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Anand Vaneswaran
August 12, 2007
Summer Independent Study Business Intelligence
Final Report
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Table of Contents
Business Intelligence Report
1.0 Problem Statement 32.0 Purpose of this Report 3
3.0 Acronyms 34.0 Industry Applications of Business Intelligence (BI)
4.1 Background/Introduction 4
4.2 Measuring BI Benefits 54.3 BI & Data Warehousing (DW) Success Stories 7
4.4 BI Governance Model 84.5 BI The Operational Model 84.6 Current State of BI Solutions 9
5.0 Project Design5.1 The Company 125.2 Mission Statement 125.3 Company Operations 125.4 Process Diagram 13
5.5 Company Performance Tracking & Monitoring 135.6 Dashboard 145.7 E-R Diagram 18
5.8 Design Process 195.9 Design Implementation 195.10 Design Output 20
6.0 Conclusion 207.0 References 218.0 Acknowledgements 21
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Business Intelligence Report
1.0 Problem Statement
The scope of this project is to build a dummy corporation using real-world data from
organizations and explore the use of a tool that extracts data and converts it into useful
information for better management decision-making. The tool used will be NetCharts Designer 5.1.
2.0 Purpose of this Report
The purpose of this report is to describe the process and the approach used to provide
Business Intelligence for a dummy corporation using one of the available tools in the market.
3.0 Acronyms
IT Information Technology
BI Business Intelligence
DW Data Warehousing
IDC International Data Corporation
ROI Return-on-investment
TDWI The Data Warehousing Institute
OLAP Online Analytical Processing Technology
ODBC Open Database Connectivity
SQL Structured Query Language
DSN Database Source Name
HTML Hypertext Markup Language
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4.0 Industry Applications of Business Intelligence (BI)
4.1 Background/ Introduction
In the context of todays Information Age as opposed to the Industrialization age of
the past, heavy emphasis and reliance by most Corporations for data/information and the ability of
the systems to synthesize the same to enable better and quick decision-making is seen as a distinct
competitive edge in the marketplace.1 The fallout of this paradigm has resulted in the critical need
for establishing a strong nexus between the Business Processes in a company and the
corresponding Information Technology needs to provide the needed Business Intelligence that
would facilitate Corporations to arrive at informed decisions through Data Analysis and Synthesis.
In such a setting, Business Intelligence refers to various tools, in the form of applications
and technologies, used to gather, convert, and interpret data regarding the nature of the business,
such as metrics on sales, production, and internal operations. In this day and age, there is a need
for decision makers to receive accurate, actionable, and standardized information in a timely,
proactive, and automated manner. Organizations that make use of these applications and
technologies and implement them in their Business will gain an automatic competitive advantage
over those that have to manually build comprehensive financial and operational reports. When
things are done manually, it often results in a huge disparity in information availability and
accuracy.
Organizations in the US and across the globe have invariably moved towards the concept
of information democracy in todays global marketplace. This simply means that all users should
have access to the insight that they need to carry out their respective roles, and when information
is uniform and consistent across the enterprise. While companies look for ways to contain the costs
and complexity of managing data, the real goal is to leverage the information to make better
business decisions, to be more agile, and to gain insight into business performance. A recent
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survey of respondents constituting mostly IT and Business Professionals indicated that improving
data accuracy and integrity and gaining quick access to information is most important for their
organizations. Among other compelling findings in surveys, the need to find reliable business
intelligence (BI) strategies is becoming increasingly important. Currently, most organizations are
not taking full advantage of existing BI solutions. Benchmarks conducted on data warehouses (DW)
in organizations, small and large, across the United States revealed that companies are successful
in their BI & DW initiatives. While quick access to data and providing a repository of decision
support data scored high, completeness of data and improved communication scored low.
4.2 Measuring Business Intelligence benefits
To make the case for value, the International Data Corporation (IDC), a market research
and analysis firm, has found that organizations who successfully integrate Business Intelligence into
a Business Process can achieve a significant return on investment. IDCs Financial Impact of
Business Analytics study interviewed over 40 companies in a wide variety of industries in North
America and Europe. The study found that a Business Intelligence implementation generates a
median five-year return on investment (ROI) of 112% with a mean payback of 1.6 years on
average costs of $4.5 million. Of the organizations included in this study, 54% had an ROI of 101%
or more. The largest class of benefit was due to business process enhancement, where BI was
applied to operational decision in areas such as logistics, call centers, fraud detection, and
marketing campaign management. 2
Despite its promise, BI is not exactly delivering the ROI that C-level executives have
expected. The cause for most BI problems is not so much about the technology but actually about
the significant effects that BI has on the organizations and its people. Therefore, effective change
management as part of a BI program means putting in place a governance system to proactively
facilitate data integration for every change to the business architecture.
