Course Name: Business Intelligence
Year
: 2009
Value of Business Intelligence
1st Meeting
References
Main Books
•
(1).
Williams, Steve & Williams, Nancy (2007). The Profit Impact of Business Intelligence.
Morgan Kaufmann. San Francisco. ISBN 978-0-12-372499-1.
•
(2).
Loshin, David (2003). Business Intelligence: The Savvy Manager’s Guide. Morgan
Kaufmann. San Francisco. ISBN 978-1-55860-916-7.
Supporting Books
•
(1).
Moss, Larrisa T. & Atre, Shaku (2003). Business Intelligence Roadmap: The Complete
Project Lifecycle for Decision-Support Applications. Addison Wesley. Boston. ISBN 0-20178420-3.
•
(2).
Vercellis, Carlo (2009). Business Intelligence : Data Mining and Optimization for
Decision Making. John Wiley and Sons. United Kingdom. ISBN: 978-0-470-51138-1.
•
(3).
Turban, Efraim, Sharda, Ramesh, Aronson, Jay E., & King, David (2008). Business
Intelligence: A Managerial Approach. Person Education. New Jersey. ISBN 978-0-13-2345569.
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Source of this Material
(1).
Williams, Steve & Williams, Nancy (2007). The Profit
Impact of Business Intelligence. Chapter 1
(2). Loshin, David (2003). Business Intelligence:
The Savvy Manager’s Guide. Chapter 1 & 2
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Introduction to Business Intelligence
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Why Business Intelligence?
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Increased Profitability
Business intelligence can help business clients to evaluate customer lifetime value
and short-term profitability expectations and to use this knowledge to distinguish
between profitable and non profitable customers.
•
Decreased Cost
Decreasing of the investments required to make sales, BI can be used to help
evaluate organizational costs.
•
Improved Customer Relationship Management (CRM)
This is basically a BI application that applies the analysis of aggregated customer
information to provide improved customer service responsiveness.
•
Decreased Risk
Applying BI methods to credit data can improve credit risk analysis, whereas
analyzing both supplier and consumer activity and reliability can provide insight into
how to streamline the supply chain.
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What is Business Intelligence?
The Data warehousing institute defines business intelligence as:
“The process, technologies, and tools needed to turn data into information,
information into knowledge, and knowledge into plans that drive profitable
business action. Business Intelligence encompasses data warehousing,
business analytic tools, and content/knowledge management.”
In addition, this definition also exposes two critical notions:
• A BI practice is more that just a collection of tools. This means that without
the processes and the right people, the tools are of little value.
• The value of BI is realized in the context of profitable business action. This
means that if knowledge that can be used for profitable action is ignored,
the practice is of little value.
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What is Business Intelligence? (cont..)
•
Turning Data into Information
The process of turning data into information can be summarized as the process of
determining what data is to be collected and managed and in what context.
•
Turning Information into Knowledge
Accumulate piles of information, which are then analyzed in many different ways until
some critical bits of knowledge are created. BI involves the analytical components,
such as data warehousing, online analytical processing (OLAP), data quality, data
profiling, business rule analysis, and data mining.
•
Turning Knowledge into Actionable Plans
A BI program provides benefits that increase business efficiency, increase sales,
provide better customer targeting, reduce customer service costs, identify fraud, and
generally increase profits while reducing costs. Because of this, implemented
properly, BI is one IT area that can be a profit center instead of the traditional cost
center.
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What is Business Intelligence? (cont..)
•
•
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BI combines products, technology,
and methods to organize key
information that management
needs to improve profit and
performance.
BI as business information and
business analyses within the
context of key business processes
that lead to decisions and actions
and that result in improved
business performance.
BI means leveraging information
assets within key business
processes to achieve improved
business performance.
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Figure 1-1
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Business Intelligence in Action
•
•
•
•
BI Investment are wasted unless they are connected to specific business
goals, analyses, decisions, and actions that result in improved performance.
BI from business perspective is primarily about profit.
BI needs to be highly specific to the industry and to how the company
competes in that industry.
The kinds of business information, business analyses, and business
decisions that BI must deliver or enable, and the way that BI creates
business value must be specifically determined for each company. That’s
the only way to get the best possible return on your BI investment.
