Lecture notes 4 foundation of business intelligent

MANAGEMENT INFORMATION SYSTEMS (MIS)MANAGEMENT INFORMATION SYSTEMS (MIS)

LECTURE NOTES 4LECTURE NOTES 4

SPRING 2010SPRING 2010

FOUNDATIONS OF BUSINESS INTELLIGENTFOUNDATIONS OF BUSINESS INTELLIGENT(DATABASES AND INFORMATION MANAGEMENT)(DATABASES AND INFORMATION MANAGEMENT)

FOUNDATIONS OF BUSINESS INTELLIGENCEFOUNDATIONS OF BUSINESS INTELLIGENCE

This lecture focuses on Data Management and how Businesses use Database

Technology to achieve their objectives.

• When Businesses use Database Management Systems (DBMS) to organize their data, data get analyzed and the resulting information can be used to:

Develop New businesses, Achieve Operational Excellence, Improve Management Decision Making, Help the firm fulfil its Regulatory Reporting requirements.

• Databases are the backbone or foundation of the business today and that most businesses would fail should their Databases cease to exist.


Database Knowledge is essential for several professions

If your career is in Information Technology :

• You will play a key role in providing Data Management Tools and expertise to

the firm.

• You will be expected to Design Databases, implement and maintain Database Technology, and help promote Data Administration Policies and Procedures.

If your career is in Finance and Accounting:

• You will be using Databases to deal with Financial transactions such as Payments, Invoices, or Credit history. Or you will be working solidly with a massive Databases housing data about Security Stock prices , Investment Portfolios and Economic statistics.


If your career is in Sales and Marketing:

• You will be using Databases for tracking Customer orders, Analyzing Customer data for targeted Marketing campaigns, or identify profitable customers and products.

If your career is in Manufacturing Production, or Operations Management :

• You will be working with large Databases with data on raw materials, Finished goods in inventory , Suppliers, Product components. Product quality and goods in transit that can be used for Supply Chain Management.

If your Career is in Human Resources:

• You will be working with Databases maintaining data on Employees, Benefit plans, Compensation plans, Training programs, and Compliance with Governmental regulations on health, safety, and equal employment opportunity.


USING DATABASES TO IMPROVE BUSINESS PERFORMANCE AND DECISION MAKING

Businesses use Databases to:

Keep track of basic transactions, such as Paying Suppliers, Processing

Orders, Keeping track of Customers, and Paying Employees etc ….. .

Provide information that will help the company run the business more efficiently, by helping managers and employees make better decision.

In a large company with large Database Systems for separate Business

Functional areas such as Manufacturing and sales, special capabilities and tools

are required to analyze vast quantities of data and to access data from multiple

Systems.

• Special capabilities such as Data Warehousing , Data Mining, and Database Access tools can provide accessing facility to internal Databases via Web.


DATA WAREHOUSES

Data Warehouse is a Database that stores both current andhistorical data of potential interest to Decision Makers throughoutthe firm.

• Suppose that a Manager wants concise, reliable information about current operations, trends, and changes across the entire company. Obtaining this information in a large company might be difficult because data are

often maintained in separate Databases for specific Application Systems, such as Sales, Manufacturing, or Accounting Systems.

Moreover, some of the data may be in Sales Systems Database , and other pieces of data may be in the Manufacturing System Database.

Additionally, Systems may be Legacy Systems that use outdated DBMS Technologies or non-Database File Systems where information is difficult for users to access.


DATA WAREHOUSES (Continued)

A Manager might have to spend a lot of time locating and gathering the needed data for decision making, or s/he may be forced to make decision based on incomplete knowledge.

Also, Manager might face with troubles if s/he wants to find data about past events since most firms only make the current data immediately available. - Data Warehousing address all of these problems.

The data originated in many Core Operational Transaction Systems, such as

Sales, Customer Accounts and Manufacturing, as well as data from

Web site transactions and from outside databases are consolidates and

standardize in a Data Warehouse.

• As a result of Data Warehouse, information from different Operational Databases can be used across the Enterprise Management for Analysis and Decision making.


DATA WAREHOUSES (Continued )

Data Warehouse makes the data available for anyone to access, as needed. However, data on Warehouse cannot be altered by the users.

