assimilated quickly
Three layer of information
Monitoring
Analysis
Management

4-‹#›

Application Case 4.6
Saudi Telecom Company Excels with Information Visualization
Questions for Discussion
Why do you think telecommunication companies are among the prime users of information visualization tools?
How did Saudi Telecom use information visualization?
What were their challenges, the proposed solution, and the obtained results?

4-‹#›

Application Case 4.6

4-‹#›

Performance Dashboards
What to look for in a dashboard
Use of visual components to highlight data and exceptions that require action.
Transparent to the user, meaning that they require minimal training and are extremely easy to use
Combine data from a variety of systems into a single, summarized, unified view of the business
Enable drill-down or drill-through to underlying data sources or reports
Present a dynamic, real-world view with timely data
Require little coding to implement/deploy/maintain

4-‹#›

Best Practices in
Dashboard Design
Benchmark KPIs with Industry Standards
Wrap the Metrics with Contextual Metadata
Validate the Design by a Usability Specialist
Prioritize and Rank Alerts and Exceptions
Enrich Dashboard with Business-User Comments
Present Information in Three Different Levels
Pick the Right Visual Constructs
Provide for Guided Analytics

4-‹#›

Business Performance Management (BPM)
Business Performance Management (BPM) is…
A real-time system that alerts managers to potential opportunities, impending problems and threats, and then empowers them to react through models and collaboration.
Also called corporate performance management (CPM by Gartner Group), enterprise performance management (EPM by Oracle), strategic enterprise management (SEM by SAP)

4-‹#›

Business Performance Management (BPM)
BPM refers to the business processes, methodologies, metrics, and technologies used by enterprises to measure, monitor, and manage business performance.
BPM encompasses three key components
A set of integrated, closed-loop management and analytic processes, supported by technology …
Tools for businesses to define strategic goals and then measure/manage performance against them
Methods and tools for monitoring key performance indicators (KPIs), linked to organizational strategy

4-‹#›

A Closed-Loop Process to Optimize Business Performance
Process Steps
Strategize
Plan
Monitor/analyze
Act/adjust
Each with its own process steps

4-‹#›

Strategize:
Where Do We Want to Go?
Strategic planning
Common tasks for the strategic planning process:
Conduct a current situation analysis
Determine the planning horizon
Conduct an environment scan
Identify critical success factors
Complete a gap analysis
Create a strategic vision
Develop a business strategy
Identify strategic objectives and goals

4-‹#›

Plan:
How Do We Get There?
Operational planning
Operational plan: plan that translates an organization’s strategic objectives and goals into a set of well-defined tactics and initiatives, resources requirements, and expected results for some future time period (usually a year).
Operational planning can be
Tactic-centric (operationally focused)
Budget-centric plan (financially focused)

4-‹#›

Monitor/Analyze:
How Are We Doing?
A comprehensive framework for monitoring performance should address two key issues:
What to monitor?
Critical success factors
Strategic goals and targets
…
How to monitor?
…

4-‹#›

Success (or mere survival) depends on new projects: creating new products, entering new markets, acquiring new customers (or businesses), or streamlining some process.
Many new projects and ventures fail!
What is the chance of failure?
60% of Hollywood movies fail
70% of large IT projects fail, …
Act and Adjust: What Do We Need to Do Differently?

4-‹#›

Application Case 4.7
IBM Cognos Express Helps Mace for Faster and Better Business Reporting
Questions for Discussion
What was the reporting challenge Mace was facing? Do you think this is an unusual challenge specific to Mace?
What was the approach for a potential solution?
What were the results obtained in the short term, and what were the future plans?

