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Buisiness Intelligence Datawarehouse Research

Please refer to attached assignment document for actual question.

Attached are the Chapter 3 and Chapter 4 PPT Please refer to it while doing this assignment.

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

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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?

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A Generic DW Framework

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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?

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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?

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DW Architectures

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

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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?

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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

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Teradata Corp. DW Architecture

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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

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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

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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?

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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

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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

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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

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Star versus Snowflake Schema

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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

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OLAP vs. OLTP

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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

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OLAP
Slicing Operations on a Simple Tree-Dimensional
Data Cube

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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,…

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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…

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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

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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

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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

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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

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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

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Enterprise Decision Evolution and Data Warehousing

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Real-Time/Active DW at Teradata

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Traditional versus Active DW

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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

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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
…

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

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End of the Chapter

Questions, comments

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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.

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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)

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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…)

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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

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

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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.

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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…

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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

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Business Reporting

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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

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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?

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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

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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

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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?

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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, …

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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?

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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

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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.

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

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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 – ?

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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?

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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

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

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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

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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

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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

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

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

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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

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Performance Dashboards

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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

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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?

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Application Case 4.6

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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

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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

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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)

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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

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

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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

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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)

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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?
…

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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?

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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?

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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

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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

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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

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.

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.

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 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!!!!!!!!!!!!

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