A Data Warehouse System also provide a range of Ad-hoc and Standardized

Query tools, Analytical tools, and Graphic reporting facilities.

Many Companies use Intranet Portals to make the Data Warehouse Information widely available across the firm.


DATA WAREHOUSE

Data Warehouse extracts current and historical data from multiple Internal

Operational Systems. This data is combined with data extracted from External sources

and reorganized into a Central Database designed for Management Reporting and

Analysis purpose.

The Information Directory provides Users with information about the available data.

the Warehouse.


DATA MARTS

Companies often build Enterprise-wide Data Warehouses, where a Central Data

Warehouse serves the entire organization, or they create smaller, Decentralized

Warehouses called Data Marts.

A Data Mart is a subset of a Central Data Warehouse, in which a summarized

or highly focused portion of the organization’s data is placed in a separate

Database for a specific Users population .

e.g. A company might develop Marketing and Sales Marts to deal with Customer information.

Data Mart typically focuses on a single Business area or line of business areas, so it can be constructed more rapidly and at lower cost than an Enterprise-wide Data Warehouse.


BUSINESS INTELLIGENCE TOOLS,

Data that have been captured and organized in Data Warehouses and Data

Marts, are available for further analysis.

A series of tools enables users to analyze the data to see new patterns, new

relationships, and insights that are useful for guiding Management Decision making.

These tools for consolidating, analyzing and providing Access to vast amounts of data to help Users make better business decisions are often referred to as Business Intelligence (BI).

The main Tools for Business Intelligence include :-

Database Query and Reporting Software Multidimensional Data Analysis Tools (Online Analytical Processing - OLAP) Data Mining


BUSINESS INTELLIGENCE TOOLS

Business Intelligence tools provide firms with the capability to access mass

Information to:

Develop knowledge about Customers, Competitors, and Internal

Operations;

Change Decision making behaviour to achieve higher profitability

and other business goals.


BUSINESS INTELLIGENCE TOOLS

Firm’s Operational Databases keep track of the transactions generated as a

result of running the business.

Operational Databases feed data to the Warehouse. Managers use Business Intelligence tools to find patterns and meanings in the data.

Managers then act on what they have learned from analyzing the data by making more informed and intelligent business decisions.


ONLINE ANALYTICAL PROCESSING (OLAP)

Online Analytical Processing (OLAP). Supports Multidimensional Data

Analysis, enabling users to view the same data in different ways using multiple

dimensions.

Each aspect of information such as - Product , Cost, Pricing, Region, or Time period - represents a different dimension.

e.g. A Product Manager could use multidimensional Data Analysis tools to

learn:- How many of a particular item were sold in Southeast region in March 2010 How that compares with the previous month and the previous March, How it compares with the Sales Forecast.

OLAP enables users to obtain Online answers to Adhoc questions in very large Databases, such as Sales figures for multiple years.





The figure shows Multidimensional Model that could be created to represent say

Products, Regions, Actual Sales, and Projected Sales.

A Matrix of Actual Sales can be stacked on top of a matrix of projected sales to form a

cube with six faces.

If you rotate the cube 90 degrees again, you will see Region Versus Actual and Projected

Sales.

If you rotate 180 degrees from the original view you will see Projected Sales and Product versus Region.

Cubes can be nested within cubes to build complex views of data.

A company would use either a Specialized Multidimensional Database or a

Tool that creates Multidimensional views of Data in Relational DBMS.


DATA MINING

Traditional Database Query answer such as :

How many units of Product # 403 were shipped in March 2010?

OLAP supports much more complex requests for information such as ;

Compare Sales of Product 403 relative to Plan by Quarter and Sales Region for the past two years.

With both OLAP and Query-oriented Analysis, Users need to have a good idea about the information for which they are looking for.

Data Mining however is more discovery driven:

– Data Mining provides insight into corporate data that can not be obtained with OLAP or traditional Database Query

– Data Mining finds hidden patterns and relationship in large Databases andInferring Rules from them to predict future behaviour.

The Patterns and Inferring Rules are used to guide Decision making and forecast the effect of those decisions.


