IT : Silent Cycles - Case Study Example

IT Case Study Report: Silent Cycles s of Learning: s IT Case Study Report: Silent Cycles 1. Information gathering techniques Data collection for research and decision making processes for an organization is an important task. Validity of data collected depends on the technique used. Valid data collected enables decision-making managers perform accurate and logical analysis. There are various data collection techniques that Silent Cycles can use to determine the best data base design for its products and services as well as staff and clients. Some of data collection techniques are; written survey, telephone survey, face-to-face survey, Key Informant Interviews, group interviews, and Focus Group Discussions. a. Written survey These are questionnaires sent to respondents via mails, fax and returned through the same medium. Advantages Interviewer resources are not required. Questions are prepared before time considering the level of knowledge of the population the survey is to be taken. It is confidential when it comes to personal matters, therefore respondents can answer comfortably. Effective when quantitative information is needed because it can be set as a random survey. Disadvantages Only the literate are able to respond to the questionnaire. If the questions are not well defined the response will be poor because there is no guidance If the survey is based on computer the respondents will have to be able to access computers Enough time must be given for the respondents to take time to answer the questionnaire. b. Telephone survey Is a survey where questions and answers are given via the telephone. Advantages Shortest time is spent to get the results Through the phone the interviewer can get several responses from a given area and is at liberty to get as many answers as possible from a given area Questions are prepared before time so quantity research is done so as to get qualitative answers. It’s an efficient process especially when an emergency arises and a survey is needed to be carried out. Disadvantages Very few telephone numbers can be found on time Interviewers may fail to get the attitude to which the interviewee responds with to the questions This type of survey is limited to those who have telephones and their contacts are recorded. c. Face-to-face survey This is a type of survey where the interviewer meets the interviewee with a written question are and asks questions orally. Advantages It efficient where detailed information is needed. It can be plotted for all both the literate and illiterate to understand. Can be used to those who cannot be accessed over the network. Questions must be written for all to understand so as to get qualitative data. Disadvantages It requires movement through communities so as to reach the intended population. Best when the interviewers have a good knowledge of the research they are going to carry out. Inefficient where the interviewees cannot access phones and are spread out geographically. Interviewees should be well trained in order to be reliable data. d. Key Informant Interviews This survey targets the leaders of a community who have a clear background about the community are interviewed to give information about the needs and concerns of the community. Advantages It’s private because only one person is met at a time. Because the knowledgeable people are interviewed. It allows sharing of rich information about the history and trend. Questions that people are cannot answer in public are asked and the leaders can answer because they are alone with the interviewer (Hoffer, Prescott & McFadden, 2005). Disadvantages The right people must be selected in order to get reliable data A large number of people cannot be met at a go. Information cannot be given then expected to reflect the whole community. It’s not appropriate if the interviewer needs a quantitative data. The interviewer may be bias in selecting the informants. 2. Database Security Issues Security of databases is based on various key issues: availability, authenticity, integrity and confidentiality. a. Availability The required data has to be available whenever it is needed. Those accessing the data must be authorised to access it. There should be track on who accessed the database and the data that was accessed. b. Authenticity All data must be edited by the only authorized sources There must be verification that the requests presented to the database are from authorised users The users accessing the data should be confirmed not fake c. Integrity The data should be going through the right process in the institution Any external data must be verified and should have the correct formatting. All the accurate data that is input should be accurate and can be able to be verified. All data that has changes should be known whoever changed it and whoever authorised it to be changed so as there can be privacy in the institution information. d. Confidentiality Data should only be available to the authorised people Data must be secured both internally and externally to ensure that the database is secure. Whoever accesses data should be reported and whatever they did with the data. Sensitive data should have high security instead of losing institutional litigation (Chen, 1976). There are ways in which to keep the data confidential so as to ensure data is not leaked. Leakages may arise through: internal data loss, external hacking, securing data if hardware is stolen, and unapproved administrator access. To ensure security there can be the middleware security where there is a middleware between the user and the data that is to be accessed and allowing different passwords to access the data concerning the institution in different systems. It is also advisable to use the third party security options who are security vendors that will ensure no unauthorised person accesses the data or by buying a fake data set to be used for data masking (Batra & Wishart, 2004). The third party security option is advantageous because the fake data set is to be used by the design and implementation of teams, it also allows data to be scrambled if intrusion occurs, it can be able to look for odd data and poor access commands, it also arrests data being exited from the system (Bernstein, 1976). Though the third party has its advantages, it also has its demerits like being very expensive to buy the fake data, it adds options for other performance issues, it needs a specific a specific process to set the fake data up, it does not protect the data in its warehouse. Another protection measure for database security is creation of a built in database protection. This is responsible for: password controls, data to be accessed basing on the