4-‹#›

Performance measurement system
A system that assists managers in tracking the implementations of business strategy by comparing actual results against strategic goals and objectives
Comprises systematic comparative methods that indicate progress (or lack thereof) against goals
Performance Measurement

4-‹#›

Key performance indicator (KPI)
A KPI represents a strategic objective and metrics that measure performance against a goal
Distinguishing features of KPIs
KPIs and Operational Metrics
Strategy
Targets
Ranges
Encodings
Time frames
Benchmarks

4-‹#›

Key performance indicator (KBI)
Outcome KPIs vs. Driver KPIs
(lagging indicators (leading indicators
e.g., revenues) e.g., sales leads)
Operational areas covered by driver KPIs
Customer performance
Service performance
Sales operations
Sales plan/forecast
Performance Measurement

4-‹#›

Balanced Scorecard (BSC)
A performance measurement and management methodology that helps translate an organization’s financial, customer, internal process, and learning and growth objectives and targets into a set of actionable initiatives
“The Balanced Scorecard: Measures That Drive Performance” (HBR, 1992)
Performance Measurement System

4-‹#›

Balanced Scorecard
The meaning of “balance” ?

4-‹#›

Six Sigma
A performance management methodology aimed at reducing the number of defects in a business process to as close to zero defects per million opportunities (DPMO) as possible
Six Sigma as a Performance Measurement System

4-‹#›

The DMAIC performance model
A closed-loop business improvement model that encompasses the steps of defining, measuring, analyzing, improving, and controlling a process
Lean Six Sigma
Lean manufacturing / lean production
Lean production versus six sigma?
Six Sigma as a Performance Measurement System

4-‹#›

Comparison of Balanced Scorecard and Six Sigma

4-‹#›

Application Case 4.8
Expedia.com’s Customer Satisfaction Scorecard
Questions for Discussion
Who are the customers for Expedia.com? Why is customer satisfaction a very important part of their business?
How did Expedia.com improve customer satisfaction with scorecards?
What were the challenges, the proposed solution, and the obtained results?

4-‹#›

End of the Chapter

Questions, comments

4-‹#›

Data
Repositories
Business Functions
UOB 1.0X
UOB 2.2
UOB 2.1XUOB 3.0
1
Machine
Failure
SymbolCountDescription
Exception Event
Transactional Records
PHASE 5
DEPT 4
DEPT 3
DEPT 2
DEPT 1
PHASE 4PHASE 3PHASE 2PHASE 1
DEPLOYMENT CHART
1
2
3
4
5
Information
(reporting)
Decision
Maker
Action
(decision)
Data

ITS 531: Business Intelligence
Week 2: Video Lecture

Lecture Objectives
During this lecture, we will:
Go over concepts of data warehouses, business reporting, visual analytics, and performance management.
Go over and review discussion expectations for week 2.

ITS 531: Data Warehouses

Data Warehouses (Part 1)
In the realm of business intelligence, it is always good to understand the fundamental concepts of data warehouses. For example:
One characteristic of a data warehouse is that these systems are subject oriented where data is organized by detailed subject, such as sales, products, or customers, containing only information relevant for decision support.
Another characteristic of a data warehouse is that these systems are integrated. For example, data warehouses must place data from dissimilar sources into a reliable format. Furthermore, data warehouses must deal issues associated with naming conflicts and discrepancies among units of measure in essence to be totally integrated.
Data warehouses are also based on time variances. For example, the data in a data warehouse does not necessarily provide a current status unless the technology driven system is real-time. Data warehouses also detect trends, deviations, and long-term relationships for forecasting and comparisons, leading to decision making which is the very foundation of the most important benefit of business intelligence. Even more, every data warehouse has a time-based quality which is a dimension where data for analysis from multiple sources contains multiple time points.
Another characteristic of a data warehouse is that these technology driven information systems are nonvolatile meaning that after data is entered, users of the system cannot change or update the data.
An important benefit of today’s data warehouses is that many are based on web-based technology which creates efficient computing environment.
The design of data warehouses is based on either the relational or the multidimensional model and structure. In addition, the architecture of these technology driven data warehouses have focus on client/server uses which promote efficiency and good productivity for end users.
In addition, data warehouses of today’s technology advancements support real time activities for data access and data analysis.
Data warehouses of today technology era also includes a data form known as meta data which is data about data. In other words, a user would understand how the data is organized and how to best use the data for data analysis to support the decision making process.

