Business Intelligence and Analytics System

Question: Discuss about the Business Intelligence and Analytics System. Answer: Introduction Analysis of data quality is necessary for getting a clear overview of the different features of the data such as accuracy, completeness, update status, relevance, consistency across sources of data, reliability, presentation appropriateness and accessibility. First part of the current assignment provides a brief mind map of the problem that is the result of brainstorming. Scope and justification of the project that explain primary objective of the analysis is also presented in this assignment. A consistent description of the data has been provided in a data dictionary, which has been adjusted and copied from NSW data dictionary. A summary has also been provided for exploring the dataset. Analysis plan is also provided as a justification. The second part of the assignment provides two annotated bibliography that forms the basis of the discussions and brainstorming. Data construction has also been presented along with the result of exploration of the data. Finally, the data quality has been analyzed for the dataset in the current assignment. Project justification and scope Performance of the emergency departments (ED) has decreased by approximately 45% in New South Wales Productivity Committee. Patients at ED often stay for long due to improper handling of the database and improper maintenance of the data. Often the officials of the New South Wales Productivity does not maintain the excel database and hence the monitoring of the patients stay at the emergency department is not tracked properly. Due to poor data entry by the4 staffs, often-wrong arrival time and departure time are entered and the triage number is written improperly. Wrong management of the patients arrival and departure causes patients stay back in the ED for longer time that causes risk of overcrowding of the healthcare. Overcrowding within the clinic might lead to inferior outcomes such as mortality. When a patient requires priority checkup often the patient does not receive one and leads to the patient dying in the healthcare. Quality of treatment is another issue that arises due to overcrowding of the clinic as it causes healthcare officials to get busy managing and dealing with a huge crowd and they end up in improper management of the crowd priority and inputs wrong triage number. Timeliness of therapy is another serious issue, which occurs because of over crowdedness as doctors, nurses and healthcare professionals find it difficult to treat each patient with precision and care. The current project will analyze the database, which has been handled by New South Wales Productivity Department and look into the improvement scope of the datasets for performance of the emergency department especially in terms of patients long stay at the ED. diag1_commonwealth The person identifier column contains the set of data that links the data present in the row to a specific person and hence can be referred to as the patient ID. A patient can simply refer to the New South Wales Productivity Committee with their person identifier and they can trace them in their database. Departure date is inserted as date format and highlights the date the patient leaves the emergency department. Departure time denotes the time of departure followed by the actual departure time that contains the date along with time. Arrival date contains the date of patient arrival followed by arrival time that denotes the time of patient arriving at the emergency department. A duration column has been added that signifies the staying time of the patient at the emergency department in minutes. Mode of separation describes the status of the person at separation from the New South Wales Productivity Committee and denotes the place to which the person has been released. Ed visit type identifies the number allocated for each presentation in the department of agency on a regular basis. Triage category provides the emergency priority of the patient and presenting problem shows the problems suffered by the patient. Finally, the diagnosis codeset contains the information about the diagnosis done by the doctor and professionals. The data analysis will be done by following business understanding that will lead to proper data understanding. Data will be prepared based on the consecutive business as well as the data understanding. After preparing the data, modeling will be done and it will be evaluated. Through evaluation, it will lead to future scope of better business understanding. Finally, it will be deployed to understand and mitigate issues within New South Wales Productivity Committee. Bibliography Corrales, D.C., Ledezma, A. and Corrales, J.C., 2015. A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal.JCP,10(6), pp.396-405. The journal provides about the data quality determination in discovery tasks based on knowledge. A conceptual framework has been provided that is used as a guideline for our project. Shafique, U. and Qaiser, H., 2014. A comparative study of data mining process models (KDD, CRISP-DM and SEMMA).Int. J. Innov. Sci. Res,12(1), pp.217-222.