ch09_businessintelligence

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2007 Prentice Hall, Inc. 1

Using Management Information Systems

David KroenkeBusiness Intelligence and Knowledge Management

Chapter 9


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Learning Objectives

Understand the need for business

intelligence systems.Know the characteristics of reporting

systems.

Know the purpose and role of datawarehouses and data marts.

Understand fundamental data-mining

techniques.Know the purpose, features, and functions

of knowledge management systems.


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The Nature of Intelligence

Some of the characteristics of intelligent behavior include the ability todo the following:

Learn from experience including the ability to learn by trial and errorApply knowledge acquired from experience to another situation

Handle complex situations

Solve problems when important information is missing is the essence ofdecision making when dealing with uncertainty

Determining what is important is a mark of a good decision makerThe ability to reason and think

Reacting quickly and correctly to a new situation

Understand and interpret visual images including processing andmanipulating symbols

Being creative and imaginativeUsing heuristics, or rules of thumb, or even guesses in making

decisions


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What is AI?

Artificial Intelligence systems include people,

procedures, hardware, software, data and knowledgeneeded to develop computer systems and machines

that demonstrate characteristics of intelligence [Ralph

Stair].


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Components of AI

Expert Systems are computer programs that act or behave like a

human expert in a field or area.

Robotics involves developing mechanical or computer devicescontrolled by software to perform tasks that require a high degree of

precision or are tedious or hazardous for humans

VisionSystems include hardware and software that permit computers

to capture, store and manipulate visual images and pictures

NaturalLanguageProcessing allows the computer to understand andreact to statements and commands made in a "natural" language, such

as English

LearningSystems include hardware and software that allow the

computer to change how it functions or reacts to situations based on

feedback it receives

NeuralNetworks are computer systems that act like or simulate the

functioning of the human brain


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Expert Systems

Expert systems are created by interviewing experts in a given business

domain and codifying the rules stated by those experts.

Many expert systems were created in the late 1980s and 1990s, and

some of them have been successful.

Expert systems suffer from three major disadvantages.

They are difficult and expensive to develop.

They are difficult to maintain.

They were unable to live up to the high expectations set by their

name.


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The Need for Business Intelligence Systems

According to a study done at the University of

California at Berkeley, a total of 403 petabytes of

new data were created in 2002.

403 petabytes is roughly the amount of all printed

material ever written. The printed collection of the Library of Congress is .01

petabytes.

400 petabytes equals 40,000 copies of the print

collection of the Library of Congress.


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Business Intelligence Systems

The purpose of a business intelligence (BI)

system is to provide the right information, to theright user, at the right time.

BI systems help users accomplish their goals and

objectives by producing insights that lead to

actions.


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Business Intelligence Tools

Tools for searching business data in an attempt to find patterns is

called business intelligence (BI) tools.The processing of data is simple:Data are sorted and grouped and

simple totals and averages are calculated.

Reporting tools are used to address questions like:

What has happened in the past?

What is the current situation?

How does the current situation compare to the past?

Data-mining tools process data using statistical techniques, many ofwhich are sophisticated and mathematically complex.

Data mininginvolves searching for patterns and relationships among

data.In most cases, data-mining tools are used to makepredictions.

For example, we can use one form of analysis to compute theprobability that a customer will default on a loan.

Data-mining tools use sophisticated techniques.


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Data MiningData mining is the application of statistical techniques to find patterns

and relationships among data and to classify and predict.

Data mining represents a convergence of disciplines.

Data-mining techniques emerged from statistics and mathematics and

from artificial intelligence and machine-learning fields in computer

science.


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Unsupervised Data MiningWith unsupervised data mining, analysts do not

create a model or hypothesis before running theanalysis.

Instead, they apply the data-mining technique to

the data and observe the results.

Analysts create hypotheses after the analysis to

explain the patterns found.


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Supervised Data MiningWith supervised data mining, data miners

develop a model prior to the analysis and apply

statistical techniques to data to estimateparameters of the model.

One such analysis, which measures the impact of

a set of variables on another variable, is called a

regression analysis.

Neural networks are another popular supervised

data-mining technique used to predict values and

make classifications such as good prospect orpoor prospect customers.


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Data Warehouses and Data MartsBasic reports and simple OLAP analyses can bemade directly from operational data.

Many organizations choose to extract operationaldata into facilities called data warehouses anddata marts, both of which are facilities thatprepare, store, and manage data specifically for

data mining and other analyses.Programs read operational data and extract,clean, and prepare that data for BI processing.

The prepared data are stored in a data-warehousedatabase using data-warehouse DBMS, which canbe different from the organizations operationalDBMS.


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Data Warehouses Versus Data MartsA data martis a data collection, smaller than the

data warehouse, that addresses a particularcomponent or functional area of the business.

The data warehouse is like the distributor in the

supply chain and the data mart is like the retail

store in the supply chain.

Users in the data mart obtain data that pertain to a

particular business function from the data

warehouse.It is expensive to create, staff, and operate data

warehouses and data marts.


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Problems with Operational DataMost operational and purchased data haveproblems that inhibit their usefulness for businessintelligence.

D i i T


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Decision TreesA decision tree is a hierarchical arrangement of

criteria that predict a classification or a value.

Decision tree analyses are an unsupervised data-mining technique.

The analyst sets up the computer program and

provides the data to analyze, and the decision treeprogram produces the tree.

A D i i T f L E l ti


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A Decision Tree for Loan EvaluationA common business application of decision trees is to

classify loans by likelihood of default.

Organizations analyze data from past loans to produce adecision tree that can be converted to loan-decision rules.

A financial institution could use such a tree to assess the default

risk on a new loan.


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Reporting Systems

The purpose of a reporting system is to create

meaningful information from disparate datasources and to deliver that information to the

proper user on a timely basis.

