Tuesday, 26 May 2015

Assignment Two (2)



UNIVERSITY OF CAPE COAST
COLLEGE OF DISTANCE EDUCATION
DEPARTMENT OF BUSINESS
MASTER OF BUSINESS ADMINISTRATION











BUS 809: MANAGEMENT INFORMATION SYSTEMS
ASSIGNMENT TWO (2)








BY:



SB/DAC/14/0033
CHAPTER SIX (6)
FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT

6.1 ORGANIZING DATA IN A TRADITIONAL FILE ENVIRONMENT
For an organization to have an effective information system that provides users with accurate, timely and relevant information, requires a good and organized data management system. Information is useful when decision makers get it on time and that can be achieved by maintaining an organized data system.

File Organization Terms and Concepts
The computer system organizes data in a hierarchy starting from the least to the highest as follows: Bit and bytes, fields, records, files and databases. Examples are;
Bit: - a character like K, O, F or I.
Field: - combing the characters to form a word like Kofi.
Record: - grouping the fields like Kofi’s Academic details, that, name, course offered, date admitted and grades obtained makes up a record.
Files: - a group of records of same type makes up the file like course details of a number of students.
Databases: - This is made up of a group of related files like a students’ financial file, course file and personal file.


Problems with the Traditional File Environment
The following are some problems with the traditional file environment in most organizations:

Data Redundancy and Inconsistency
Data redundancy is where there is duplicate data in different data files making same data be stored in more than one location. It occurs when different groups in an organization collect data independently and store it independently. In this case an update of file in one group doesn’t automatically update the same record with another group which makes it inconsistent. Example if a student’s name has been updated in the course file it doesn’t update same name at the financial file.

Program-Data Dependence
The traditional file system stores data in specific programs which require that a change in the program will require a change in the data too. For instance, a program used require five letter index number to record a student’s data and if a program which uses a ten letter is installed, the old one becomes less useful as it will not work in the new system and requires a change.

Lack of Flexibility
The traditional file system is too rigid in that, it only produces routine reports but ad hoc reports will be expensive to retrieve with more time.

Poor Security
The traditional system has little control or management of data which makes access to and dissemination of information may be out of control, because there is no way of knowing who is accessing or even making changes to the organization’s data.

Lack of Data Sharing and Availability
Due to the information scattered in different files in different groups in the organization, it is virtually impossible for information to be shared or accessed in a timely manner. Information cannot flow freely across different functional areas or different parts of the organization.

6.2 THE DATABASE APPROACH TO DATA MANAGEMENT
Database is a collection of data organized to serve many applications efficiently by centralizing the data and controlling redundant data. Data is stored in system in one location but shared by all the groups which need it.

Database Management Systems
A database management system (DBMS) is software that permits an organization to centralize data, manage them efficiently, and provide access to the stored data by application programs. The DBMS acts as an intermediary between application programs and the data files. For instance using the DBMS to search for say a name, it will produce all related data concerning the name without requesting then you can choose what you want unlike the traditional system which needs a specific description to find the name for you.

How a DBMS Solves the Problems of the Traditional File Environment
It helps reduces data redundancy
It helps eliminates data inconsistency
It helps get access and availability to information
It helps manage data centrally, its use and security

Relational DBMS
Relational DBMS is a type of database that uses different database models to keep track of entities, attributes, and relationships. Examples of relational DBMS are Microsoft Access, DB2, Oracle Database, and Microsoft SQL. Relational database uses tables which can be combined easily to deliver data required by users, provided that any two tables share a common data element.

Object-Oriented DBMS
Object-oriented DBMS is a database system that can store and retrieve not only structured numbers and characters but also drawings, images, photographs, voice, and full-motion video. Object-oriented DBMS is designed for organizing structured data into rows and columns are not well suited for handling graphics based or multimedia applications.

Capabilities of Database Management Systems
DBMS is capable of organizing, managing and accessing data
DBMS is cable of defining data to specify the structure of the content
It is capable of defining data language, data dictionary and data manipulation language

Designing Databases
To create a database in a company-wide perspective, you must understand the relationships among the data, the type of data that will be maintained in the database, how the data will be used, and how the organization will need to change to manage data.