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One reason why organizations would be resistant toward change is because of the fact that
they never seem to be able to figure out the quality and relevance of data the starting point in a
BI initiative. One of the best ways to understand more about the current quality of data is to look
at how much the data is being used and how many users are actually using the data. Organizations
are now moving towards a pyramid that uses eight parameters as levels of data sophistication.
Data type & domain: looking at specific data type and attribute value domains to ensure
conformance. Non-printable characters, time zone ambiguities are a few examples.
Completeness:both logical and physical completeness of data.
Uniqueness & referential integrity:uniqueness applies within an entity. Referential integrity applies
to uniqueness in foreign-key/primary-key relations between entities.
Consistency:while referential integrity addresses entity relationships, consistency is concerned with
content overlaps and inconsistencies of data.
Freshness and timeliness:freshness addresses the currency of the content of the data. Timeliness
addresses when data becomes available to users.
Business rules conformance: it deals with whether data is used and transformed consistently with
its intent, definition, and semantics.3
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4.3 BI and DW Success Stories4
The table below covers a snapshot of Success Stories in the BI space from some Major
Corporations:
Business
Opportunities/ Benefits
Information needed Actions
WALMART: optimized
merchandising, promotion and
pricing, reduced stock by
20%, improved turnover by
2% to 5%
Items, costs, customer
behavior, inventory levels
Analysis of each cost and each
item for all 3500 suppliers,
quick response for inventory
levels and pricing
SONY THEATRES: improved
film scheduling/booking,
enhanced promotion
Detailed daily box office
information (number of tickets
sold, etc)
Forecasts of attendance and
potential sales and margins
HEWLETT PACKARD:
improved after sales service
Data on customers and after
sales services
Customer surveys (Satisfaction
ratings)
BANK OF AMERICA:
improved customer service,
65% increase in loan requests
Quick access to data on 36
million customers (profitability,
risk, needs)
Launch of a new loan by
phone product, development
of a comprehensive knowledge
of each customers needs
MTV NETWORKS:
programming changes in line
with customer services
Internal and external
(panels) data
Better reporting, monitoring
and decision support for
programming and ad planning
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4.4 Business Intelligence Governance Model
The fundamental components of governance are the mission statement, where the
organization or business unit provides and communicates overall purpose of the BI and DW
initiative; working definitions, which ensures common understanding between all parties involved;
and guiding principles, which provides specific direction to the project team. There will also be put
in place a Steering Committee, which will determine the mission and guiding principles, determine
the scope of each DW, establish quality standards, and sanction the governing data models.
The Data Warehousing Institute (TDWI) found in a recent survey that successful BI
initiatives are almost five times more likely to have project teams in which IT is very aligned with
the business. Organizations should ask some fundamental but pertinent questions when leading BI
initiatives:
Who are my customers?
Who are my profitable customers?
Who, what, and where am I on target to meet sales goals?
How do we manage our customer equity?
Which possibilities for additional business do we have with our current customers?
How should we handle our relationships with customers?
BI and DW will lead to development of additional and profitable sales, increased efficiency of sales
and marketing, improved customer service, decision support and monitoring, and cost reduction. 4
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4.5 Business Intelligence The Operational Model
The Typical Steps in institutionalizing and operationalizing a BI Program in a company would
tend to revolve around the following:
- Mission of the Program Statement of Purpose
- Strategic Objectives of the Program derived from the Mission Statement
- Strategic Measures / Critical Success Factors of the Program in line with the Strategic
Objectives
- Understanding Business Processes resulting in a Requirements Document
- Design/Code/integration testing resulting in a IT Platform for use by the Business
There are two major success constructs that have emerged from a recent survey of all the
important players in organizations BI and DW programs: product measures and development
measures.
Product Measures
Information quality:DW provides accurate, complete, and consistent information
System quality:DW is flexible, scalable, and able to integrate data
Individual impacts:quick and easy access to data; improves their decision-making
Organizational impacts: BI and DW should meet business requirements; support the
accomplished of strategic business objectives; enable improvements in business processes;
lead to high, quantifiable return on investment (ROI).