DATA MINING

The type of Information obtained from Data Mining include:

Associations Sequences Classifications Clusters Forecasts

ASSOCIATIONS - Are occurrences linked to a single event. E.g. A study of Supermarket Purchasing patterns might reveal that, when Corn Chips are purchased, 65% of the time. a Cola drink is also

purchased. But when there is a Promotion, cola drink is purchased 85% of the time with Corn Chips/.

This information helps the Managers make better decisions because they have learned the profitability of a promotion.

SEQUENCES - Sequences of events are linked over time. We might find for example, that if a House is purchased, 65% of the time a

new refrigerator will be purchased within two week , and 45% of the time an Oven will be bought within one month of the home purchase.


DATA MINING

CLASSIFICATION - Recognizes patterns that describe the group to which an item belongs to. by examining existing items that have been

classified and by inferring a set of rules.

E.g: Businesses such as Credit Card or Telephone companies worry about the loss of steady customers. The Classification helps discover the characteristics of Customers who are likely to leave and can provide a model to help managers predict those customers so that the Managers can devise special campaigns to retain such customers.

CLUSTERING – Works in a manner similar to Classification when no groups have yet been defined. A Data Mining Tool can discover different groupings within data, such as finding affinity (similar) groups for Bank Cards or Partitioning a Database into groups of Customers based on demographics and types of personal investment.

FORECASTING – Uses prediction in a different way than the others. It uses a series of existing values to forecast what other values will be.

e.g. Forecasting might find patterns in data to help managers estimate the future value of continuous variables, such as Sales

figures.


DATA MINING

Associations, Sequences, Classifications, Clustering and Forecasting perform high-level Analyses on patterns or trends, but they can also Drill down to provide more detail when needed.

There are Data Mining Applications for all the Functional areas of business, and for government and scientific work. One popular use of Data Mining is to provide detailed Analyses of Patterns in Customer Data for One-to-One Marketing Campaigns or for identifying profitable customers.

Example - Virgin Mobile Australia uses a Data Warehouse and Data Mining to increase Customer loyalty and roll out new services.

The Data Warehouse consolidates data from its Enterprise Systems, Customer Relationship Management System, and Customer Billing Systems in a massive Database.

Data Mining has enabled Management to determine the demographic profile of new customers and relate it to the handsets they purchased.

It has also helped Management evaluate the performance of each store and point-of-sale campaign, Customer reactions to new products and services, customer attrition rates, and the revenue generated by each customer.


PREDICTIVE ANALYSIS

Predictive Analysis predicts the outcomes of events, such as the probability of acustomer willingness in responding to an offer on purchase a specific product.

Predictive Analysis uses the following factors

• Data Mining Techniques• Historical data, • Assumptions about future conditions

Example; Body Shop International Plc., used Predictive Analysis with its Catalogue Database on their Web site, and Retail Store Customers to identify Customers who were more likely to make catalogue purchases.

This information helped the company build a more precious and targeted mailing list for its Catalogues, improving the response rate for catalogue mailing and Catalogue revenue.


Data Mining Technology can combine information from many diverse sources to create a detailed ‘’Data Image’’ about each of us – our income, our driving habits , our hobbies , our families, and our political interests, within the privacy protection law!.

Thus, Data Mining is both a powerful and profitable tool, but it poses challenges to the protection of individual privacy.


DATABASES AND THE WEB

Many companies now use the Web to make some of the information in their internal Databases available to Customers and business partners.

A Customer with a Web Browser can search an online Retailer’s Database for pricinginformation.

The Customer as shown in the figure below, access the Retailer’s Web site over the Internet using Browser software to request pricing data from the Retailer organization’s Database, using HTML or XML commands to communicate with the Web Server.

• Users access an organization’s Internal Database through the Web using PCs and Web Browser Software.



Since many Back-end Databases cannot interpret commands written in HTML, the Web Server passes these requests for data to software that

translates HTML commands into SQL so that they can be processed by the DBMS.

In a Client/Server Architecture environment, the DBMS resides on a dedicated Computer called Database Server. The DBMS receives the SQL requests and Returns the required data.

The Middleware transfers information from the Organization’s internal Databases back to the Web Server for delivery in the form of a Web page to the Customer.



The Application Server software handles all application operations, includingTransaction Processing and Data access, between browser-based computers

(PC) and the company’s back-end business applications or Databases.

