6 Discussion Management of Information Systems

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Figure 11.1 Potential applications of data mining © Cengage Learning 2015

A data warehouse is a large database containing historical transactions and other data

Data warehouses are useless without software tools to process the data into meaningful information

Business intelligence (BI) is information gleaned with information analysis tools

Also called business analytics

Data mining is a the process of selecting, exploring, and modeling large amounts of data

Data mining is used to discover relationships that can support decision making

Data-mining tools may use complex statistical analysis applications

Data-mining queries are more complex than traditional queries

Data-warehousing techniques and data-mining tools facilitate the prediction of future outcomes

The objectives of data mining are:

Sequence or path analysis, which is finding patterns where one event leads to another

Classification which is finding whether certain facts fall into predefined groups

Clustering which is, finding groups of related facts not previously known

And Forecasting which is discovering patterns that can lead to reasonable predictions

Data mining techniques are applied to various fields, including marketing, fraud detection, and targeted marketing to individuals

Data mining techniques are used in predicting customer behavior

Banking uses data mining to help find profitable customers, detect patterns of fraud, and predict bankruptcies

Mobile phone services vendors use data mining techniques to help determine factors that affect customer loyalty

Customer loyalty programs ensure a steady flow of customer data into data warehouses

Many industries utilize loyalty programs, e.g., frequent-flier programs and consumer clubs

Huge amounts of data about customers is amassed

UPS’ Customer Intelligence Group analyzes customer behavior and predicts customer defections so that a salesperson can intervene to resolve problems

Data mining techniques are used in identifying profitable customer groups

Financial institutions dismiss high-risk customers

Companies attempt to define narrow groups of potentially profitable customers

Data mining utilize loyalty programs

Companies develop customized email newsletters targeted to individual customers

Targeted special offers and partner specials are tailored to each customer

Figure 11.3 Using OLAP tables to compare the sales of three product models by continent

Online analytical processing (OLAP) is a technology used to exploit data warehouses

OLAP provides extremely fast response times and allows a user to view multiple combinations of two dimensions by rotating virtual “cubes” of information

Drilling down is a process of starting with broad information and then retrieving more specific information as numbers or percentages

OLAP can use relational or dimensional databases designed for OLAP applications

OLAP applications compose tables “on the fly” based on the desired relationships

A dimensional database is data is organized into tables showing information summaries

Also called multidimensional databases

OLAP applications are powerful tools for executives

Case: Ruby Tuesday restaurant chain

One location was performing below average

OLAP analysis showed that customers were waiting longer than normal

Appropriate changes were made

OLAP applications are usually installed on a special server

OLAP applications are faster than relational applications

OLAP is increasingly used by corporations to gain efficiencies

Office Depot used OLAP on a data warehouse to determine cross-selling strategies

Ben & Jerry’s tracks the popularity of ice cream flavors

Business Intelligence (BI) software is becoming easier to use

Intelligent interfaces accept queries in free form

BI software is integrated into Microsoft’s SQL Server database software

Figure 11.5 An employee knowledge network can capture information and distribute information not captured in an information system © Cengage Learning 2015

Knowledge workers: research, prepare, and provide information

There is much overlap in the work that they do

Money can be saved by collecting and organizing knowledge gained by workers

Avoid having workers solve the same problem that has already been solved by others

To support KM, organizations should require:

Workers to create reports of findings

Reports about sessions with clients

The biggest challenge for employees is how to find answers to specific questions

Some software tools can help

Bank of Montreal implemented application software providing information to its corporate credit card managers and sales force

Multiple reports were replaced with only a few dashboards

Purchase volume and the number of transactions were provided over a series of months by region and city

In addition to building knowledge bases, some tools direct employees to other employees who have the required expertise

Such experts can provide non-recorded expertise

No need to waste money hiring experts in every department

Learning from past mistakes can save money

Employee knowledge network is a tool that facilitates knowledge sharing through intranets

Tacit Systems’ ActiveNet tool:

Continually processes business communications (e-mail, documents, etc.) to build a profile of each employee’s topics, expertise, and interests

Profiles are accessible by other employees, but the private information used to create the profiles is not accessible to others

Helps ensure uninhibited brainstorming and communication

AskMe’s software detects and captures keywords from e-mail and documents created by employees

Creates a knowledge base with names of employees and their interests

Allows free-form search queries on Web

A search returns the names of employees who have created documents, e-mail, or presentations on the subject

Knowledge can be attained from the Web. Consumers post opinions of products on the web at various locations, such as:

On the vendor’s site

At product evaluation sites such as epinions.com

And In blogs

Distilling consumer opinions could aid a company’s market research, e.g., learning about their own products and those of their competitors

Some companies have developed software to search for this information

Factiva is a a software tool that gathers online information from over 10,000 sources

It collects information from newspapers, journals, market data, and newswires

Factiva screens all new information for information specified by a subscribing organization

It helps an organization know what others say about their products and services

Autocategorization (or automatic taxonomy): automates classification of data into categories for future retrieval

Used by companies to manage data

Used by most search engines

Constantly improved to yield more precise and faster results

U.S. Robotics (USR) wanted to reduce its customer support labor

A survey showed that most clients visited their website before calling support personnel

USR purchased autocategorization software

Accuracy and response was improved, allowing a higher number of support issues to be resolved by the web visit

Figure 12.1 The steps associated with information systems planning © Cengage Learning 2015

Enterprise ISs are gaining in popularity

IS resource investment considerations

Require a substantial investment

Carry a high risk in implementation

Successful integration of the system is vital

Must align IT strategies with the overall organization strategies

Careful planning of an IS implementation is necessary

Key steps in IT planning

Create a corporate and IT mission statement

Articulate the vision for IT within the organization

Create IT strategic and tactical plans

Create a plan for operations to achieve the mission and vision

Create a budget to ensure that resources are available to achieve the mission and vision

Mission statement: communicates the most important overarching goal of organization

Includes how the goals will be achieved

IT mission statement: describes the role of IT in the organization

Should be compatible with the organizational mission statement

Includes the ideal combination of hardware, software, and networking to support the mission

CIO develops a strategic plan for implementation of IT in the organization

Addresses what technology will be used by employees, customers, and suppliers

Goals in the plan are broken down into objectives, such as:

Resources to be acquired or developed

Timetables for acquiring and implementing resources

Training

Objectives are broken down to operational details

IT planning is similar to planning of other resource acquisitions

Growing proportion of IT funds is spent on software in recent years

More purchasing and adapting of software

Less developing in-house software

Systems analysis: a five-step process

Investigation

Technical feasibility study

Economic feasibility study

Operational feasibility study

Requirements definition

Investigation

Is a system really necessary?

Is the system, as conceived, feasible?

Small ad hoc team performs a preliminary investigation by interviewing employees

Feasibility studies: a larger analysis conducted after preliminary results indicate an IS is warranted

Technical feasibility study determines if:

Components exist or can be developed

The organization has adequate hardware

Economic feasibility study

Cost/benefit analysis: spreadsheet showing all costs and benefits of the proposed system

Return on investment (ROI): difference between the stream of benefits and the stream of costs over the life of the system

Operational costs during the system’s life include

Software license fees, maintenance personnel, telecommunications, power, and computer-related supplies

Total cost of ownership (TCO): a financial estimate for business leaders to objectively and accurately evaluate the direct and indirect costs of a new organizational project

Operational feasibility study determines how the new system will be used

Organizational culture: general tone of the corporate environment

Must determine the new system’s compatibility with the organizational culture

System requirements: detail the functions and features expected from the new system