Database Systems

Database systems help to organize information on a computer. Database systems allow an organization to be able to run smoothly, this means that database systems give a convenient way to handle business transactions.
The Database systems that are used in my workplace are the DBMS. The DBMS helps us to support, organize, store, delete or retrieve the data in a database.
Database architecture describes the architectures of data structures; this means that the database architecture describes the different aspects of a database and how it all put together or organized.
For the database in my workplace the architecture they fall under is the DBMS architecture which consists of the external level, conceptual level, and the internal level. The external and conceptual levels focus on and serve the DBMS users while the internal level is effective implementation details.
Relational database architecture is a protocol set that allows multiple database systems and application programs to work together.

Database Management Systems data collection, organization, maintenance and, dissemination is crucial to an agencies daily operation and fiscal well-being. Operating Systems: The DBMS reside on two operating systems; AIX and Windows. The AIX mainframe maintains all of the patient??™s information. The mainframe is ideal for this particular function since all users access one system, maintain the same patient data and, provides the output necessary to provide patient information and billing information. The Windows systems are also well suited for their functions. However, since various user interfaces or DBMS applications are used these management systems have differing operating system requirements for their respective databases. As such we have databases residing on three different versions of Windows; Windows 2000, Windows 2003 and, Windows 2008.
Design and Function virtually all of the databases have three distinct versions. We maintain a test system, training system and, production system. The test system or test upgrade system exists primarily to test new modules and patches supplied by either the application vendor or the operating system vendors. The training and production systems are self-explanatory. These three systems, using a stepped approach, give us an opportunity to provide a measure of safety with software upgrades and user error before either affects the production systems.
There is one AIX database, which is accessed via an application called CMHC. We also have nine SQL databases residing on our three database systems accessed by applications such as Mas500, Abra, and eRequester. We also utilize Active Directory and Microsoft Exchange for our domain and email services respectively.

References:

Date, C. J. An Introduction to Database Systems, Eighth Edition, Addison Wesley, 2003.

Galindo, J., Ed. Handbook on Fuzzy Information Processing in Databases. Hershey, PA: Information Science Reference (an imprint of Idea Group Inc.), 2008.

http://www.databasesystems.info/

Database Fundamentals

Student Number: 200202650
School of Engineering & Computing
Course: HNC Business Information Technology
Module: Database Fundamentals
Module Reference: CSH1025
Tutor: Adrian Stevart
Date: Monday, 21 June 2004

CONTENTS

| |ix |
|User Requirements |1 |
|Normalization |1 |
|Forms and there uses |1 |
|Data Entry Procedures |1 |
|Data Validation |2 |
|Query Based Enquiry |2 |
|Data Dictionary |2 |
|Automation |2 |
|Creating a Switchboard |2 > 3 |
|Conclusion |4 |
| | |
|Appendices |5 > 51 |
| | |
|Bibliography |52 |

1. 1. User Requirements
??? Database to be used by recruitment company called Finders
??? Finders work for a number of external clients
??? All types of employment
??? Agency get a fee for each vacancy filled
??? Upon instruction by client, Finders place advert in suitable publication
??? Details of applicants, jobs, clients, publication details to be stored on database
??? System must be created so that Mail merge can be used to automate interview letters
??? Reports and Queries to be used to interrogate the database

To achieve all the objectives outlined within the Database scenario and to be able to manipulate the data and to present it in such a way as to be useful for decision-making, a structured process needs to be adhered to. Normalization is a data technique that attempts to capture all the system needs to store and to organize the data into an efficient structure, and one that doesn??™t fall down when operational. With the Finders Employment agency in mind this will enable the data to be organized in such a way that can then be applied to creating a Database that works efficiently. This process can start from a list of seemingly disorganized data items collected during the fact-finding stages of analysis. After normalization has been applied and grouped data within logical groups (entities), this then creates a data model, as seen in [1.1] on Appendix A.

??? First normal form demands that each entity has an identifying key that is unique only to that entity. For this purpose I have created a key field, as seen in each and every entity at the end of the attributes list underlined, as seen in [1.2].

??? Normalization provides an algorithm for reducing complex data structures into irreducible simple structures. The data that the system needs to store data is organized into its minimal form. Therefore name and address within the APPLICANTS entity and similarly address within the CLIENTS entity have been broken down further. Please see end of respective attributes list for amendments within [1.2].

Please see Appendix B for refined Entity Relationship Diagram.

2. Data validation puts into place further safeguards that ensure that the data input maintains its integrity. It checks that the data is sensible before processing it. An example of a validation rule that could be applied to Finders Employment Agency??™s database is a range check, whereupon the data within a form falls between a specified range of values. For example the month of a persons date of birth falls between 1 and 12.

Forms??¦??¦??¦..what are they good for

Forms are excellent and user-friendlier for data entry than tables, for example. Forms feel more comfortable. Forms also have facilities for data filtering, automation, and validation beyond that possible using tables or queries alone.

Within the Finders database all forms were linked to an underlying table. Therefore all data within the form is written to and read from the bound table. Each and every form was created by using the AutoForm Wizard that generates a form by using the data within a table. A wizard speeds up the process of creating a form because it does all the basic work for you. You are still able to amend and add features within the design view.

Please refer to Appendix C for example of a Data Input Form (filtered job & filtered client form).

Adding Features to increase speed and accuracy
An Input Mask is another tool that ensures that the data inserted within the database is consistent and maintains the integrity of the data by fitting in to the pre-defined format. This process is undertaken within the design stage of a table/form by defining the format of a field. This is very essential for the database when it comes to sorting and/or executing queries. I created an Input Mask within the Applicant table and specifically the Date of Birth field. Please refer to [1.3] for example of Input Mask for Date of Birth. I covered every variation of applicant Post Codes by using the following Input Mask: – Aa90 9AA

Predefined display formats were also applied for the currency; this again was to ensure that the data entries were consistent. I did this by setting the display format within the design window, and the bottom part of the window and the Validation Rule box. This can be viewed job details form and the ???Salary PA??™ field. Now all data will be returned in the same format, irrespective of what actually gets typed into the field.

3. Please see Appendix D for Associate Printouts.

4. How is QBE used and its relationship to SQL
QBE stands for Query by Grid. It is a front-end interface that allows a user to interact through using an icon-based system, and fits comfortably with people already familiar with Windows based Operating Systems. It enables a user to create complex queries simply by first selecting the range of tables and then by placing various fields relating to the query within the grid. The user is then able to restrict or sort the query within the criteria or the sort field. The query can be manipulated by using the icons supplied on the toolbars.

