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Chapter 6
THE ESSENTIALS
OF BUSINESS
INTELLIGENCE
Origins and Drivers
of Business Intelligence
• Organizations are being compelled to capture,
understand, and harness their data to support decision
making in order to improve business operations
• Business cycle times are now extremely compressed;
faster, more informed, and better decision making is
therefore a competitive imperative
• Managers need the right information at the right time
and in the right place
• Table shows the business value of BI analytical
applications
Origins and Drivers
of Business Intelligence
Analytic Application Business Questions
Business Value
Customer Segmentation
What market segments do my
Personalize customer
customers fall into, and what are relationships for higher
their characteristics?
satisfaction and retention.
Propensity to buy
Which customers are most like
to respond to my promotion?
Target customers based on their
need to increase their loyalty to
your product line.
Fraud detection
How can I tell which
transactions are likely to be
fraudulent?
Quickly determine fraud and take
immediate action to minimize cost.
Customer attrition
Which customers is at risk of
leaving?
Prevent loss of high-value
customers and let go of lowervalue customers.
Customer Profitability
What is the lifetime profitability
of my customer?
Make individual business
interaction decisions based on the
overall profitability of customers.
General Process of
Intelligence Creation and Use
• To be successful in today’s business environment,
enterprises must:
– Assess their readiness for meeting the challenges posed by
these new business realities
– Take a holistic approach to BI functionality
– Leverage best practices and anticipate hidden costs
• Key questions as a framework for BI analysis:
– How can enterprises maximize their BI investments?
– What BI functionality do enterprises need, and what are they
using today?
– What are some of the hidden costs associated with BI initiatives?
General Process of
Intelligence Creation and Use
• 全称萨班斯·奥克斯利法案（Sarbanes－Oxley Act），起源于美国安然
公司倒闭后引起的美国股市剧烈动荡，投资人纷纷抽逃资金。为防止和
保证上市公司财务丑闻不再发生，由美国参议员Sarbanes和美国众议员
Oxley联合提出了一项法案，该法案即以他们的名字命名。
• BI implemented by one firm runs a performance
management System.
– Perform flawless analysis and compilation of thousands of transaction and
journal entries.
– Balance more access to data with the need to control access to sensitive
insider information.
– Deliver reports to the SEC in less time.
– Get “more eyes on the data” and key performance indicators and build in
strict security controls.
– Provide live reports that allow people to drill down to the lowest level of
transaction detail.
– And so on.
General Process of
Intelligence Creation and Use
General Process of
Intelligence Creation and Use
• Intelligence creation and use and BI governance
– BI governance: Project prioritization process
• When a BI/DW is in place, the general process of intelligence creation starts
by identifying and prioritizing specific BI project. For each potential BI
project in portfolio, it is important to use return on investment and total cost
ownership (TCO) measures estimate the cost-benefit ratio.
• Benefit analysis involves the end-user examinations of decision-making
impact.
• Who serve as the decision maker involve the Project Prioritization process.
– Issues for the BI governance team is to address the following:
•
•
•
•
•
Creating categories of projects
Defining criteria for project selection
Determining and setting a framework for managing project risk
Managing and leveraging project interdependencies
Continually monitoring and adjusting the composition of the portfolio
Major Characteristics
of Business Intelligence
• Business intelligence is NOT transaction processing
– Online analytical processing (OLAP)
An information system that enables the user, while at a PC, to query the
system, conduct an analysis, and so on. The result is generated in
seconds
– Online transaction processing (OLTP)
Handle a company’s ongoing business, which is are computerprocessing systems in which the computer responds
immediately to user requests. Each request is considered to
be a transaction, that is, a computer records a discrete event.
Most of operational data in enterprise resource planning (ERP)
systems, supply chain management or customer relationship
management system are stored in what is referred to as OLTP
systems.
Major Characteristics
of Business Intelligence
• Business intelligence is NOT transaction processing
– OLTP concentrates on processing repetitive transactions in
large quantities and conducting simple manipulations
– OLAP involves examining many data items complex
relationships
– OLAP may analyze relationships and look for patterns, trends,
and exceptions
– OLAP is a direct decision support method .
Major Characteristics
of Business Intelligence
• The information factory view
– Terms of data warehouse is associated with the concept of a
factory.
– Enterprise information factory as a way to describe how
companies conduct and organize BI efforts.
– A cornerstone component of that factory concept is the DW
– An information factory is connected to other internal information
system, such as ERP, CRM, and e-commerce, as well as the
external information, usually, via internet or extranet.
– An information factory is illustrated in figure.