Reporting systems generate information from data

as a result of four operations: Filtering data

Sorting data

Grouping data

Making simple calculations on the data


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Components of Reporting Systems

A reporting system maintains a database of

reporting metadata.The metadata describes the reports, users,

groups, roles, events, and other entities involved

in the reporting activity.

The reporting system uses the metadata to

prepare and deliver reports to the proper users on

a timely basis.


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Report TypeIn terms of a report type, reports can be staticor

dynamic.Static reports are prepared once from theunderlying data, and they do not change. Example, a report of past years sales

Dynamicreports: the reporting system reads themost current data and generates the report usingthat fresh data. Examples are: a report on sales today and a report on

current stock prices

Query reports are prepared in response to data enteredby users.

Online analytical processing(OLAP) reports allow the userto dynamically change the report grouping structures.


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Report MediaReports are delivered via many different report media or

channels.Some reports are printed on paper, and others are createdin a format like PDF whereby they can be printed or viewedelectronically.

Other reports are delivered to computer screens.

Companies sometimes place reports on internal corporateWeb sites for employees to access.

Another report medium is a digital dashboard, which is an

electronic display customized for a particular user.

Vendors like Yahoo! and MSN provide common examples.

Users of these services can define content they want- say, a local

weather forecast, a list of stock prices, or a list of news sources.

The vendor constructs the display customized for each user.


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Digital Dashboard Example


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RFM AnalysisRFM analysis is a way of analyzing and ranking customers accordingto their purchasing patterns.

It is a simple technique that considers how recently(R) a customer hasordered, how frequently(F) a customer orders, and how much money(M)the customer spends per order.

To produce an RFM score, the program first sorts customer purchaserecords by the date of their most recent (R) purchase.

In a common form of this analysis, the program then divides the

customers into five groups and gives customers in each group a scoreof 1 to 5.

The top 20% of the customers having the most recent orders are given anR score 1 (highest).

The program then re-sorts the customers on the basis of how

frequently they order. The top 20% of the customers who order most frequently are given a F

score of 1 (highest).

Finally the program sorts the customers again according to the amountspent on their orders.

The 20% who have ordered the most expensive items are given an M

score of 1 (highest).


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Online Analytical ProcessingOnline analytical processing (OLAP) provides the ability to sum,

count, average, and perform other simple arithmetic operations on

groups of data.

The remarkable characteristics of OLAP reports is that they are

dynamic.

The viewer of the report can change the reports format, hence, the

term online.

An OLAP report has measures and dimensions.

A measure is the data item of interest. It is the item that is to be summed or averaged or otherwise

processed in the OLAP report.

A dimension is a characteristic of a measure. Purchase data, customer type, customer location, and sales region

are all examples of dimension.

With an OLAP report, it is possible to drill down into the data.

This term means to further divide the data into more detail.

Market Basket Analysis


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Market-Basket AnalysisA market-basket analysis is a data-miningtechnique for determining sales patterns.

A market-basket analysis shows the products thatcustomers tend to buy together.

In market-basket terminology, support is theprobability that two items will be purchased

together.You can expect market-basket analysis to becomea standard CRM analysis during your career.

Knowledge Management


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Knowledge ManagementKnowledge management systems concern the sharing of knowledge

that is already known to exist, either in libraries of documents, in the

heads of employees, or in other known sources.

Knowledge management (KM) is the process of creating value fromintellectual capital and sharing that knowledge with employees,

managers, suppliers, customers, and others who need that capital.

Knowledge management is a process that is supported by the fivecomponents of an information system.

Its emphasis is on people, their knowledge, and effective means forsharing that knowledge with others.

The benefits of KM concern the application of knowledge to enableemployees and others to leverage organizational knowledge to worksmarter.

KM preserves organizational memory by capturing and storing thelessons learned and best practices of key employees.

Content Management Systems


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Content Management SystemsContent management systems are information systems that track

organizational documents, Web pages, graphics, and related materials.

KM content management systems are concerned with the creation,

management, and delivery of documents that exist for the purpose ofimparting knowledge.

Typical users of content management systems are companies that sell

complicated products and want to share their knowledge of those

products with employees and customers.

The basic functions of content management systems are the same as

for report management systems: author, manage, and deliver.

The only requirement that content managers place on document

authoring is that the document has been created in a standardized

format.

Content Delivery


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Content DeliveryAlmost all users of content management systemspullthecontents.

Users cannot pull content if they do not know it exists. The content must be arranged and indexed, and a facility for

searching the content devised.

Documents that reside behind a corporate firewall, however,are not publicly accessible and will not be reachable by

Google or other search engines. Organizations must index their own proprietary documents and

provide their own search capability for them.

Web browsers and other programs can readily format content expressedin HTML, PDF, or another standard format.

XML documents often contain their own formatting rules that browserscan interpret.

The content management system will have to determine an appropriateformat for content expressed in other ways.


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KM Systems to Facilitate the Sharing of Human

KnowledgeNothing is more frustrating for a manager to

contemplate than the situation in which one employeestruggles with a problem that another employee knowshow to solve easily.

KM systems are concerned with the sharing not only

of content, but also with the sharing of knowledgeamong humans. How can one person share her knowledge with another?

How can one person learn of another persons great idea?

Three forms of technology are used for knowledge- sharing

among humans: Portals, discussion groups, and email

Collaborations systems

Expert systems


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Ethics GuideThe Ethics of Classification

Classification is a useful human skill.

Sorting and classifying are necessary, important,and essential activities. But those activities can also be dangerous

Serious ethical issues arise when we classifypeople. What makes someone a good or bad prospect?

If were talking about classifying customers in order toprioritize our sales calls, then the ethical issue may notbe too serious.

What about classifying applicants for college?