For all intense and purposes, SQL is the language that interrogates the database by using a set of instructions. This is conducted out of sight of the user who just sees that the query has returned the information courtesy of the information in the form of the resulting record/s. It is done this way because QBG is considered more user friendly.

Please refer to Appendix E for Mail-merged letters for the short-listed candidates for job 147.

5. Data Dictionary

Please refer to Appendix F for limited Data Dictionary for the proposed solution.

6. Automate some database tasks
Macro??™s help automate common tasks. A macro is a set of one or more actions that each perform a particular operation, such as opening a form or printing a report. For the benefit of the Finders database I created a Macro for the Switchboard, where the pressing of one command button instigated when a user clicks a command button. This was achieved by following the instructions of the Control Wizard within the toolbox within Design View.

7. Creating a Switchboard Menu System
1. On the Tools menu, point to Add-ins, and then click Switchboard Manager.

2. If Microsoft Access asks if youd like to create a switchboard, click Yes.

3. In the Switchboard Manager dialog box, click Edit.

4. In the Edit Switchboard Page dialog box, type a name for the switchboard in the Switchboard Name box, and then click New.

5. In the Edit Switchboard Item dialog box, type the text for the first switchboard button in the Text box, and then click a command in the Command box. For example, type Review Products in the Text box, and then click Open Form In Edit Mode in the Command box.

6. Depending on which command you click, Microsoft Access displays another box below the Command box. Click an item in this box, if necessary. For example, if you clicked Open Form In Edit Mode in the Command box in step 5, click the name of the form you want to open in the Form box, such as Review Products, and then click OK.

7. Repeat steps 4 through 6 until youve added all the items to the switchboard. If you want to edit or delete an item, click the item in the Items On This Switchboard box, and then click Edit or Delete. If you want to rearrange items, click the item in the box, and then click Move Up or Move Down.

8. Click Close.

Please refer to [1.4] for screen dump illustrating Switchboard created within Finders Database.

8. Please refer to Appendix G for additional tasks (results of test data).

9. Conclusion of Report
To conclude, I have found my level of understanding to be that much better after interacting with the relational database and by gaining an insight into its mechanism by completing the tasks asked of me. My one and only criticism of the software that I felt impeded the database and in particular the solution to the user requirements was that within Access itself there are some software glitches that would need to be rectified. I recall within the report wizard the information that was shown on screen didn??™t reflect the information that was viewed when printed out.

Entity Relationship Diagram for Finders Employment Agency

Appendices

Appendix A

Unnormalised ERD of proposed basic system for Finders Employment Agency

|Name |Job Title |Organisation |Publication |Employment Type |
|Address |Salary PA |Contact | |Payment Period |
|Post Code |Manager |Contacts Position | |Commission Percent |
|Home Telephone Number |Department |Address | | |
|Business Telephone Number |Probation month |Post Code | | |
|E-mail address |Fee months |Telephone Number | | |
|Date of Birth |Interview Date |Discount Percent | | |
|Organisation |Interview Time | | | |
|Current Salary |Appointment Date | | | |
|Period of notice | | | | |
|Qualification | | | | |
|CV Received | | | | |
|Short List | | | | |
|Reference Received | | | | |
|Date entered | | | | |

Normalized ERD of proposed basic system for Finders Employment Agency

|Ref Number |Job Ref Number |Client Ref Number |Publication Ref Number |MOW Ref Number |
|Title |MOW Ref Number * |Address 1 | | |
|Forename | |Address 2 | | |
|Surname | |Address 3 | | |
|Address 1 | | | | |
|Address 2 | | | | |
|Address 3 | | | | |
|Job Ref Number * | | | | |

Appendix B

Appendix C

Appendix D

Appendix E
Appendix F
Appendix G
Bibliography

Britton, C and Doake, J (1993) Software System Development: A Gentle Introduction. ??“ (McGraw-Hill International Series in Software Engineering)
2.

———————–

Client Details

Client Reference No
Organisation
Contact
Contacts Position
Address 1
Address 2
Address 3
Post Code
Telephone No
Discount Percent

Job Details

Job Reference No
Job Title
Salary PA
Manager
Department
Client Ref No
Probationary month
Mode of Employment ref
Fee months
Interview Date
Interview Time
Appointment Date

Applicant??™s details

Ref Number
Title
Forename
Surname
Address 1
Address 2
Address 3
Post Code
Home Phone No
Business Phone No
E-mail address
DOB
Organisation
Current Employment
Present Salary
Period of notice
Qualification
Adverts Seen Ref
CV Received
Short list
Reference Received
Date entered
Job reference number

1

1

Publication Details

Pub Ref No
Publication

MOW Details

MOW Ref No
Employment Type
Payment Period
Commission Percent

.

.

APPLICANT

JOBS

CLIENTS

PUBLICATION

MOW

Database Configuration

Database configurations can be quite confusing in a healthcare facility. This is mostly because there are so many types of databases and each is designed for a specific purpose. A given organization may have many different database configurations because the organization needs systems that can perform the many different functions required for operations on a daily basis.

Transactional databases work best for day to day functions. Multiple users can access these databases at the same time. Transactional databases typically perform functions such as admissions and discharges, accounting, inventory and others necessary functions They store current data for operations.

Analytical databases are used for calculations. They do not store data, but instead access data stored in other areas. Few users access these databases typically for reports and analysis.

Distributed databases are data that is stored on several different computers. This is used by facilities with multiple branches or locations. The distributed database allows for the storage of only the necessary data for the branch with access to the data stored at the other locations if necessary.

External databases are just that, databases that are off-site. These are used for research, marketing, and data storage. Examples of this are sales lists that are purchased from companies, or back-up storage for imaging data for a radiology department.

A single organization would want to have several different databases in order to achieve daily, monthly, and yearly functions. These different databases allow for the many types of users to use the data without interfering with each other.