Major Characteristics
of Business Intelligence
Major Characteristics
of Business Intelligence
An information factory has:
• Inputs
– Data sources
– Acquisition
• Storage
– DW
– Data marts
• Processing of inputs
– Analysis
– Data mining
• Outputs
– Data delivery
– BI applications
Major Characteristics
of Business Intelligence
• Data warehousing and business intelligence
– Data warehouse is a collection of data, designed to support
management decision making. DW contains a wide variety of data
that present a coherent picture of business conditions a at single
point in time.
– Create a database infrastructure that is always online and that
contains all the information from the OLTP systems, including
historical data, but reorganized and structured in such a way that
it is fast and efficient for querying, analysis, and decision support
• Teradata advanced analytics methodology
– Teradata created a methodology for BI that is illustrated in figure.
BI applications (upper-left side) are supported by advanced
analytics techniques and tools (left side). The right side as a
cyclical process that circles the enterprise DW.
Major Characteristics
of Business Intelligence
Toward Competitive
Intelligence and Advantage
• Strategic imperative because:
– Barriers to entry of a new competitor to an industry are being significantly
diminished
– An organization that has a strong position within its industry could easily
face new competitors because the costs and other constraints to
becoming a player in the market have decreased
– Due to globalization
• The ability to delivery goods throughout the world readily available
supply chain channel as such FedEx, UPS and DHL, as well as ecommerce.
Toward Competitive
Intelligence and Advantage
• Competitive intelligence (CI)
– Competitive intelligence often involves more than the
BI initiatives used in most organizations, there are
some overlaps.
– CI implies tracking what competitors are doing by
gathering sources of materials on their recent and inprocess activities.
– BI initiatives use some outside sources of data are
included in the analysis process, but they are often
available from third-party vendors.
Toward Competitive
Intelligence and Advantage
• Competitive strategy in an industry
– Competitor analysis is a component of industry analysis, which
serves as a basis for strategic planning processes
• Several generic strategies are commonly based on an
industrial analysis for strategic planning. For example, Porter
five forces model.
• For example, low cost strategy.
– BI applications in this context might include scrutinizing quality
metrics associated with specific production processes, analyzing
raw materials from various suppliers to assess defect rates,
tracking costs of goods sold as a percentage of run volume, and
so on.
• For example, clickstream analysis.
Toward Competitive
Intelligence and Advantage
• Competitive strategy in an industry
– Focus on a particular market niche, perhaps through some form of
product or service differentiation
– BI applications in this context might include:
• Making sure customer needs are met and loyalty is built
• Tracking and remembering customer preferences in the next
customer encounter
• For example, a gambler gets a same treatment if they are in any of
the geographical locations of the company’s business. The rewards
the gambler earned from visiting any locations accrue with the
company, creating a loyal and frequently returning customers, which
is the targeted, high-value market niche.
Toward Competitive
Intelligence and Advantage
• Sustaining competitive advantage
– As with other IT investment, using the BI or DW attempting to
achieve a competitive advantage is only one aspect.
– Another perspective is the sustainable advantage.
– Most strategic analysts agree that low-cost leadership may not
yield a sustainable advantage unless the low cost can be
sustained
– BI projects and DW are becoming increasingly important weapons
in sustaining competitive advantage across industries
Successful Business
Intelligence Implementation
• The fundamental reasons for investing in BI must be
aligned with the company’s business strategy. BI cannot
simply be a technical exercise for the IS department. It
must serve as a way to change the manner the
company conducts business by improving its business
processes and transforming decision-making processes
to be more data driven.
Successful Business
Intelligence Implementation
• A framework for planning is a necessary
precondition
– At the business and organizational levels, it is important to
define strategic and operational objectives while considering
the available organizational skills to achieve those objectives
– Upper managers must build enthusiasm for those initiatives
and procedures for the intra-organizational sharing of BI best
practices
– Plans to prepare the organization for change must be in place
– One of the first step in that process is to assess the IS
organization, the skill sets of the potential classes of users, and
whether the culture is amenable to change.
Successful Business
Intelligence Implementation
• If a company’s strategy is properly aligned with the reasons for a DW
and BI initiatives, if the company’s IS organization is or can be made
capable of playing its role in such a project, and if the requisite user
community is in place and has the proper motivation, it is wise to start BI
and establish a BI competency center (BICC) within the company
– Demonstrate how BI is clearly linked to strategy and execution of
strategy
– Serve to encourage interaction between the potential business user
communities and the IS organization.
– Serve as a repository and disseminator of best BI practices.
– Standards of excellence of BI practices.