• The Application Server takes requests from the Web Server , runs the business logic to process transactions based on the request, and provides connectivity to the organization’s back-end Systems or Databases.

Alternatively the Software for handling Application Server operations could be a custom program or a CGI Script.

CGI Script is a compact program using Common Gateway Interface

specification for processing data on a Web Server.



There are a number of Advantages of using the Web to access an Organization’s

internal Databases:

1. Web Browser Software is much more easier to use by the users than proprietary Query Tools like SQL.

2. The Web Interface requires few or no changes to the internal Databases.

3. It also cost much less to add a Web interface in front of a Legacy System than to redesign and rebuild the Legacy System to improve User access.

4. Accessing Corporate Database through the Web is creating new efficiencies, opportunities and Business Models.


MANAGING DATA RESOURCES

Managing Data Resources after setting up of the Database is also important.

Special Policies and Procedures, called (Information Policies) will be needed for Data Management in order to make sure that the data of the business remains accurate, reliable, and readily available to those who need it.

ESTABLISHING AN INFORMATION POLICY

An Information Policy specify the organization’s rules for, acquiring,

standardizing , classifying , storing, sharing, and disseminating information.

An Information Policy lays out specific Procedures and accountabilities,

identifying which users and organizational units can share information, where

information can be distributed, and who is responsible for updating and

maintaining the information.


ESTABLISHING AN INFORMATION POLICY (Continued)

A typical example of an Information policy that specify only selected Users of the payroll and human resources department that would have the right to change and view sensitive employee data, such as an employee’s salary or social security number, and that the department is responsible for making sure that such employee data are accurate.

The Information Policy in a small business would be established by the owners or Managers.

In a large organization, managing and planning for information as a corporate resource often require a formal

Data Administration function.


ESTABLISHING AN INFORMATION) POLICY (Continued)

Data Administration is responsible for the specific policies and procedures through which data can be managed as an organizational resource.

Data Administration’s responsibilities include :

Developing information policy, Planning for data, Overseeing Logical Database Design Developing Data Dictionary Monitoring Systems specialists and end-user groups usage of data.


ESTABLISHING AN INFORMATION POLICY (Contınued)

Data Governance (promoted by IBM) used to describe many of the Information Policy activities.

Data Governance deals with the Informatıon policies and processes for managing the availability, usability, integrity, and security of the data employed in an enterprise, with special emphasis on promoting privacy, security, data quality, and compliance with government regulations.

A Large organization will also have a Database Design and Management Group within Information Systems Division.

Database Desıgn and Management Group is responsible for defining and organizing the structure and content of the Database, and maintaining the Database.

The Establishment of the Physical Database, the Logical relationships among elements, and the access rules and security procedures are performed by Database Administration .


ENSURING DATA QUALITY

A well designed Database backed up with an Information Policy will go a long way toward ensuring that the business has the information it needs.

Additional steps must also be taken to ensure that the data in Organizational Databases ( Corporate Databases) are accurate and remain reliable.

If a Database is properly designed and Enterprise-wide Data standards established, duplicate or inconsistent data elements should be minimal.

Most Data quality problems, such as misspelled names, transposed numbers or incorrect or missing codes, stem from errors during data input process.

As companies move their businesses to the Web and allow customers and Suppliers to enter date that update their internal Systems directly via Web, the quality problem caused by the Data entry will remain in the agenda.

Before a new Database is installed , organizations need to identify and correct their faulty data and establish better routines for editing data once

their Database is in operation.


DATA QUALITY AUDIT

Analysis of Data Quality often begins with a Data Quality Audit, which is a

structured survey of the accuracy and level of completeness of the data in an

Information Systems.

Data Quality Audits can be performed by surveying:

Entire Data files, Samples from Data files, End-users for their perceptions of Data Quality.


DATA CLEANSING (DATA SCRUBBING)

Data Cleansing is consisted of activities for detecting and correcting data in a

Database that are incorrect, incomplete, improperly formatted, or redundant.

Data Cleansing not only corrects errors but also enforces consistency among different sets of data (Fıle) that originated in separate Information Systems.

Specialized Data Cleansing Software is available to automatically survey data files, correct data, and integrate the data in a consistent enterprise -wide format.

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