These different database configurations can be very beneficial for an organization when used correctly. It makes perfect sense to have each type of database because each one is designed to accomplish the specific tasks that are necessary for an organization to carry out business operations. The value of this is that all the components of the system are optimized for their specific purpose. Essentially the components for each system can be customized so that operations run smoothly and do not interfere with each other.
References:
Merida Johns. 2002. Information Management for Health Professions: Delmar

Data Warehousing

Data Warehousing
Data warehouse is a repository of an organizations electronically stored data. Data warehouses are designed to facilitate reporting and analysis[1].
This definition of the data warehouse focuses on data storage. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.
In contrast to data warehouses are operational databases that support day-to-day transaction processing.
In the past, it was very difficult for managers or executives to get {draw:frame} information about their company as a whole. This is still challenging today for companies that dont use data {draw:frame} warehouses. When a company uses a number of different systems, the {draw:frame} information they retrieve can be inconsistent. Data {draw:frame} warehouses are useful because they collect data and remodel it. The {draw:frame} information is placed in a single unit, and the company can get a clear picture of how their company is performing. Most importantly, they will be able to make decisions with a great deal of confidence. Data will be stored in the warehouse from multiple sources. Once the data is stored, it must be cleaned and transformed.
Figure: Data Warehousing Environment
Understanding The Challenges of Using Data Warehouses
While data {draw:frame} warehouses can be greatly beneficial to the companies that use them, there are many challenges that a company will face in their implementation and utilization. Some experts have even said that data warehouses are one of most overrated tools in the computer industry.
It is also important for {draw:frame} companies to realize that data {draw:frame} warehouses are not core business tools. What this means is that a {draw:frame} data warehouse is much more vulernable to the politics that may occur within {draw:frame} a company or organization. If the {draw:frame} data warehouse does not have the support of the employees, it will fail. Many employees have a hard time using data {draw:frame} warehouses because of their complexity, and the {draw:frame} companies they work for will often make the situation worse by failing to educate them. It is also challenging for {draw:frame} companies to keep their data {draw:frame} warehouses in tune with their production units. To make matters worse, many of their developers are not trained in calibrating them.
Thus instead of being scared, {draw:frame} a company must be aware of the pitfalls involved with using a {draw:frame} data warehouse.
Benefits of data warehousing
Some of the benefits that a data warehouse provides are as follows: [7][8]
A data warehouse provides a common data model for all data of interest regardless of the datas source. This makes it easier to report and analyze information than it would be if multiple data models were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.
Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.
Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.
Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.
Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.
Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.
{text:bookmark-start} {text:bookmark-end} Disadvantages of data warehouses
There are also disadvantages to using a data warehouse. Some of them are:
Data warehouses are not the optimal environment for unstructured data.
Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency) in data warehouse data.
Over their life, data warehouses can have high costs. The data warehouse is usually not static. Maintenance costs are high.
Data warehouses can get outdated relatively quickly. There is a cost of delivering suboptimal information to the organization.
There is often a fine line between data warehouses and operational systems. Duplicate, expensive functionality may be developed. Or, functionality may be developed in the data warehouse that, in retrospect, should have been developed in the operational systems and vice versa.
Evolution in organization use of data warehouses
Organizations generally start off with relatively simple use of data warehousing. Over time, more sophisticated use of data warehousing evolves. The following general stages of use of the data warehouse can be distinguished:
Off line Operational Database
Data warehouses in this initial stage are developed by simply copying the data off an operational system to another server where the processing load of reporting against the copied data does not impact the operational systems performance.
Off line Data Warehouse
Data warehouses at this stage are updated from data in the operational systems on a regular basis and the data warehouse data is stored in a data structure designed to facilitate reporting.
Real Time Data Warehouse
Data warehouses at this stage are updated every time an operational system performs a transaction (e.g. an order or a delivery or a booking.)
Integrated Data Warehouse
Data warehouses at this stage are updated every time an operational system performs a transaction. The data warehouses then generate transactions that are passed back into the operational systems.
Sample Applications
Some of the applications data warehousing can be used for are:
Credit card churn analysis
Insurance fraud analysis
Call record analysis
Logistics management.
{text:bookmark-start} {text:bookmark-end} The future of data warehousing
Data warehousing, like any technology niche, has a history of innovations that did not receive market acceptance.[9]
A 2009 Gartner Group paper predicted these developments in business intelligence/data warehousing market .[10]
Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets.
By 2012, business units will control at least 40 percent of the total budget for business intelligence.
By 2010, 20 percent of organizations will have an industry-specific analytic application delivered via software as a service as a standard component of their business intelligence portfolio.
In 2009, collaborative decision making will emerge as a new product category that combines social software with business intelligence platform capabilities.
By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups).
Conclusion
References
=> http://www.erpwire.com/erp-articles/data-warehousing-issues.htm (Advantages )
=> http://en.wikipedia.org/wiki/Data_warehouse
=> http://www.exforsys.com/tutorials/data-warehousing/why-data-warehouses-can-be-useful.html
=>http://data-warehouses.net/architecture/

Data Table Analysis

Running head: KUDLER FINE FOODS

Kudler Fine Foods: Data Table Analysis
Colleen Barbee
University of Phoenix
May 3, 2010

Abstract

Data tables are used to arrange data in such a way to represent or communicate data analysis. Data tables can vary in structure, flexibility, notation, representation and use. This brief will evaluate the design elements of data tables from an accounting perspective for Kudler Fine Foods management. The following also provides for Kudler Fine Foods entity relationship diagrams and recommendations on improvements to the data tables. Attached is a pivot table using the general ledger inventory data of Kudler Fine Foods, in order to explain that this type of table will improve decision making for management.

Tables are an HTML structure designed to present information in tabular format. Table markup has several components and attributes that can be used to identify the elements in a standard data table: column headings, row headings, caption, and content summary. When these elements are used properly, a data table can be understood by both visual and nonvisual users. Design elements attribute to the effectiveness of a data table.
Design Elements of Data Tables
Data tables are used as a way to systematically show data in convenient useful formats that represent information. There are design elements to be taken into consideration in creating data tables. Events, both economic and business impact an organizations financial statements and value.