profiles and the roles of the accessor, can monitor who accessed what data and secures logging (Batra Hoffer & Bostrom, 1990). Order stock, Customer sale, schedule service, register club member 3. Level 1 Diagram 4. a). Use of Normalisation Technique Normalisation technique is used to analyse data when designing a database system. The designer is able to know the data structures within the organisation and any changes that may occur to the system. The steps on how to normalise data from a client invoice are The source to which the data is is selected then converted into unnormalised table (UNF). The unnormalised data is transformed into a first normal form (1NF). The data in the first normal is again transformed into a second normal form (2NF) The data in the second normal form is then transformed into a third normal form (3NF) If the data is still subject to anomalies in the third normal form then further transformations are performed. The third normal form is transformed into Boyce-Codd normal form (BCNF) The Boyce-Codd normal form is transformed into fourth normal form (4NF). The fourth normal form is then transformed into fifth normal form (5NF) (Batra & Anthony, 1994) Then from the tables that have been normalised a data model is produced. Case Study: Silent Cycles Step One: Unnormalized table Client Invoice Silent Cycles: Southampton Branch 345 High Road Southampton SO456JJ Client Number: 1001 Invoice Number: 405 Client Name: Sam Cooper Invoice Date: 02/01/2014 Client Address: 100 Points Staff Number: 210 Portsmouth PO155HK Staff Name: Martin Lawrence Item Description   Quantity Unit_price Discount Total B100   Ladies 26inc Raleigh   1 850.00 85.00 765.00 ST-SP   RX Helmet 1  32.00 3.20 28.80 CC-02   CC cycle glasses   2 18.00 36.00                                                                     Invoice Total         829.80 Step 2: The First Normal Form entails no repetition of groups The table has only two dimensions containing client name, staff name description and total price Step 3: Second Normal Form: AT this stage, redundant data is eliminated and the multiple unit price for each description is indicated above the table. This means that the total value column os not functionally dependent on item column. Step 3: Third Normal Form: The eliminated data does not depend on key. b). Advantages and Disadvantages for normalisation technique in relation to the traditional Top-Down (ERD) Technique Top-down (ERD) technique focuses on the knowledge of higher-level constructs like collection of things and types of entities. Its adoption requires that high-level requirements are started with. The requirements ensure that the process of identifying things needed to represent the data and their attributes are collected. The data analysis tries to develop conceptual data model by identifying data objects within the domain. Through the process, the analyst develops domain ontology. Techniques used are observations, interviews, and other data collection methods. This technique utilizes diagrammatic approaches like the conceptual data models (Avison & Fitzgerald, 2002). On the other hand, the bottom-up approaches in data design ensures that the proceeds come from an initial analysis of low-level conceptual units to acceptable logical data model achieved by logical groupings of related attributes. In other words, this technique views population identification process as a process used to generalize object identity. For example; the input for this technique could be the views of data or report or any pattern that concur with the values of the attributes. The common approach for this technique is the normalisation approach. This approach is also infused with top-down approaches like the ER diagrams. To address potential deficiencies, in a relational schema with different levels in norm form, relations are defined. This approach minimizes redundancy and dependency. 5. The Benefits of Database Solution will bring to Silent Cycles DBMS has several advantages over the traditional methods of computer file processing approach. DBA must consider the following benefits during designing of databases, coordinating and monitoring of the DBMS. a. Control data redundancy since all data is integrated into a single database but not duplicated. For example; in the dean’s file, the faculty payroll file has several identical files. When the files are converted into a database, a single database is developed and multiple copies of the same data are converted to single-copy (Codd, 1970). b. Improve data consistency: Once data redundancy is controlled, data consistency is achieved. This implies that if an update is to e done, it is done once and made available to all users. c. Sharing of data: Data can be shared by authorized users within the organization. The data is managed and the users are issued with the rights to access information simultaneously. d. Retrieval of data is made easier since a single database contains several tables with relationships created between the tables. e. Consistency rules are easily applied to database to ensure that relevant data is keyed into the database. Reference List Avison, D. E. and Fitzgerald, G. (2002). Information Systems Development: Methodologies, Techniques and Tools, 3rd Ed., London, UK: McGraw Hill. Batra, D., Hoffer, J. A., and Bostrom, R. P. (1990). Comparing representations with relational and EER models, Communications of the ACM, 33 (2), 126-139. Batra, D. and Wishart, N. A. (2004). Comparing a rule-based approach with a pattern-based approach at different levels of complexity of conceptual data modelling tasks, International Journal of Human-Computer Studies, 46, pp. 397-419. Batra, D., & Anthony, S. R. (1994) Novice errors in database design. European Journal of Information Systems, 3 (1), 57-69. Bernstein, P.A. (1976). Synthesizing third normal form relations from functional dependencies. ACM Transactions Database Systems, 1(4), 277-298. Chen, P. P. (1976). The entity-relationship model—toward a unified view of data. ACM Transactions on Database Systems, 1 (1), 9-36. Codd, E. F. (1970). A relational model of data for large relational databases. Communications of the ACM, 13 (June), 377-387. Hoffer, J. A., Prescott, M. B., and McFadden, F. R. (2005). Modern database management, 7th Ed., Prentice-Hall, Upper Saddle River, NJ. Jarvenpaa, S. L. and Machesky, J. J. (1989). Data analysis and learning: an experimental study of data modelling tools, International Journal of Man-Machine Studies, 31, 367-391. Kung, H. and Tung, H. (2006). An Alternative Approach to Teaching Database Normalization: A Simple Algorithm and an Interactive e-Learning Tool, Journal of Information Systems Education, 17(3), 315-324 Read More