Data Warehouses (Part 2 – Processing)
To continue with our foundations of data warehousing, it is good to understand some of the processes used by data warehouses. For example:
Data warehouses must deal with data sources where data is sourced from multiple independent and operational systems and some of these systems could be from external data providers. Data may also come from online transaction processing (OLTP) and Enterprise Resource Planning (ERP) systems.
When data is extracted from a data warehouse for the intentions of transformation into usable business intelligence, the transformation process happens with the support ETL software which is based on three database functions including extraction, transformation, and lading.
Another important processing feature of a data warehouse is data loading. In this process, data is loaded into a staging area, where the data is converted and cleansed. After this process is complete, the data would be ready to load into the data warehouse and in some cases a data mart.
Once a data warehouse is in is comprehensive state, this would be the processing capability as a EDW or an enterprise data warehouse where the technology driven information system, in this case, supports all decision analysis by providing relevant summarized and detailed information originating from many different sources.
In addition, a data warehouse will support processing from many software programs to support the process of metadata for organizing data summaries which are easy to index and search, especially with Web tools.
As with any major database architecture, the internal structure of these systems will be protected and middleware tools will be used to allow or enable access. There are many front-end applications that business users can use to interact with data stored in the data repositories, including data mining, OLAP, reporting tools, and data visualization tools.

Data Warehouses (Part 3– The Future)
To continue with our foundations of data warehousing, it is good to see what the future holds for data warehousing. For example below are areas of research everyone may find interesting to explore:
Data Warehouse sourcing using the following technology:
Web, social media, and Big Data
Open source software
SaaS (software as a service)
Cloud computing
Data Warehouse infrastructures base on:
Columnar (Based on storage and access)
Real-time data warehousing
Data warehouse appliances
Data management technologies and practices
In-database processing technology
In-memory storage technology
New database management systems
Advanced analytics

ITS 531: Data Warehouses (SQL Server)

Data Warehouses (SQL Server Demonstration)
For anyone that plans to focus in a career of business intelligence and data analytics may find it very beneficial to explore the SQL language. Below are free learning resources found over the internet:
SQL Server Courses, (2018). Microsoft’s Virtual Academy. Retrieved from https://mva.microsoft.com/product-training/sql-server#!lang=1033
SQL Tutorial, (1999 – 2018). W3schools.com. Retrieved from https://www.w3schools.com/sql/
SQL Tutorial, (2018). Tutorials Point: Simply Easy Learning. Retrieved from https://www.tutorialspoint.com/sql/
SQL Tutorial, (2004 – 2015). Art Branch Inc. Retrieved from http://www.sql-tutorial.net/
SQL Tutorial, (2018). Geeks for Geeks. Retrieved from https://www.geeksforgeeks.org/sql-tutorial/
SQL Tutorial (2018). Mode. Retrieved from https://community.modeanalytics.com/sql/tutorial/introduction-to-sql/
For video demonstrations, everyone might be amazed at what can be located at www.youtube.com using the following keyword searches.
SQL and SQL Server Tutorials
SQL and MySQL Tutorials
SQL and Oracle Tutorials
SQL and Microsoft Access Tutorials

ITS 531: Business Reporting

Business Reporting (Part 1 – Report Types)
In the realm of business intelligence, there are several ways businesses and organization use reports and below are some major uses. For example:
One type of report used in these cases is a metric management report. For example, an organization that use this type of report will accomplish business performance through outcome-oriented metrics. For external groups, these are service-level agreements (SLAs). For internal management, they are key performance indicators (KPIs).
Important Note: All students should conduct further research to see uses and application of service-level agreements (SLAs) and key performance indicators (KPIs) as used by any organization or business.
Another type of report an organization will use is reports in the dashboard format. These types of reports will offer a wide assortment of diverse performance indicators on one page, like a dashboard in a vehicle. Characteristically, developed dashboards will have predefined reports with static components and stationary structures. At the same time, customizations are always possible within a dashboard depending on available technology driven programs.
When looking at the customization and design of a dashboard, below are some key areas to consider:
Benchmark key performance indicators with industry standards
Wrap the dashboard metrics with contextual metadata
Validate the dashboard design by a usability specialist
Prioritize and rank alerts/exceptions streamed to the dashboard
Enrich the dashboard with business users’ comments
Present information in different levels for best support
Pick the right visual construction using dashboard design principles
Provide a medium for guided analytics
Furthermore, another type of report that organizations and businesses will use are balanced score cards. When an organization or business uses balanced score cards, they are looking and applying an integrated view of success in the organization. The scorecard measurements can show all sorts of information based on customers, business processes, and future growth potentials to name a few.