– Organization can learn a great deal of knowledge about variety of the
analytical tools
– Better understand why the DW platform must be flexible enough.
– Help the stakeholders to see how BI can play an important role.
Successful Business
Intelligence Implementation
• Attaining real-time, on-demand BI
– The demand for instant, on-demand access to dispersed
information has grown as the need to close the gap between the
operational data and strategic objectives has become more
pressing
– New data-generating technologies, such as RFID, is accelerating
this growth and the subsequent need for real-time BI
– Traditional BI systems use a large volume of static data that have
been extracted, cleansed, and loaded into a DW to produce
reports and analyses.
– Users need business monitoring, performance analysis, and an
understanding of why things are happening.
– These can alert users, virtually in real-time, about changes in data
or the availability of relevant reports, and so on
Structure and Components
of Business Intelligence
Structure and Components
of Business Intelligence
• Data warehouse
– Data flows from operational systems (e.g., CRM,
ERP) to a DW, which is a special database or
repository of data that has been prepared to support
decision-making applications ranging from those for
simple reporting and querying to complex
optimization
• Business analytics
– Online analytical processing (OLAP)
Software tools that allow users to create on-demand
reports and queries and to conduct analysis of data
Structure and Components
of Business Intelligence
• Data mining
– Data mining is a class of database information
analysis that looks for hidden patterns in a group of
data that can be used to predict future behavior
– Used to replace or enhance human intelligence by
scanning through massive storehouses of data to
discover meaningful new correlations, patterns, and
trends, by using pattern recognition technologies
and advanced statistics
Structure and Components
of Business Intelligence
• Business performance management (BPM)
– Based on the balanced scorecard
methodology—a framework for defining,
implementing, and managing an enterprise’s
business strategy by linking objectives with
factual measures
– Dashboards
A visual presentation of critical data for
executives to view. It allows executives to see
hot spots in seconds and explore the situation
Business Intelligence
Today and Tomorrow
• Recent industry analyst reports show that in the coming years,
millions of people will use BI visual tools and analytics every day
• Today’s organizations are deriving more value from BI by extending
actionable information to many types of employees, maximizing the
use of existing data assets
• A potential trend involving BI is its possible merger with artificial
intelligence (AI)
• BI is spreading its wings to cover small, medium, and large
companies
• BI takes advantage of already developed and installed components
of IT technologies, helping companies leverage their current IT
investments and use valuable data stored in legacy and
transactional systems
Business Intelligence
Today and Tomorrow
• Recent industry analyst reports show that in the coming years,
millions of people will use BI visual tools and analytics every day
• Today’s organizations are deriving more value from BI by extending
actionable information to many types of employees, maximizing the
use of existing data assets
• A potential trend involving BI is its possible merger with artificial
intelligence (AI)
• BI is spreading its wings to cover small, medium, and large
companies
• BI takes advantage of already developed and installed components
of IT technologies, helping companies leverage their current IT
investments and use valuable data stored in legacy and
transactional systems
The Business
Analytics (BA) Field: An Overview
• Business intelligence (BI)
The use of analytical methods, either manually or automatically, to
derive relationships from data
• The essentials of BA
– Analytics
The science of analysis.
– Business analytics (BA)
The application of models directly to business data. BA involves
using MSS tools, especially models, in assisting decision makers;
essentially a form of OLAP decision support
BA is a broad category of applications and techniques for gathering,
storing, analyzing, and providing access to data to help enterprises
users make better business and strategic decisions.
– BA is also known as analytic processing. BI tools, BI applications.
The Business
Analytics (BA) Field: An Overview
• An analytic application is a step upward in sophistication
form merely providing analytic techniques or tools. It
allows for activities such as:
– Automating the thinking and, inmost cases, a portion of the
decision making of a human being.
– Typically using complex quantitative techniques, such as
multivariate regression analysis, data mining, artificial intelligence,
nonlinear programming.
• There are lots of the BA tools, we can divide into three
categories.
The Business
Analytics (BA) Field: An Overview
The Business
Analytics (BA) Field: An Overview
•
MicroStrategy’s classification of BA tools: The
five styles of BI
1.
2.
3.
4.
5.
Enterprise reporting
Cube analysis
Ad hoc querying and analysis
Statistical analysis and data mining
Report delivery and alerting
The Business
Analytics (BA) Field: An Overview
The Business
Analytics (BA) Field: An Overview
•
SAP’s classification of strategic
enterprise management
– Three levels of support
1. Operational. SAP R3 mainly supports the
transaction processing on the operational level.
2. Managerial. Middle management can access all
reports, arranged by functional areas. Managers
can make queries and drill down.