References

Apollo Group, Inc. (2006). Riordan Manufacturing. Finance & accounting – overview. Retrieved March 28, 2007. CIS/319 – Computers and Information Processing. https://ecampus.phoenix.edu/secure/aapd/CIST/VOP/Business/Riordan/Finance/RioFandA001.htm
http://en.wikipedia.org/wiki/Business_Technology_Management
Teixeila, Ron (2007). Small Business Trends. Top Five Small Business Internet Security Threats. http://smallbiztrends.com/2007/06/top-five-small-business-internet-security-threats.html

Data Table Analysis

Data Table Analysis
Mary Carter
University of Phoenix
Accounting Information Systems
ACC 542
Michael Wells
December 12, 2010

Data Table Analysis
Kathy Kudler established Kudler Fine Foods In 1998 in La Jolla. At the time and with only one location, Kathy opted to employ Microsoft Access for tracking inventory, sales, orders, employees, and customers. Inventory must be carefully tracked because the products are perishable. If the informatin is out dated, incomplete, or inaccurate, it will have no value. The use of pivot tables and entity relationship diagrams allows Kathy Kudler to prepare better accounting reports and improve her decision making processes.
Design Elements
The inventory tables enables the end user to determine what inventory is available and when to order more. Data table analysis is vital for this process. The table is designed to organize the data into department, financial codes, products, and transaction totals. The data is organized according to each company department and location by the general ledger codes. ?  These codes differentiate by series per location, department, and item; for example, the first two digits (12) represent the La Jolla Company location, the next two digits (10) represent the bakery department, and the (00) represents the inventory item Rustic Baguette. ?  All of the inventory is broken down into different departments according to the general ledger (GL) code. ?  Proper analysis of the data table increases the visual presentation and information necessary for the financial planning. The end user can view the table and be able to identify the necessary information that the received product is recorded in the correct department, the quantity, and cost for a specified period. The total amount and quantity on hand for a product is used to calculate the average price the item. The total quantity of the inventory is difficult to determine based on the current inventory tables.
Entity Relationship Diagram
Entity relationship (E-R) diagrams are used to illustrate the hierachy of how data is reviewed. These diagrams represent the relationship between business entities. There are four symbols used for E-R Diagrams:
Table 1: Entity Relationship (E-R) Diagram Symbols
Symbol | Representation |
Rectangles | Business entity |
Diamonds | Relationship |
Ovals | Entity Characteristics |
Connecting Lines | Depicts Relationships |

The key entity is Kudler Fine Foods followed by each location ??“ La Jolla, Del Mar, and Encinitas. Each store has a unique GL codes for each inventory item. This is for the purposes of tracking sales transactions. The day to day accounting transactions occurring at Kudler Fine Foods are sales, purchases, sale receipts, and payments. ? 
Recommendations for Improvements to the Data Tables
A Pivot Table is a two dimensional statistical summary of database information. Databases can be improved by designing the accounting codes chronologically and sorting the data by product inventory levels. This method permits the tracking of inventory for departments that offer different products. By sorting the data, end users can more efficiently find the information needed for more effective decisions.
Benefits Through the Use of Pivot Tables
Kudler??™s current inventory system is overloaded with a large amount of detailed information. Data tables are supposed to make it easier to visualize information for making decisions efficiently and effectively. Pivot tables can keep track of the inventory and inventory movement for the company and store the information in one centralized location. Kathy Kudler must be able to easily identify the inventory balance for each store and which items need to be re-ordered. Pivot tables will allow Kathy Kudler to quickly summarize the inventory data from a spreadsheet with up to date totals.
Conclusion
Kathy Kudler can improve her current inventory database through the use of pivot tables and entity relationship diagrams. These improvements will allow Kathy Kudler to maintain a more up to date database that is easier visibility for more efficient and effective decision making for each location as well as for the entire company. The pivot tables will also allow for fewer inventory tables that will reduce redunacy. Kathy Kudler??™s database needs to go through a normalization process. Each location has the same entities and events. All the end users will benefit from the pivot tables.

References
Apollo Group, Inc (2005). Kudler Fine Foods. Department/Section.
Retrieved November 26, 2010.
Bagranoff, N.A., Simkin, M.G., & Strand Norman, C. (2008). Core
concepts of accounting information systems (10th ed). New York: Wiley & Sons.
O??™Brien, J.A. & Marakas, G.M. (2008). Management information
systems (8th ed). New York: McGraw-Hill.

Data Table Analysis-Kudler Fine Foods

Running Head: Data Table Analysis

Mirjana Elez
June 7, 2010
ACCT/542
University Of Phoenix

Data Table Analysis
In this paper, I will evaluate the design elements of the data tables from an accounting perspective, create an entity relationships diagram illustration the existing data tables, and recommend improvements to the data tables. I will also create a pivot table using Kudler??™s general ledger inventory data, and explain how the information in the pivot table may improve decision making for management for Kudler.
Looking at Kudler??™s inventory data table, one can easily read the table and be able to tell what the information is telling us. The data table is organized into GL Code, inventory item, summary line item, and amount. Someone who is reading this report will know that the first two numbers indicate the store location. For example, (12) is for La Jolla, (13) is for Del Mar, and (14) is Encitinitas. The table also lists the inventory items and breaks it down into departments. The last column in the table also sums the total amounts of items in each code. Kudler??™s inventory data is very easy to read and follow. It makes it easy to identify which items belong to each department and the amount of items in the inventory. The information helps determine sales by stores, the amount of inventory on hand, and overstocked items.
Entity-relationship (E-R) diagram is a graphical documentation technique used to depict the entities and their direct relationships. The model consists of four symbols: rectangles, diamonds, ovals, and connecting lines. Rectangles represent entities, diamonds describe the nature of relationships, ovals denote an entity??™s attributes, and connecting lines depict relationships (Bagranoff, Simkin, Strand, 2008). See table 1.1.
The main reason for Kudler??™s inventory table is to help Kudler with managing inventory. The Inventory Table contains the components that make up an Item. It is used for managing inventory and determining the availability of ingredients that go into prepared items, such as bakery products, etc. (University Of Phoenix, 2010). One way that Kudler can improve their inventory table is by adding the quantity and price columns. They can keep the total column. Adding these two columns will help them easily see how much exactly they have in their inventory and it will help when it comes to re-ordering. It would be easier to see where they stand and what items they need to push to get rid of. It would help Kudler determine if they are over or under stock levels. It would also help them see what items need to be discounted and moved.
Pivot tables are two-dimensional statistical summaries of database information. Pivot tables enables users to chose what type of summary information to display (e.g., total sales, average sales, or maximum sales), as well as to change an overall selection category (e.g., change the period in which to view sales data) (Bagranoff, Simkin, Strand, 2008). By looking at Kudler??™s inventory table as it is now, by creating pivot table, it can make it easier to read the table. Instead of memorizing the codes for each department, in a pivot table, Kudler can have a summary line item column, then list all the inventory items under the same category, the amounts, and then sum up the totals. For example, Kudler can have a Del Mar Bakery Department Merchandise Inventory, then list all items under that department (Agiago cheese, Calamata olive bread, Challah, etc), and the amounts. The pivot table would sum up all the amounts and give a total amount of inventory for Del Mar Bakery Department Merchandise Inventory. Kudler can also add an entity of Items sold which would easily show the relationship between inventory and sales. The system would automatically update the remaining of the inventory and compare it to target inventory every time the cashier process a sale. For example, the table would have GL#, Invoice #, items sold, in stock #, minimum inventory, and maximum inventory. Every manager would have an access so the managers can compare inventory levels. The pivot tables would help because the redundant information in the database would be eliminated.
Kudler has a high level of customer satisfaction, and in order to keep their customers satisfied, I have identified some improvements to their current inventory data table. I also explained how the information in the pivot table could improve decision making for Kudler??™s management. The proposed improvements would help Kudler by eliminating redundant information.