6.3 USING DATABASES TO IMPROVE BUSINESS PERFORMANCE AND DECISION MAKING
Businesses perform a number of activities such as to keep track of basic transactions, such as paying suppliers, processing orders, keeping track of customers, and paying employees and a use of a database helps to perform such tasks with ease.
Businesses also need databases to provide information that will help the company run its operations more efficiently, and help managers and employees make better decisions. Decisions like knowing which product is the most popular or who is its most profitable customer. These capabilities include data warehousing, data mining, and tools for accessing internal databases through the Web.


Data Warehouse
A data warehouse is a database that stores current and historical data of potential interest to decision makers throughout the company. The data comes in many core operational transaction systems, such as systems for sales, customer accounts, and manufacturing, and from Web site transactions. The data warehouse consolidates and standardizes information from different operational databases so that the information can be used across the enterprise for management analysis and decision making.
The data warehouse makes the data available for anyone to access as needed, but it cannot be altered. A data warehouse system also provides a range of ad hoc and standardized query tools, analytical tools, and graphical reporting facilities

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 or a smaller, decentralized warehouse in which a summarized or highly focused portion of the organization’s data is placed in a separate database for a specific population of users. For example, a company might develop marketing and sales data marts to deal with customer information.

Tools for Business Intelligence: Multidimensional Data Analysis and Data Mining
Business intelligence tools enable users to analyze data to see new patterns, relationships, and insights that are useful for guiding decision making after data has been captured and organized in the data warehouses and data marts. Principal tools for business intelligence include the following:

Online Analytical Processing (OLAP)
OLAP is a system that allows users of data to view same data in a multidimensional manner using different ways using each aspect of information such as product, pricing, cost, region or time period.
OLAP enables users to obtain online answers to ad hoc questions such as, how many washers were sold in the East in June, how that compares with the previous month and the previous June, and how it compares with the sales forecast.

Data Mining
Data mining is more discovery-driven which provides insights into corporate data that cannot be obtained with OLAP by finding hidden patterns and the view that is shows product versus region. The types of information obtainable from data mining include associations, sequences, classifications, clusters, and forecasts.
• Associations are occurrences linked to a single event.
• In sequences, events are linked over time.
• Classification recognizes patterns that describe the group to which an item belongs by examining existing items that have been classified and by inferring a set of rules.
• Clustering works in a manner similar to classification when no groups have yet been defined.
• Although these applications involve predictions, forecasting uses predictions in a different way. It uses a series of existing values to forecast what other values will be.

Text Mining and Web Mining
Text mining tools are used in analyzing unstructured data such as E-mail, memos, call center transcripts, survey responses, legal cases, patent descriptions, and service reports now available to help businesses analyze these data. The Web is another rich source of valuable information, some of which can now be mined for patterns, trends, and insights into customer behavior. Web mining is the discovery and analysis of useful patterns and information from the World Wide Web. For instance, marketers use Google Trends and Google Insights for Search services, which track the popularity of various words and phrases used in Google search queries, to learn what people are interested in and what they are interested in buying.

6.4 MANAGING DATA RESOURCES
After setting up a database, there is the need to make sure that the data for your business remain accurate, reliable, and readily available to decision takers and therefore calls for managing the data resources. The following are some policies for managing data resources:

Establishing an Information Policy
An information policy shows the organization’s rules and regulations for sharing, disseminating, acquiring, standardizing, classifying, and inventorying information. Since the firm’s data is an important resource, there is the need for rules on how to organize and maintain it, who is allowed to view them, rules on procedures and accountability, identifying which users can share, where it can be distributed and who is responsible for updating and maintaining information.

Ensuring Data Quality
The need for setting up an information system that ensures that, data in the organization’s databases are accurate, reliable and consistent. Data that is inaccurate, untimely, or inconsistent with other sources of information lead to incorrect decisions, product recalls, and financial losses. 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 system. Data quality audits can be performed by surveying entire data files, surveying samples from data files, or surveying end users for their perceptions of data quality. Data cleansing, also known as data scrubbing, consists of activities for detecting and correcting data in a database that are incorrect, incomplete, improperly formatted, or redundant

No comments:

Post a Comment