3. Strategic. The company offers products under
the title SAP SEM (strategy Enterprise
Management) which includes BA.
The Business
Analytics (BA) Field: An Overview
•
Executive information and support systems
–
–
–
Executive information systems (EIS)
Provides rapid access to timely and relevant information
aiding in monitoring an organization’s performance
Executive support systems (ESS)
Also provides analysis support, communications, office
automation, and intelligence support
Capabilities of EIS/ESS, see table in next slide.
The Business
Analytics (BA) Field: An Overview
Capability
Description
Drill-Down
Go to additional details at one or several levels. Done
through a series of menus or by direct queries.
Critical Success Factors
The factors most critical for the success of business.
Can be organizational, industrial, departmental, etc.
Key Performance Indicator The specific measure of each CSF
Status reports
The latest data available on KPI or some other metric,
ideally in real-time
Trend analysis
Short, medium, long-term trend of KPI or metrics.
Projected using forecasting methods.
Ad hoc analysis
Analysis made at any time and with any desired
factors and relationship
Exception reporting
Highlight the deviation larger than certain thresholds.
Slicing and Dicing
Rearranging data so that they can be viewed from
different perspectives.
Online Analytical Processing (OLAP)
•
•
Drill-down
The investigation of information in detail (e.g., finding not only total
sales but also sales by region, by product, or by salesperson).
Finding the detailed sources.
Online analytical processing (OLAP)
An information system that enables the user, while at a PC, to query
the system, conduct an analysis, and so on. The result is generated
in seconds
Online Analytical Processing (OLAP)
•
Types 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
Online Analytical Processing (OLAP)
Codd’s 12 Rules for OLAP
1.
2.
3.
4.
5.
6.
Multidimensional
conceptual view for
formulating queries
Transparency to the user
Easy accessibility: batch
and online access
Consistent reporting
performance
Client/server architecture:
the use of distributed
resources
Generic dimensionality
7. Dynamic sparse matrix
handling
8. Multiuser support rather
than support for only a
single user
9. Unrestricted crossdimensional operations
10.Intuitive data manipulation
11.Flexible reporting
12.Unlimited dimensions and
aggregation level
Data Visualization
• Data visualization
A graphical, animation, or video presentation of data and the results
of data analysis
– The ability to quickly identify important trends in corporate and market
data can provide competitive advantage
– Check their magnitude of trends by using predictive models that provide
significant business advantages in applications that drive content,
transactions, or processes
• New directions in data visualization
– In the 1990s data visualization has moved into:
• Mainstream computing, where it is integrated with decision
support tools and applications
• Intelligent visualization, which includes data (information)
interpretation
Data Visualization
Data Visualization
Data Visualization
• New directions in data visualization
– Dashboards and scorecards
• Will be discussed in later chapter.
– Visual analysis
– Financial data visualization
Geographic
Information Systems (GIS)
• Geographical information system (GIS)
An information system that uses spatial data, such as digitized
maps. A GIS is a combination of text, graphics, icons, and symbols
on maps
• As GIS tools become increasingly sophisticated and
affordable, they help more companies and governments
understand:
– Precisely where their trucks, workers, and resources are
located
– Where they need to go to service a customer
– The best way to get from here to there
Geographic
Information Systems (GIS)
• GIS and decision making
– GIS applications are used to improve decision
making in the public and private sectors including:
•
•
•
•
•
Dispatch of emergency vehicles
Transit management
Facility site selection
Drought risk management
Wildlife management
– Local governments use GIS applications for used
mapping and other decision-making applications
Geographic
Information Systems (GIS)
• GIS combined with GPS
– Global positioning systems (GPS)
Wireless devices that use satellites to enable users to detect
the position on earth of items (e.g., cars or people) the devices
are attached to, with reasonable precision
• GIS and the Internet/intranets
– Most major GIS software vendors provide Web access that
hooks directly to their software
– GIS can help the manager of a retail operation determine
where to locate retail outlets
– Some firms are deploying GIS on the Internet for internal use or
for use by their customers (locate the closest store location)
Real-Time BI,
Automated Decision Support (ADS),
and Competitive Intelligence
•
Real-time BI
– The trend toward BI software producing real-time data updates for
real-time analysis and real-time decision making is growing rapidly
– Part of this push involves getting the right information to
operational and tactical personnel so that they can use new BA
tools and up-to-the-minute results to make decisions
– Automated decision support (ADS) or enterprise decision
management (EDM)
A rule-based system that provides a solution to a repetitive
managerial problem. Also known as enterprise decision
management (EDM)
– Business rules