References:

Bagranoff, N.A., Simkin, M.G., & Strand Norman, C. (2008). Core Concepts of Accounting Information Systems (10th ed.). New York: Wiley & Sons.

University Of Phoenix. Kudler Fine Foods. Retrieved on June 7, 2010 from
https://ecampus.phoenix.edu/secure/aapd/cist/vop/business/Kudler/IT/KudlerITDatabases001.htm

Entity-Relationship (E-R) diagram for Kudler Fine Foods
Table 1.1
Resources

Equipment

Cash
Inventory

Events

Orders
Purchases
Sales

Agents
Resources
Events
Employees
Suppliers
Customers
Agents

Data Table Analysis for Kudler Fine Foods

Data Table Analysis for Kudler Fine Foods

In this brief, I will determine the design elements of data tables using accounting ideas to develop entity relationship diagrams illustrating the data tables already used and create entity relationship diagrams advising other alternatives to improve the data tables. I will also include a pivot table using the general ledger inventory data of Kudler, explaining how this information may improve the way management makes decisions for the company.
When observing Kudlers inventory data table, the table can easily be read and one can understand what the table is telling them. The structure of the data table consists of the GL Code, summary line item, inventory item, and the amount. Reading this report an individual will recognize that the first two numbers represent the store location. The tables also consist of inventory items, and the items are also broken down into the different departments. The last column lists the total sum amount of each code. The company??™s inventory data is not difficult to read and understand. The structure of the table makes it easy to recognize what items belong to which department and the amount of the inventory items. This information assist the stores in determining the sales made per store, the items in stock, and the amount of the inventory on hand.
An entity relationship diagram is an illustration representing graphical techniques describing the entities and their direct relationships. The diagram model includes four symbols: ovals, diamonds, rectangles, and connecting lines. Each of the symbols represents a description of what the entity stands for. For example, the diamond is a description of the relationships, rectangles represent entities, an oval signify the qualities of the entity, and the lines presents the connection of the relationships (Bagranoff, Simkin, Strand, 2008).
Kudlers inventory table is very important because it helps to maintain the inventories which, consist of the elements that make up an Item. These elements are used in managing the inventory and helping to recognize the availability of the ingredients included in the items, for products like bakery goods, etc. (University Of Phoenix, 2010). Kudler could make improvements in their inventory table by including a price column and a quantity column. The total column can remain. With the additional two columns this will assist them more efficiently in recognizing the exact amount of inventory before they have to reorder. It would be beneficial for them to know where they are and what they need to alleviate. Kudler would be able to know if what their stock levels are. For the discounted items or those that need to be moved this would be very helpful.
Improvements of the data and pivot tables will be a hugh help in rearranging codes to a certain particular order. Facilitating these improvements will make it more accessible in monitoring the different departments that has several items to sell. ???Separating the data will allow better visualization, understanding, and arrangement of the data needed to make better decisions??? (Birnbaum, 2003, p.89). Pivot tables are two dimensional summaries of statistical database information. This particular table allows the users the opportunity to select the type of information to exhibit for example, the maximum sales, total sales, average sales or even make changes to the selection categories (Bagranoff, Simkin, Strand, 2008). Reviewing the inventory table of Kudler as is, creating a pivot table may make it more understandable to read. Instead of trying to remember the coding for the different departments the pivot table could have a column for a summary line, next under the same category list all the inventory items, amounts, and last sum up the totals.
For instance, Kudler??™s Del Mar location may have their bakery department Merchandise Inventory, in which they can list some of the items in that department like cheeses, breads, etc. in addition to the amounts, and store location. The pivot table would total up the entire amounts and give a total sum amount of inventory for that locations bakery department Merchandise Inventory. The company can also include some entity items sold and can help recognize the relationships among the sales and inventory. Automatically the system will incorporate more current information regarding the inventory examining the similarities of the targeted inventory whenever a sales transaction is made. Using updated data tables and pivot table, Kudler will have the opportunity to recognize the variances of inventory they have left in the stores, what inventory items they need to order, and what needs to be relocated to storage.. The pivot table can be put to good use because with it Kudler will be able to make quick summaries about the data from a worksheet, finding totals, and average inventory counts of their items. The pivot table is also helpful because this would eliminate the redundancy of information in the database within the database. See Figure 2.
In concluding, Kudler Fine Foods is very extensive concerning their customer satisfaction, and in maintaining those satisfied customers, I have included some various recommendations that would assist Kudler with improving the current inventory data table. In addition I have given explanations of how the information in their pivot tables can help make better decisions for their management teams. So these proposed recommendations will assist Kudler in eliminating redundancy of information.

References
Bagranoff, N. A., Simkin, M. G., & Strand, N. C. (2008). Core Concepts of Accounting Information Systems (10th ed.). New York, NY: Wiley & Sons

Birnbaum, Duane, (2003) p.89, Microsoft Excel VBA professional projects, retrieved on January 12, 2011 from UOP online database.