Business Reporting (Part 2 – Best Practices)
As we continue with a foundation of business reporting, it is good to understand and explain the importance of Reports to an organization. Who typically reads the reports, and what use is made of reports? Who is responsible for delivering the content of reports?
When it comes to report design, there could always be some variance toward best practices depending on personal and professional preferences and even expectations when reports are used in an official work environment. Regardless, good reports will typically follow some or all of the following not limited to:
Reports when prepared, by a data scientist or data analyst, should always have scale and balance between textual content and any illustrations.
Reports should offer enhanced formatting in efforts to draw attention to key areas of the report. This could be as simple as using bold formatting for report headings.
Reports especially when associated with data analysis and data analytics should be enhanced with visualizations. These visualizations could be the use of charts.
In association more with charts, these are very important visualizations and can help any entity in an organization or business make better decisions. Common types of charts used in these cases include and is not limited to;
Histograms
Gantt charts
Column and Bar charts
PERT charts
Pie charts
Geographic maps
Bullets
Heat maps
Highlight tables
Tree maps and Decision Trees
Any may more
Regardless of overall presentation and format, reports are very important to any organization as data within will help managers, leadership, or any other entity that reviews the report to make better decisions.

ITS 531: Visual Analytics and Performance Management

Visual Analytics and Performance Management
In the realm of business intelligence, the support of data analytics is cortical in the decision making process and to support this process, we have visual analytics. For example,
The development of visual analytics associates with the growth of analytics in a fundamental and universal way.
Vendors that develop business intelligence and data analytic applications are seeing their customers require quick and preferably interactive visualizations, not just for their normal reporting systems, but also to illustrate predictive and prescriptive decision-making information.
The use of predictive and prescriptive decision-making methods are becoming more and more cortical because big data is everywhere and most organizations and businesses, with the capabilities, are using big data to be competitive based on offered services and products where applicable to customers.
In the realm of business intelligence, performance is really all that matters because competition is very high among organizations that may offer similar services and products. When it comes to general performance, below are performance measures an organization and or business should consider, not limited to:
Measurements that focus on key factors.
Measurements that mix and or compare past, present, and future data analytics.
Measurements that support needs of shareholders, employees, partners, suppliers, and other stakeholders but in harmony and balance.
Measurements showing top/down approaches
Measurements showing targets based on research and reality with the avoidance of arbitrary data or research.


Visual Analytics and Performance Management
Going even futher when thinking about the very nature of reports and visual analytics, always remember that:
A report is a communication object equipped with the precise purpose of relaying information in a presentable form and if the report concerns business or organizational matters, then it is called a business report.
Business and organizational reporting is a vital part of the business intelligence process toward improving managerial decision making. In other words, we use technology to help us prepare reports but it is what we do with the reports to support the decision making process.
Furthermore, these reports are more visually adapted to, often using colors and graphical icons that cooperatively look like a dashboard to enhance the information content. In these visual oriented reports, charts are one of the most common types of visual elements.
Business reporting with the aid of various technologies will enable business intelligence and analytics as a decision support tool and when done analytically, these reports are collected with integrated sets of processes, methodologies, metrics, and applications intended to drive the overall business and operative performance of a business or organization.
In other words, well designed reports which have raw data from a database transformed into usable business intelligence can help and business or organization translate strategies and objectives into plans, monitor performance against those plans, analyze variations between actual results and planned results, and adjust their objectives and actions in response to this analysis.