Automating the decision-making process is usually achieved by
encapsulating business user expertise in a set of business rules that are
embedded in a rule-driven workflow (or other action-oriented) engine
Real-Time BI,
Automated Decision Support (ADS),
and Competitive Intelligence
• Real-time BI
– Characteristics and benefits of ADS
ADS are most suitable for decisions that must be made
frequently and/or rapidly, using information that is available
electronically
– Concerns about real-time systems
• An important issue in real-time computing is that not all data
should be updated continuously
• when reports are generated in real-time because one
person’s results may not match another person’s causing
confusion
• Real-time data are necessary in many cases for the
creation of ADS systems
Real-Time BI,
Automated Decision Support (ADS),
and Competitive Intelligence
• Capabilities of ADSs
– Rapidly builds rules-based applications and deploys them into
almost any operating environment
– Injects predictive analytics into rule-based applications
– Provides services to legacy systems
– Combines business rules, predictive models, and optimization
strategies flexibly into state-of-the-art decision-management
applications
– Accelerates the uptake of learning from decision criteria into
strategy design, execution, and refinement
Real-Time BI,
Automated Decision Support (ADS),
and Competitive Intelligence
• ADS applications
– Product or service configuration
– Yield (price) optimization
– Routing or segmentation decisions
– Corporate and regulatory compliance
– Fraud detection
– Dynamic forecasting
– Operational control
Real-Time BI,
Automated Decision Support (ADS),
and Competitive Intelligence
• Implementing ADS—software companies provide these
components to ADS:
– Rule engines
– Mathematical and statistical algorithms
– Industry-specific packages
– Enterprise systems
– Workflow applications
BA and the Web: Web
Intelligence and Web Analytics
• Using the Web in BA
• Web analytics
The application of business analytics activities to Web-based
processes, including e-commerce. The main tools are Clickstream
analysis.
– Clickstream analysis
The analysis of data that occur in the Web environment.
– Clickstream data
Data that provide a trail of the user’s activities and show the user’s
browsing patterns (e.g., which sites are visited, which pages, how long) By
analyzing these data, a firm can find the effectiveness of promotions and
determine which products and ads attract the most attention.
As clickstream operations increase, the amount of data to process grows
exponentially, and scalability issues become critical for Web analytics.
BA and the Web: Web
Intelligence and Web Analytics
Clickstream
analysis
BA and the Web: Web
Intelligence and Web Analytics
• Vendor support for Web analytics
– BusinessObject provides a Web-enabled, full client solution to
querying and analysis called Web Intelligence. It allows to easily
track and manage information stored in multiple data sources with
and beyond the enterprise in an integrated manner form a desktop
computer. (SAP)
– Cognos provides a complete web service architecture in its
Cognos 8 product. These enable users easier software
development. (IBM)
– Informatica has focus on closely using the web to enable
organizations to track business performance.
– The Web Trends BI tool focus on real-time analysis of Web traffic and
online purchasing trends, revenues and the effecitiveness of ad
campaigns or sales promotions through millions of site visit daily.
– Google provide a Google Analytics to small organization for free. For
example, clickstream analysis.
Usage, Benefits,
and Success of BA
• Usage of BA
– Almost all managers and executives can use some BA systems,
but some find the tools too complicated to use or they are not
trained properly.
– Most businesses want a greater percentage of the enterprise to
leverage analytics; most of the challenges related to technology
adoption involve culture, people, and processes
• Success and usability of BA
– Performance management systems (PMS) are BI tools that
provide scorecards and other relevant information that decision
makers use to determine their level of success in reaching their
goals
Usage, Benefits,
and Success of BA
•
Why BI/BA projects fail
1. Failure to recognize BI projects as cross-organizational business
initiatives and to understand that, as such, they differ from typical
standalone solutions
2. Unengaged or weak business sponsors
3. Unavailable or unwilling business representatives from the
functional areas
4. Lack of skilled (or available) staff, or suboptimal staff utilization
5. No software release concept (i.e., no iterative development
method)
6. No work breakdown structure (i.e., no methodology)
7. No business analysis or standardization activities
8. No appreciation of the negative impact of “dirty data” on business
profitability
9. No understanding of the necessity for and the use of metadata
10. Too much reliance on disparate methods and tools
Usage, Benefits,
and Success of BA
•
System development and the need for integration
–
–
Developing an effective BI decision support application can be
fairly complex
Integration, whether of applications, data sources, or even
development environment, is a major CSF for BI
Online Analytical Processing (OLAP)
• Explanation: BA, BI, BI governance, BICC, GIS,
data visualization, real-BI.
• How many OLAPs do you know?