University of Phoenix. Kudler Fine Foods. Retrieved on January 12, 2011 from website: https://ecampus.phoenix.edu/secure/aapd/cist/vop/business/Kudler/IT/KudlerITDatabases001.htm

Figure-2

|General Ledger Inventory Pivot Table | | |
| | | |
|Sum of Amount |?  |?  |
|Summary Line Item |Inventory Item |Total |
|Del Mar Bakery Department Merchandise Inventory |Agiago Cheese |816.48 |
|?  |Calamata Olive Bread |270.83 |
|?  |Challah |1210.68 |
|?  |Ciabatta |736.87 |
|?  |Danishs |255.05 |
|?  |Ficelle |417 |
|?  |Finnish Rye |2217.73 |
|?  |Focaccia |612.83 |
|?  |French Croissants |555.93 |
|?  |Fruit Croissants |873.05 |
|?  |Fruit Tarts |464.85 |
|?  |Honey Wheat |1599.38 |
|?  |Italian Sssemolina |319.36 |
|?  |Prussian Rye |266.57 |
|?  |Rustic Baguette |623.45 |
|?  |Sourdough Rounds and Batards |266.85 |
|?  |Swiss Muesli |1273.96 |
|Del Mar Bakery Department Merchandise Inventory Total |12780.87 |
|Del Mar Cheese & Dairy Merchandise Inventory |Aioli Sauce |268.03 |
|?  |Asiago |1473.45 |
|?  |Beurre Barratte de Celles sur Belle |376.28 |
|?  |Beurremont 83% Butter |278.38 |
|?  |Boursin |7935.99 |
|?  |Brie |362.71 |
|?  |Cabridoux |367.66 |
|?  |Camembert |264.59 |
|?  |Cheddar |2899.77 |
|?  |Chevre |1778.05 |
|?  |Clotted Cream |567.6 |
|?  |Danish Lurpak Butter |361.69 |
|?  |Dulce de Leche |252.4 |
|?  |Edam |616.32 |
|?  |Emmental |276.46 |
|?  |English Brandy Butter |1520.67 |
|?  |Feta |266.8 |
|?  |Fontina |851.76 |
|?  |Gorgonzola |707.75 |
|?  |Gouda |294.4 |
|?  |Grana Padano |437.43 |
|?  |Gruyere |460.56 |
|?  |Havarti |315.98 |
|?  |Jarlsberg |277.45 |
|?  |Limburger |745.91 |
|?  |Manchego |464.53 |
|?  |Mascarpone |304.85 |
|?  |Maytag |33475.13 |
|?  |Mozzarella |696.46 |
|?  |Parmigiano Reggiano |279.61 |
|?  |Pecorino Romano |513.33 |
|?  |Provolone |653.98 |
|?  |Raclette |253.34 |
|?  |Ricotta |853.87 |
|?  |Roquefort |331.71 |
|?  |Stilton |708.65 |
|Del Mar Cheese & Dairy Merchandise Inventory Total |?  |62493.55 |
|Del Mar Meat and Seafood Merchandise Inventory |Angus Beef |328.11 |
|?  |Beluga |1859.68 |
|?  |Caviar |557.58 |
|?  |Coho Salmon |843.65 |
|?  |Copper River King Salmon |312.41 |
|?  |Duck |1006.87 |
|?  |Dungeness Crab |595.88 |
|?  |Goose |298.32 |
|?  |Guinea Fowl |17200.46 |
|?  |Lobster |252.42 |
|?  |Oregon Sanddabs |343.51 |
|?  |Ostrich |440.95 |
|?  |Oysters |326.65 |
|?  |Pacific Swordfish |671.68 |
|?  |Partridge |277.92 |
|?  |Pheasant |251.57 |
|?  |Poussin |397.01 |
|?  |Quail |741.39 |
|?  |Red Snapper |2648.37 |
|?  |Sausages |267.9 |
|?  |Squab |269.56 |
|?  |Wild King Salmon |2217.22 |
|?  |Wild Turkey |524.71 |
|Del Mar Meat and Seafood Merchandise Inventory Total |32633.82 |
|Del Mar Produce Merchandise Inventory |Atemoya |442.34 |
|?  |Breadfruit |331.39 |
|?  |Canistel |1269.28 |
|?  |Cortland Apples |529.26 |
|?  |Empire Apples |1440.37 |
|?  |Feijoa |506.67 |
|?  |Fuji Apples |282.38 |
|?  |Gala Apples |941.87 |
|?  |Golden Delicious Applies |551.25 |
|?  |Gravenstein Apples |251.61 |
|?  |Litchi |8696.56 |
|?  |Longan |290 |
|?  |Mamey Sapote |476.36 |
|?  |McIntosh Apples |593.74 |
|?  |Northern Spy Apples |415.55 |
|?  |Pink Lady Apples |365.93 |
|?  |Rhode Island Grening Apples |6333.53 |
|Del Mar Produce Merchandise Inventory Total |?  |23718.09 |
|Del Mar Wine Merchandise Inventory |Blush Wines |395.1 |
|?  |Dessert Wines |748.4 |
|?  |Fruit Wines |303.39 |
|?  |Red Wines |304.81 |
|?  |Rice Wines |399.16 |
|?  |Sparkling Wines |384.17 |
|?  |White Wines |117030.98 |
|Del Mar Wine Merchandise Inventory Total |?  |119566.01 |
|Encinitas Bakery Department Merchandise Inventory |Agiago Cheese |561.14 |
|?  |Calamata Olive Bread |362.66 |
|?  |Challah |473.86 |
|?  |Ciabatta |478.83 |
|?  |Danishs |278.53 |
|?  |Ficelle |950.16 |
|?  |Finnish Rye |349.38 |
|?  |Focaccia |335.08 |
|?  |French Croissants |2517 |
|?  |Fruit Croissants |352.62 |
|?  |Fruit Tarts |286.07 |
|?  |Honey Wheat |1235.08 |
|?  |Italian Sssemolina |775.23 |
|?  |Prussian Rye |332.16 |
|?  |Rustic Baguette |511.72 |
|?  |Sourdough Rounds and Batards |363.85 |
|?  |Swiss Muesli |386.7 |
|Encinitas Bakery Department Merchandise Inventory Total |10550.07 |
|Encinitas Cheese & Dairy Merchandise Inventory |Aioli Sauce |863.25 |
|?  |Asiago |9772.47 |
|?  |Beurre Barratte de Celles sur Belle |334.08 |
|?  |Beurremont 83% Butter |335.11 |
|?  |Boursin |418.42 |
|?  |Brie |311.81 |
|?  |Cabridoux |638.82 |
|?  |Camembert |3139.56 |
|?  |Cheddar |984.71 |
|?  |Chevre |6493.36 |
|?  |Clotted Cream |1634.89 |
|?  |Danish Lurpak Butter |541.86 |
|?  |Dulce de Leche |1289.39 |
|?  |Edam |267.53 |
|?  |Emmental |979.79 |
|?  |English Brandy Butter |5365.83 |
|?  |Feta |252.68 |
|?  |Fontina |261.15 |
|?  |Gorgonzola |832.11 |
|?  |Gouda |310.68 |
|?  |Grana Padano |254.57 |
|?  |Gruyere |412.28 |
|?  |Havarti |1775.41 |
|?  |Jarlsberg |1062.24 |
|?  |Limburger |267.6 |
|?  |Manchego |490.35 |
|?  |Mascarpone |2485.93 |
|?  |Maytag |3972.44 |
|?  |Mozzarella |440.93 |
|?  |Parmigiano Reggiano |412.23 |
|?  |Pecorino Romano |348.55 |