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The Information Asset
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Significant amount of money has been invested in attempts at building and
launching BI frameworks and applications (most on infrastructure).
Very little has been invested in managing and exploiting a valuable
corporate asset  a company’s data
There are a number of organization that have started to view their data as a
corporate asset and to realize that properly collecting, aggregating, and
analyzing their data.
BI as a set of tools and methodologies designed to exploit actionable
knowledge discovered from the company’s information asset.
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Exploiting Information
Figure 1-2
Level of Abstraction
Knowledge
&
Intelligence
Information
Data
Size of Data
A Pyramid of Abstraction
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The Origins of BI
•
Decision Support Systems (DSSs)
Since the 1970s and 1980s, business have used business information and structured
business analysis to tackle complex business decisions. DSSs range from
sophisticated, customized analytical tools running on mainframe computers to
spreadsheet-based products running on personal computers.
•
Executive Information Systems (EISs)
These were an early attempt to deliver the business information and business
analyses to support management planning and control activities. EIS applications
have been replaced and extended by BI applications such as scorecards,
dashboards, performance management, and other “analytical applications.” These
application combine business information and business analyses to provide custom
built and/or packaged BI solutions.
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Business Intelligence Today
• BI is bringing a powerful new tool to businesses.
• With an effectively executed BI program, businesses can compete
by being better that competition at leveraging information to improve
profits and performance.
• Business need to rethink how they use information in general and BI
particular.
• A key recent innovation is the use of business-centric BI methods,
these methods are designed to help companies fully leverage the
profit potential of BI.
• The availability and affordability of business- centric methods for
designing and developing BI means that a cohesive BI systems that
drives profits and performance.
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Value of Business Intelligence
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Using BI to Capture Business Value
Figure 1-3
•
•
•
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In economic term, the business value of
an investment (an asset) is the net
present value of the after-tax cash flows
associated with the investment.
BI can be seen as a matter of
determining how an organization can
use BI to:
 Improve management processes
 Improve operational processes
Figure 1-3, capturing the business value
of BI requires organizations to go well
beyond the technical implementation of
a BI environment.
Figure 1-4, The implication of this
requirement is that BI methodologies
must be extended to include these
additional preconditions.
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Figure 1-4
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How do We Achieve Strategic Alignment?
1.
2.
3.
4.
Understand your organization’s strategic drivers and goals.
Determine the business question you need BI to answer in order to
achieve those goals.
Identify tools, methods, and analytical frameworks to inform decisions and
measure performance.
Deliver the information your organization needs to take actions that
improve performance and support your goals.
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The Need of Process Engineering
Figure 1-5
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•
Process engineering focuses on providing answer to the questions posed in figure 1-5,
and those answer can be captured as the foundation for business rules, standard
processes, and standard analytical applications for responding to productivity variances.
This approach can be used for all planned BI applications and will allow organizations to
move from ad hoc responses for recurring business conditions to effective repeatable
responses that capture the business value of BI.
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The Need for Change Management
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Process engineering provides a map of which processes must change and
how they must change in order to create business value with BI
applications.
Change management is a generic discipline with principles that are
generally understood and have been widely applied for decades to a variety
of organizational change processes, including business process changes
induced by IT investment in enterprise applications such as ERP.
One of the primary change management challenges for BI applications is
that most organizations use information and analytical frameworks within
management processes has, until recently, been very limited.
To capture the business value of BI initiatives aimed at management
processes, organizations will have to apply scientific management and
process control thinking to “white collar” activities, a substantial change.
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The Information Assets and Data Valuation
Data can be viewed as an asset, because data can be used to provide benefits
to the company, is controlled by organization, and is the result of previous
transactions.
The Value of Information and The Value of BI:
• Time Value of Data
• Information as a Sharable Resource
• Increasing Value through Increased Use
• Increasing Value through Quality
• Increasing Value through Merging
• Value versus Volume
• Measuring the Value of Information
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BI Application and Dashboard
The different uses of data and the contexts of each use as it pertains to the
exploitation of information. It’s can break those into two areas. The first area is
operational data use, and the other is strategic use.