Development Measures
Development cost:cost of developing and maintaining DW
Development time:the time taken to develop DW5
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4.6 Current state of Business Intellige nce solutions
Business Intelligence Tools
Business Intelligence tools are interactive computer-based structures and subsystems
intended to help decision-makers use communication technologies, data, documents, knowledge,
and analytical models to identify and solve problems. During the 1990s, many organizations
around the US engaged in data warehousing projects. These activities included bringing together
the organizations legacy systems to developing user interface tools for analysis and reporting. The
data warehouse is the underlying structure from which reports and documents for analysis are
generated. Tools typically are in the form of dashboards and scorecards. Dashboards give
information seekers insight into day-to-day progress and performance data on a real-time and
integrated basis. The whole purpose of a dashboard is to integrate all the necessary data and
visualize it to the decision-makers. It is ideal because information is conveyed quickly and executive
managers think of it as eye candy. A Dashboard type user interface design allows presentation
of complex relationships and performance metrics in a format that is easily understandable and
digestible by time pressured managers. More specifically, such interface designs significantly
shorten the learning curve and thus increase the likelihood of effective utilization. A scorecard is a
custom user interface that helps optimize an organizations performance by linking inputs and
outputs both internally and externally through the use of metrics and graphs. To be effective, a
scorecard must link into the organizations vision. Typically, tools can be categorized as data-
driven and model-driven. Model-driven systems tend to utilize analytical constructs such as
forecasting, optimization algorithms, simulations, decision trees, and rules engines. Data-driven
systems deal with data warehouses, databases, and online analytical processing (OLAP)
technology.6
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One of the best ways to measure how well organizations utilize information/data
management solutions and from which vendors these products and services were obtained is
through the use of surveys.
As illustrated in the figure below, only 20% of respondents deploy BI tools widely
throughout the enterprise, followed by 32% whose deployment is limited to specific problems, and
another 30% who report scattered deployment. Nearly 15% roll out BI tools widely only after
initial deployment. These results indicate that only very few organizations take full advantage of BI
solutions for their business needs.
7
Business Intelligence Systems
The survey also indicated that 68% of the respondents are using Microsoft Products for
their business intelligence and data management needs. 61% also use Oracle Products, while
about one-third use IBM.
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5.0 Project Design
5.1 The Company
SmartBiz, Inc. is a Telecommunications company based in Munich, Germany with employee
strength of roughly 5,000 and headquartered in Munich with field offices spread all across the
world. This company caters to the Telecom industry and primarily supplies products for the internet
market with stiff competition from other competitors, notably Allied, Inc and Tech Com. SmartBiz
is listed in the New York Stock Exchange (NYSE) as a publicly-held company and for the year
ending 2006 reported a net revenue of $1 million.
5.2 Mission Statement
To transform and enrich peoples lives through outstanding communications network and
growth of customers.
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5.3 Company Operations
Fundamentally operating on a customer-centric model, the following are some key
Business Processes:
1. Order administration and handling
2. Offer and contract management
3. Product lifecycle management
4. Manufacturing and volume production
5. Repair and Logistics
The process view model depicting the flow of the above processes is shown below.
5.4 Process Diagram
OrderReceipt
OrderEntity
OrderValidation
Customer
Inputs
Customer
Outputs
FactoryManufacturing*
R & D Design
Transportation- Logistics*
Installation
Customer Satisfaction*
ContractClosure
Delivery*
RepairedProduct
DefectiveProduct*
* - Points of
Measurement
Suppliers
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5.5 Company Performance- Tracking and M onitoring
The Organization tracks and monitors its performance around the processes listed above
through a scorecard approach refereed to as the Balanced Scorecard. The Executive Management
chaired by the Chief Executive Officer and his direct reports review the performance on a monthly
basis and work on corrective actions for underperforming measures.
5.6 Dashboard
The most recent Business Scorecard performance for the month of June 07 is given below.