|?  |Provolone |338.12 |
|?  |Raclette |288.19 |
|?  |Ricotta |306.13 |
|?  |Roquefort |322.62 |
|?  |Stilton |308.87 |
|Encinitas Cheese & Dairy Merchandise Inventory Total |48513.96 |
|Encinitas Meat and Seafood Merchandise Inventory |Angus Beef |1665.2 |
|?  |Beluga |252.94 |
|?  |Caviar |250.61 |
|?  |Coho Salmon |423.49 |
|?  |Copper River King Salmon |348.63 |
|?  |Duck |5041.08 |
|?  |Dungeness Crab |1113.25 |
|?  |Goose |252.16 |
|?  |Guinea Fowl |5031.94 |
|?  |Lobster |589.1 |
|?  |Oregon Sanddabs |285.16 |
|?  |Ostrich |413.75 |
|?  |Oysters |897.36 |
|?  |Pacific Swordfish |1896.75 |
|?  |Partridge |529.24 |
|?  |Pheasant |635.77 |
|?  |Poussin |513.12 |
|?  |Quail |365.92 |
|?  |Red Snapper |458.53 |
|?  |Sausages |311.86 |
|?  |Squab |2471.77 |
|?  |Wild King Salmon |1343.44 |
|?  |Wild Turkey |1253.82 |
|Encinitas Meat and Seafood Merchandise Inventory Total |26344.89 |
|Encinitas Produce Merchandise Inventory |Atemoya |921.44 |
|?  |Breadfruit |4951.69 |
|?  |Canistel |528.16 |
|?  |Cortland Apples |423.26 |
|?  |Empire Apples |284.55 |
|?  |Feijoa |541.19 |
|?  |Fuji Apples |1524.09 |
|?  |Gala Apples |758.18 |
|?  |Golden Delicious Applies |580.05 |
|?  |Gravenstein Apples |2944.58 |
|?  |Litchi |266.08 |
|?  |Longan |827.25 |
|?  |Mamey Sapote |877.22 |
|?  |McIntosh Apples |501.02 |
|?  |Northern Spy Apples |1050.7 |
|?  |Pink Lady Apples |424.07 |
|?  |Rhode Island Grening Apples |3457.13 |
|Encinitas Produce Merchandise Inventory Total |?  |20860.66 |
|Encinitas Wine Merchandise Inventory |Blush Wines |306.17 |
|?  |Dessert Wines |353.8 |
|?  |Fruit Wines |329.36 |
|?  |Red Wines |363.9 |
|?  |Rice Wines |456.29 |
|?  |Sparkling Wines |373.71 |
|?  |White Wines |255.3 |
|Encinitas Wine Merchandise Inventory Total |?  |2438.53 |
|La Jolla Bakery Department Merchandise Inventory |Agiago Cheese |1061.63 |
|?  |Calamata Olive Bread |452.67 |
|?  |Challah |254.02 |
|?  |Ciabatta |392.99 |
|?  |Danishs |534.07 |
|?  |Ficelle |558.43 |
|?  |Finnish Rye |945.27 |
|?  |Focaccia |4463.93 |
|?  |French Croissants |290.64 |
|?  |Fruit Croissants |427.24 |
|?  |Fruit Tarts |932.86 |
|?  |Honey Wheat |3116.01 |
|?  |Italian Semolina |697.06 |
|?  |Prussian Rye |6937.42 |
|?  |Rustic Baguette |283.2 |
|?  |Sourdough Rounds and Batards |727.92 |
|?  |Swiss Muesli |401.61 |
|La Jolla Bakery Department Merchandise Inventory Total |22476.97 |
|La Jolla Cheese & Dairy Merchandise Inventory |Aioli Sauce |261.69 |
|?  |Asiago |265.58 |
|?  |Beurre Barratte de Celles sur Belle |288.12 |
|?  |Beurremont 83% Butter |823.82 |
|?  |Boursin |423.91 |
|?  |Brie |1019.83 |
|?  |Cabridoux |540.44 |
|?  |Camembert |368.82 |
|?  |Cheddar |566.9 |
|?  |Chevre |576.65 |
|?  |Clotted Cream |625.71 |
|?  |Danish Lurpak Butter |273.89 |
|?  |Dulce de Leche |2456.18 |
|?  |Edam |511.42 |
|?  |Emmental |307.86 |
|?  |English Brandy Butter |315.09 |
|?  |Feta |1446.62 |
|?  |Fontina |564.24 |
|?  |Gorgonzola |385.18 |
|?  |Gouda |740.97 |
|?  |Grana Padano |607.78 |
|?  |Gruyere |310.16 |
|?  |Havarti |287.86 |
|?  |Jarlsberg |262.21 |
|?  |Limburger |296.34 |
|?  |Manchego |634.94 |
|?  |Mascarpone |336.56 |
|?  |Maytag |314 |
|?  |Mozzarella |489.27 |
|?  |Parmigiano Reggiano |494.5 |
|?  |Pecorino Romano |778.7 |
|?  |Provolone |335.21 |
|?  |Raclette |361.97 |
|?  |Ricotta |935.11 |
|?  |Roquefort |700.46 |
|?  |Stilton |251.08 |
|La Jolla Cheese & Dairy Merchandise Inventory Total |?  |20159.07 |
|La Jolla Meat and Seafood Merchandise Inventory |Angus Beef |2770.92 |
|?  |Beluga |573.66 |
|?  |Caviar |263.04 |
|?  |Coho Salmon |1049.8 |
|?  |Copper River King Salmon |509.69 |
|?  |Duck |1088.09 |
|?  |Dungeness Crab |330.15 |
|?  |Goose |350.81 |
|?  |Guinea Fowl |307.2 |
|?  |Lobster |370.72 |
|?  |Oregon Sanddabs |372.8 |
|?  |Ostrich |504.35 |
|?  |Oysters |353.35 |
|?  |Pacific Swordfish |798.2 |
|?  |Partridge |262.25 |
|?  |Pheasant |567.73 |
|?  |Poussin |889.17 |
|?  |Quail |322.08 |
|?  |Red Snapper |302.09 |
|?  |Sausages |2210.92 |
|?  |Squab |258.75 |
|?  |Wild King Salmon |352.24 |
|?  |Wild Turkey |404.35 |
|La Jolla Meat and Seafood Merchandise Inventory Total |15212.36 |
|La Jolla Produce Merchandise Inventory |Atemoya |631.5 |
|?  |Breadfruit |315.63 |
|?  |Canistel |250.74 |
|?  |Cortland Apples |526.1 |
|?  |Empire Apples |271.09 |
|?  |Feijoa |402.17 |
|?  |Fuji Apples |1341.05 |
|?  |Gala Apples |303.32 |
|?  |Golden Delicious Applies |428.4 |
|?  |Gravenstein Apples |612.82 |
|?  |Litchi |263.54 |
|?  |Longan |293.93 |
|?  |Mamey Sapote |278.22 |
|?  |McIntosh Apples |348.44 |
|?  |Northern Spy Apples |491.06 |
|?  |Pink Lady Apples |1039.07 |
|?  |Rhode Island Grening Apples |360.16 |
|La Jolla Produce Merchandise Inventory Total |?  |8157.24 |
|La Jolla Wine Merchandise Inventory |Blush Wines |282.64 |
|?  |Dessert Wines |310.83 |
|?  |Fruit Wines |547.9 |
|?  |Red Wines |495.27 |
|?  |Rice Wines |400.01 |
|?  |Sparkling Wines |544.77 |
|?  |White Wines |512.49 |
|La Jolla Wine Merchandise Inventory Total |?  |3093.91 |
|Grand Total |?  |429000 |