ITS 531: Week 2 Discussion – Case Studies

ITS 531: Week 2 Discussion (Cast Study – Tips) — 
For the week 2 discussion, we will be looking at and discussing the application case studies of chapters 1, 2, 3, and 4 and for the first case of chapter 1, think about the following questions:
Why would an organization need an enterprise-wide data warehouse?
In this case, things to potentially consider would be:
Duplication in the process gathering data
Data processing methods that are not consistent or uniform
Data redundancies
Operating cost to manage data is high
What could be the business value of integrated data?
In this case, integrated data could potentially do the following:
Improve marketing
Improve communications
Improve customer satisfaction correlating to increase sales
Improve data reporting correlating to better decision making
What forms of analytics are used?
In this case, analytics to discuss could potentially include:
Descriptive analytics
Predictive analytics
And prescriptive analytics
What other data applications could be developed in this case?
In this case, thinking about a business environment, other application tools could include:
Pricing of products
Regulatory compliance
Hiring and payroll
Risk management
Facilities management

ITS 531: Week 2 Discussion (Cast Study – Tips) — 
For the 2nd case of chapter 2, think about the following questions:
Why would an organization want to solve logistic problems with storage?
In this case, and when dealing with logistic problems with storage, always remember that space not typically used is wasted space and reducing wasted space issues will greatly improve spending and operational efficiency.
What could be some benefits of using optimization or other mathematical based models?
In this case, always remember that all mathematical based models including the optimization model can be used in so many ways to help organizations boost efficiency along with more flexibility of operations. In addition, these models can help organizations achieve better quality and safety and a better process of securing resources.
It should also be noted that forecasting models can help organizations reduce errors.
Could Empty Container Logistics Optimization (ECO) help other organizations having problems with empty storage?
When researching ECO in this case, think about the industry as a whole and that there could always be variance in every work condition and environment. At the same time, strategies, heuristics, and intelligent systems are always being tested and suggested to tackle these complex problems.
Could Empty Container Logistics Optimization (ECO) be applied to other domains?
In this case, always remember that several industries will have complex logistic issues well beyond storage and shipping and with limited resources, in many of these cases, the ECO idea could easily be applied to help industries with scheduling, inventory management, routing, and work flow and many other domains.

ITS 531: Week 2 Discussion (Cast Study – Tips) — 
For the third case of chapter 3, think about the following questions:
What are the benefits of implementing the Continental Go Forward strategy.?
In this case, things to potentially consider would be:
Restoring profitability
Better industry metrics
Reducing any occurrences of fraud
The role of a data warehouse in support of this case
What be some benefits of a real-time data warehouse?
In this case, things to potentially consider would be:
Improved decision making relying on data that is current and not old
Improved communications
Improved operations
Improved customer support
What are the major differences between the traditional data warehouse and a real-time data warehouse?
In this case, things to potentially consider would be:
A traditional data warehouse moves data from operational databases to a data warehouse on a scheduled basis, typically daily or weekly. This is a consistent process; however, data is never current and could be as old as week and in some cases longer.
A real time data warehouse works the same as the traditional with the exception that data is moved from and updated from databases on a more current time cycle. For example and instead of days or weeks, data is updated to the data warehouse, in most cases, on an hourly basis making data analysis to support decision making more reliable.
What are advantages of real-time system?
In this case, things to potentially consider would be :
Decisions are made based on current and up to date information.
Decisions become more strategic in nature and can be distributed across all levels with information that is more reliable being current.

ITS 531: Week 2 Discussion (Cast Study – Tips) — 
For the 4th case of chapter 4, think about the following questions:
What is Premier?
In this case, think about and do research on various health care services which can be used to help hospitals improve cost controls and quality of services.
What are challenges in this case?
In this case, discuss based on experience and or research issues when technology driven information systems do not offer scalability to handle various transactions.
Also in this case, think about ideas for technology and needed infrastructures to handle large or massive volumes of data.
What could be some solutions?
In this case, think about a new platform to be designed to deliver trusted information in the right context at the right time to users based on their roles.
In addition, this platform will need to be able to use a portal to access integrated systems for various clinical, business, and compliance-related applications in order to help the healthcare organizations provide their staff with accurate and timely information to support evidence-based, patient-centric, and accountable care.
Are there any other benefits from integrated systems?
In this case, always remember that potential benefits for saving lives, helping people enjoy healthier lives, and reducing healthcare costs are enormous and any technology which is integrated to do this is always a benefit.

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