Repair Performance
NodeNumber
Repair/turnaround time(# daysactual) Target Status
1 10 15
2 15 15
3 13 15
4 22 15
5 11 156 16 15
7 9 15
8 14 15
9 13 15
10 12 15
Order Fulfillment Lead Time Performance
Item # Lead Time Actual Status
1 10 152 10 17
3 10 18
4 10 9
5 10 12
6 10 8
7 10 13
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8 10 16
9 10 10
10 10 7
Delivery Quality Performance
Month # of items shipped # of defective items # allowed Status
1 40 4 4
2 40 8 4
3 40 9 4
4 40 2 4
5 40 0 4
6 40 6 4
7 40 12 4
8 40 6 4
9 40 5 410 40 3 4
11 40 7 4
12 40 10 4
Customer Critical Issue (CCI) Performance
Item Number CCI - # of customer critical issuesper month Target Status
1 24 20
2 35 20
3 44 20
4 42 20
5 55 20
6 17 20
7 16 20
8 11 20
9 9 20
10 18 20
Financial Revenue Performance
Quarter Revenue (in millions) Target Status
1 20 20
2 22 20
3 19 20
4 16 20
5 17 20
6 18 20
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7 26 20
8 24 20
Gross Margin Outlook Performance
Quarter Gross Margin (%) Target (%) Status
1 12 15
2 14 15
3 16 15
4 16 15
5 17 15
6 11 15
7 15 15
8 13 15
Product Cost Reduction for a single product - Performance
Month Unit Price Target ($) Status
1 35 40
2 38 40
3 45 40
4 40 40
5 42 40
6 50 40
7 36 40
8 35 409 30 40
10 45 40
11 55 40
12 40 40
First Pass Yield Performance
Item Number FPY (Factory) Target (%) Status
1 82 90
2 88 903 87 90
4 95 90
5 93 90
6 100 90
7 91 90
8 80 90
9 75 90
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10 90 90
Supplier Audit Corrective Action Closure Performance
Month # of findings Target Status
1 22 10
2 15 10
3 13 10
4 10 10
5 17 10
6 14 10
7 10 10
8 8 10
9 9 10
10 12 10
11 16 10
12 14 10
Customer Satisfaction Performance
Quarter Customer Loyalty Index(CLI) Target CLI Status
1 8 8.5
2 7.5 8.5
3 7.7 8.5
4 9.2 8.5
5 8.6 8.5
6 8.5 8.5
7 7.8 8.5
8 6.9 8.5
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5.7 E-R Diagram
Repair Performance
PK Serial Number
Location Performance
Turnaround time
Target
Status
FK1 Month
Delivery Performance
PK Item number
Lead Time
Actual
Status
FK1 Month
Delivery Quality Performance
PK,FK1 Month
Number of items shipped
Number of defective items
Number allowed
Status
Customer Satisfaction Performance
PK,FK3 Month
Customer Loyalty Index (CLI)
Target
FK1 Serial Number
FK2,FK4 Item numberStatus
FK5 Quarter
Customer Critical Issue Performance
PK Item Number
CCI
Target
Status
FK1 Month
Financial Revenue Performance
PK Quarter
Revenue
TargetStatus
Gross Margin Outlook Performance
PK Quarter
Gross Margin
Target
Status
FK1 Month
Product Cost Reduction Performance
PK,FK1 Month
Unit Price
Target
Status
First Pass Yield Performance
PK Item Number
FPY
Target
Status
FK1 Month
Supplier Audit Corrective Action Closure Performance
PK,FK1 Month
Number of Findings
Target
Status
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5.10 Design Output
The dashboard can be viewed at:
http://68.38.10.229:8001/projects/SmartBiz/SmartBizPage1.jsp
6.0 Conclusion
Business Intelligence is one of the leading investment priorities for companies today. This
reflects greater recognition of the need to reduce complexity caused by multiple sources of
information and to improve decision-making processes in the enterprise. Yet companies face
implementation challenges, notably data integration, scalability, availability, and security issues.
Finally, providing innovative business practices that address cost of implementation issues, such as
fixed cost implementation, are creative ways to reduce customer risk and gain increased customer
mindshare as a true partner for BI implementations.
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The approach described in this project demonstrates a simple but effective methodology of
translating key company measures into an operational dashboard for decision-making by SmartBiz,
Inc. With variations to this approach, it is easy to establish design of similar dashboards to provide
business intelligence to the executives and operational managers in any organization.
7.0 References
1. Hoisington, Steven H. Vaneswaran, SA. Implementing Strategic Change: Tools for
Transforming an Organization, McGraw-Hill, 2005.
2. Eastwood, Matthew. Vesset, Dan. Morris, Henry D. HP: Delivering Value in Business
Intelligence, White Paper, February 2005.
3. Knightsbridge Solutions LLC. A Practical Approach to Data Quality: Proven Formulas for
Delivering Measurable Results, White Paper, 2005.
4. Chicago Business Intelligence Group (CBIG). Using Business Intelligence and Data
Warehousing Methods, Tools and Processes to Drive Business Results, White Paper, 2005.
5. Ariyachandra, Thilini. Watson, Hugh. Benchmarks for BI and Data Warehousing Success.
http://www.dmreview.com/article_sub.cfm?articleID=1044330. January 2006.
6. Hall, Owen P, Jr. Using Dashboard-based Business Intelligence Systems: An approach to
improving business performance. http://gbr.pepperdine.edu/034/bis.html. 2003.
7. TechRepublic. Information Management Trends in 2005. CNET Networks. 2005.
8.0 Acknowledgements
I wish to place on record my deep appreciation for all the guidance and advice given by my
professor, Bryan D. French, throughout the tenure of this project. His insightful comments now and
then helped me to be aware of certain aspects of this project that would not have been possible
otherwise. I also wish to thank some of my friends for their help and inputs.