Data Protection Act

DATA PROTECTION FACTSHEET

Data protection covers how information about living identifiable persons is used

The Data Protection Act ??™98 strikes a balance between the rights of individuals and the sometimes competing interests of those with legitamite reasons for using personnel information

8 Principles of good practise (processing information)

1 Fairly and lawfully gained
2 Processed for limited purposes
3 Adequate, relevant and not excessive
4 Accurate and up to date
5 Not kept longer than necessary
6 Processed in accordance with individuals rights
7 Secure
8 Not transferred to countries outside the EU

Rights Under The Act
There are seven rights under the Data Protection Act

1 The right to subject access
This allows people to find out what information is held about them on computer and within some manual records
2 The right to prevent processing
Anyone can ask a data controller not to process information relating to him or her that causes substantial unwarranted damage to them or anyone else

3 The right to prevent processing for direct marketing
Anyone can ask a data controller not to process information relating to him or her for direct marketing purposes
4 Rights in relation to automated decision-taking
Individuals have a right to object to decisions made only by automatic means e.g. there is no human involvement
5 The right to compensation
An individual can claim compensation from data controller for damage or distress caused by any breach of the act. Compensation for distress alone can only be claimed in limited circumstances
6 The right to rectification, blocking, erasure and destruction
Individuals can apply to the court to order a data controller to rectify, block or destroy personal detail if they are inaccurate or contain expressions of opinion based on inaccurate information
7 The right to ask the Commissioner to access whether the Act has been contravened
If someone believes their personal information has not been processed in accordance with the DPA, they can ask the Commissioner to make an assessment. If the act is found to have been breached and the matter cannot be settled informally, then an enforcement notice may be served on the data controller in question.

References
Data protection act facts
www.aimhigher.com

Data Modelling

Product Development
Ihort was given the opportunity to develop and market a new type of Voice Recognition Device (VRD) that has advanced capabilities for controlling computer applications based on natural language or spoken command. The over all purpose and objective of this report is to provide feedback and broader view of the market in which our firm was operating and our performance within the market while developing and marketing the VRD.
It was extremely important for our firm to understand the market, our competitors, the targeted group and the external environment to help us do a better job and maximize our profits. It was prudent for our firm to undertake some vital steps before fully getting into the market place. Some of the steps we had to work through were:

??? Assess ??“ identify and clarify primary market strategic objectives and align those objectives with the target market.
??? Plan and Design – Design the product to fit customer needs for ultimate success.
??? Enable – Train sales managers in the methodologies best suited to the firms goals, maximize profits
??? Sustain and grow – Empower our target market, keep the clients long-term and ultimately grow as a firm and maybe capture other markets

We started with the intention of specifically cornering the modern student market. This market was selected due to its constant thirst for new technology; furthermore it was viewed as a market that could make use of the product given the products substantial attributes and functions. The marketing department looked at several survey reports available and assessed some of the needs and wants of this target market and it was clear that students were easy to please with a few changes to the underdeveloped form of the VRD. This target market comprised of college students who could not afford a high price for the product hence it was within our thinking that we would provide most of the basic elements required for the product while maintaining a low price in the market

Because of the long term plans within the organization, in the initial round we wanted to reserve some money for later use and marketed our product too conservatively. This caused us to perform poorly in round 1 and all other consecutive rounds. Some of the failures within our organization could be attributed to failure to advertise aggressively and execute our firm??™s objectives firmly. We were under the impression that this was a large market segment that did not need a lot of advertising and that most of the advertising could be done through word of mouth and customer experience, this did not turn out to be the case. Secondly we were not aggressive enough with pricing, as much as it was known that our target market was the students, we failed to capture those within the segment willing to pay more for the product. We should have set the price slightly higher in the first round and advertised more.

In the third round we changed the product in accordance with an old report up in the hopes of regaining market share and possibly run the business profitably, this turned out to be a mistake because our competitors continued to grow within our target market while gaining ground in their market segments as well. By the fourth and fifth rounds we were so far behind there wasnt much we could do to get back into business firmly or establish our selves in a more profitable way. As mentioned above our strategy was to corner students, but too much of our market was going to Talk2Me and Dude Inc. We underestimated our competitors and once we realized our mistakes it was difficult to regain the confidence of the lost customers. We were never able to successfully push our competitors out of the student market despite our product being better customized to fit the modern students. Numerous actions were put in place to revive our firm??™s objectives and execute them firmly but we lacked the funding to establish ourselves in the market again. We are not sure if our initial mistake was as a result of the low price charged in hopes of gaining more sales or lack of advertising in the initial rounds.

We are more knowledgeable about how the market operates and stand in a better position to execute firm plans given another opportunity. Some of the lessons learned include:

-Low pricing does not always mean high sales volume and profitability
-Advertising ultimately pays off and helps increase market share.
-Be aggressive when a product is just introduced to the market to make the firms presence known
-It??™s ok to take risks and take up the giant instead of being conservative.
-Despite the best intentions, the market does not always do what you expect.