Warehouse A Backbone Business Intelligence â€Myassignmenthelp.Com

Question: Discuss About The Warehouse A Backbone Business Intelligence? Answer: Introducation Data warehouse is the process by which the organization Mudrock University will change over its raw information into meaningful data that can be represented in various forms in view of its detailing capacities. It must attain better, aggregated, merged vast volumes of information that is amassed in multidimensional information structure to help multidimensional data form. It perceives the requirement for present as well as future information, yet in addition perceives the requirement for recorded information. It enables a business association to control a lot of information in meaningful form. (Al-Debei, M., M., 2011). The main issue in data warehouse is the data quality which is the integral part to Mudrock University. The data quality must be analyzed before proceeding for data warehouse. By having the knowledge about key dimensions for improving data quality, data warehouse for Mudrock University can be defined effectively. In order to be processable as well as interpretable in a viable and proficient way, information needs to fulfill the value criteria. Various Measurements of information quality normally incorporate accuracy, reliability, importance, consistency, precision, timeliness, understandability, brevity and its usability. The main key measurements which needs to be satisfied are as following: Completeness - This key dimension helps to manage all the accessible essential data. Any important data must not be missing. Consistency: The data which must be arranged in data warehouse must be consistent. Legitimacy: It alludes to the accuracy and sensibility of information and must be fulfilled while arranging data. Accuracy - The accurate data must represent the real world values as inaccurate data can affect operational as well as investigative applications. Integrity -Data must not miss any relationship. If data is not properly linked it can have duplicate records. (Pandey, 2014). Data Security Issue Mudrock University must concentrate on the security challenges while implementing data warehouse. Requires the security framework that guarantees that the entire staff must be able to access data applicable to their own particular division, but IT administration department can access the entire information. The organization's data warehouse centers stores the employee as well as student's personal data. Protection laws may represent the utilization of such individual data. The adherence to these security laws must be actualized in the data warehouse. In spite of database, data distribution center must comprises of many more. The whole condition ranges from the extraction of information from operational framework, exchange of this information to the data distribution center, the dispersion of this information to information marts as well as other expository servers, and at last the moving this information to end-clients. The whole network traverses numerous servers and numerous product items and each segment must be secure. (Oracle White Paper, 2005). Granularity for Data Warehouse Granularity is to check that how much details of the data will be required for future purpose. In the given scenario, Offering_Coordinator, Enrollment will be detailed Fact tables whereas Aggregate Fact tables will be Student, Staff_mamber, Unit. Here, Course, Student, Staff_member, Unit will be Dimensional tables as all the information related to Course, Student, Staff, Unit will be stored in their respective table, but Offering Coordinator will be detailed table as it will store all transactional details regarding Units offered to which Staff member. Aggregate Fact tables can have large number of data whereas number of students, units, staff will be limited. While designing these entities, data warehouse use surrogate key or primary key. For making data base in Third Normal Form, proper relationship and functional dependencies needs to be implemented. A Foreign key will be set that will establish the relationship between aggregate tables and fact tables. The fact tables will record all the information of units offered to staff member, student enrolled in which unit etc. Constraints will signify join relationships between fact and dimension tables with primary key on the dimension tables and foreign keys on the fact tables. Another type of a logical relationship in a dimensional model is a hierarchy which expresses rollup or aggregation relationship within the columns of a dimension table. Also, surrogate keys as well as operational keys will be used in Dimensional tables. (Hobbs, L. Hillson, S., 2005). Star Schema COURSE (CourseCode, Version, CourseName) UNIT (UnitCode, Version, UnitName) UNIT_OFFERING (OfferingNumber, UnitCode, Year, TeachingPeriod) OFFERING_COORDINATOR (StaffID, UnitOfferingNumber) STUDENT (StudentID, StudentName, DateOfBirth) SCHOOL(SchoolCode, SchoolTitle) ENROLMENT (EnrolNumber, StudentID, UnitOfferingNumber, Grade, CourseCode, SchoolCode) STAFF_MEMBER(StaffNumber, StaffName, SchoolCode) References Al-Debei, M., M. (2011). Data Warehouse as a Backbone for Business Intelligence: Issues and Challenges. European Journal of Economics, Finance and Administrative Sciences ISSN 1450-2275 Issue 33 (2011). Pandey, R., K. (2014). Data Quality in Data warehouse: problems and solution. IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 1, Ver. IV (Jan. 2014), PP 18-24. Retrieved from - https://www.iosrjournals.org/iosr-jce/papers/Vol16-issue1/Version-4/D016141824.pdf?id=8572 Security and the Data Warehouse. Oracle White Paper (2005). Retrieved from - https://www.oracle.com/technetwork/middleware/bi-foundation/twp-bi-dw-security-10gr1-0405-128087.pdf Hobbs, L. Hillson, S. (2005). Oracle 10g Data Warehousing. Elsevier Digital Press. Retrieved from - https://books.google.co.in/books?id=EzpC7cuOGqUCpg=PA178lpg=PA178dq=appropriate+level+of+granularity+for+your+data+warehousesource=blots=m3hYf39KfQsig=WgwIIez1CBXxv9-ddY9ySJS0w4whl=ensa=Xved=0ahUKEwjgw4LYvL_WAhUeR48KHeLCC-wQ6AEIQTAE#v=onepageq=%20granularity%20f=false