The strategic uses of information as manifested through BI analytics:
• Customer Analytics
Customer Relationship Management (CRM) is actually based on a number of
customer analytic functions that together help people in a company better understand
who their customers are and how to maximize the value of each customer.
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Customer profiling: the continuous refinement of individual customer profiles that
incorporate demographic, psychographic, and behavioral data about each individual.
Targeted Marketing: knowledge of a set of customer likes and dislikes can augment a
marketing campaign to target small clusters that share profiles.
Personalization: web site personalization exploits customer profiles to dynamically collect
content designed for an individual, and it is meant to enhance that customer’s experience.
Collaborative Filtering: this kind of recommendation generation creates relatively reliable
cross-sell and up-sell opportunities.
Customer Satisfaction: this can improve these representatives’ ability to deal with the
customer and expedite problem resolution.
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BI Application and Dashboard (cont)
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
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Customer Lifetime Value: the lifetime value of a customer is a measure of a customer’s
profitability over the lifetime of the relationship.
Customer Loyalty: that company’s best new customers are its current customers.
Human Capital Productivity Analytics
One way to attain value internally from BI is to be able to streamline and optimize
people within the organization, including:
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Call Center Utilization and Optimization: high level to high-value customers, minimal
support to low-value customers.
Production Effectiveness: includes evaluating on-time performance, labor cost, production
yield, etc.
Business Productivity Analytics
Another popular analytic realm involves business productivity metrics and analysis,
including:
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
Defect Analysis: companies struggle to improve quality production, there may be specific
factors that affect the number of defective item produced.
Capacity Planning and Optimization: understanding resource utilization for all aspects of a
physical plant.
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BI Application and Dashboard (cont)
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Financial Reporting: financial reporting analytics provide the means for high-level
executives to take the pulse of the company and drill down on particular areas.
Risk Management: to make better decisions about how and when to allocate resources in a
way that minimize risk to the organization.
Just-in-Time: can help in accurately delivering products built to customer order within a
predictable amount of time.
Asset Management and Resource Planning: to provide insight into short and long-term
resource planning.
Sales Channel Analytics
Sales channel analytics a subset of business productivity analytics, yet there is
enough value is segmenting this area of application.

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Marketing: to fine-tune a marketing program and the ability to determine marketing
effectiveness.
Sales Performance and Pipeline: to identify variables that affect the efficiency of the sales
cycle.
Supply Chain Analytics
Aspects of supply chain analytics involve the following:
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BI Application and Dashboard (cont)
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Supplier and Vendor Management: to track performance and reliability by supplier,
evaluating and rating the quality of the product supplied.
Shipping: there are different methods by which a company delivers its products to its
customers, each with its own cost schedule.
Inventory Control: maintaining an inventory of commodity products that exhibit volatile
pricing and limited useful life creates a market risk if those products cannot be used before
their obsolescence.
Distribution Analysis: the optimal distribution model would arrange for the delivery of the
exact number of products from each factory to its closets warehouse.
Behavior Analysis
This type of analytical processing makes use of historical data to look for behavior
patterns that take place before the significant event. This allow for the following kinds
of analytics:
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Purchasing Trends: indentify purchasing patterns that indicate a growing trend that can be
used to adjust a company’s reaction to customer trends.
Web Activity: the content presentation can be crafted to direct the web site visitor into these
success patterns.
Fraud and Abuse Detection: fraudulent behavior frequently is manifested in patterns.
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BI Application and Dashboard (cont)


Customer Attrition: can be done by evaluating patterns of behavior before previous
attritions and then using those patterns for ongoing customer behavior analysis.
Social Networking Analysis: it is important to identify relationships between specific entities
within a system and to analyze their behavior as a group.
The Intelligence Dashboard
A key performance indicator (KPI) is some objective measurement of an aspect of a
business that is critical to the success of that business. In fact a large number of KPIs
can be defined in terms of measuring performance associated with many of the BI
analytics functions that we described earlier. Another conceptual value of BI is the ability
to capture the business definitions of the key performance indicators, manage those
definitions as part of the corporate knowledge base, and then provide a visualization
dashboard that reflects those KPI measurements, presented in a form for management
review.
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“The social responsibility of business is to increase its profits.”
-Milton Friedman, Nobel laureate economist
End of Slide
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