1ITECH7406- Business Intelligence and Data Warehousing Research Report Group Assignment Sem1-2019 ...

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1ITECH7406- Business Intelligence and Data Warehousing Research Report Group Assignment Sem1-2019  Overview For this assignment, the students will work in team and create a written report that will eview the applications of business intelligence analytics and Data Mining in different industry domains in decision making contexts. The purpose of this assessment is to enable students to understand how business intelligence analytics and Data Mining techniques revolutionize businesses today.  Timelines and Expectations Percentage Value of Team Report: 15% Percentage Value of Team Presentation: 10% Group Presentation Due Date - Week 08 –Timetabled Tutorial Group Report Due Date – Week 09 (Sun, May 19, XXXXXXXXXX:00)  Assignment Details  Background Business analytics and Data Mining techniques can help organizations make sense of -- and gain a competitive advantage from -- all the data that they have in their systems. Business analytics includes “decision management, content analytics, planning and forecasting, discovery and exploration, business intelligence, predictive analytics, data and content management, stream computing, data warehousing, information integration and governance” (IBM, 2013, p. 4).  There are different types of business intelligence analytics that an organization can take advantage of, including predictive analytics, text analytics and text mining, sentiment analysis, customer analytics and business intelligence data mining. Data Mining is the process of analyzing large data-sets to identify trends and patterns in the data. The data can be generated through different sources such as social media, websites, transactions, mobile devices, sensors, etc. The information extracted from this data helps organizations to derive their real business value and generate new business opportunities.  In the light of above information write a 3000 words research report on specific business analytics and Data Mining techniques applications that derive business value and generate new business opportunities in any of the following three (3) industry verticals. Illustrate the impact of these techniques on businesses with examples of application from the chosen domain. 2Choose only any THREE (3) domains from the following list:  1. Transportation industry – in this domain the business analytics help stakeholders in making effective decision in Traffic control, route planning, intelligent transport systems and congestion management (by predicting traffic conditions). Also, could e useful for route planning to save on fuel and time, for travel aangements in tourism etc. revenue management, technological enhancements, logistics and for competitive advantage (by consolidating shipments and optimizing freight movement), etc. 2. Banking industry - Data mining techniques can be used to detect financial fraud, including credit card fraud, corporate fraud and money laundering. 3. Health Care industry - Health care applications include discovery of patterns in adiological images, analysis of microaay (gene-chip) experimental data to cluster genes. Moreover, chronic disease states and high-risk patients can be tracked. 4. Manufacturing industry - Large volumes of data from the manufacturing industry are untapped. The underutilization of this information prevents improved quality of products, energy efficiency, reliability, and better profit margins. Business analytics can be used in solving today’s manufacturing challenges and to gain competitive advantage among other benefits. 5. Education industry - Major challenge in the education industry is to incorporate ig data from different sources and vendors and to utilize it. Business analytics can e used to measure teacher’s effectiveness, overall progress of a student over time and effectiveness of cuiculum, etc. 6. Customer Relationship Management - Data mining and analytics provides efficient tools to analyze customer data for the purpose of decision-making. Moreover, data mining aids analysis of buying patterns, determination of marketing strategies, segmentation of customers, stores or products. Requirements In this assignment, you will be required to form teams of approximately three (3) people. The findings of your research study will be presented through: • A group research report • Interactive presentation. 3The report The report will take the form of a well-researched academic report of approximately 3000 words. Diagrams or tables are encouraged to be used to support your statements. The eport should be well supported with appropriate references from reliable sources. You should include academic journals, books, theses, trade magazines and well-respected sources of related Internet materials that you find relevant. Please note – Wikipedia is NOT considered a reliable source to quote in an academic document of this type, without ackup from other well reputed sources. Your report should present as a collective effort, not a series of submissions by various team members. It is expected to FLOW as one document. Each team member’s contribution should be clearly identified in the report, with a notation about which section he/she wrote about. Table of contents, reference list and contribution statements do not count towards the final words count. All reports must use the APA referencing styleThe Presentation Duration: 20 minutes for each team For the presentation component of this assessment, your team will focus on the following: 1. Business analytics and Data Mining techniques specific to the chosen domains 2. Illustration of applications of the above business analytics and Data Mining techniques within the chosen domains 3. Explanation on how these specific business analytics and Data Mining techniques added business value and generated new business opportunities within the chosen domains 4. Any Challenges that associated with the application of the above business analytics and Data Mining techniques for the chosen domains 4Submission Submit your report as either a word or PDF document via Moodle.  Marking Criteria/Ruic Refer to the attached marking guide.  Feedback Feedback will be supplied through Moodle. Authoritative marks will be published through FdlGrades.  Plagiarism: Plagiarism is the presentation of the expressed thought or work of another person as though it is one's own without properly acknowledging that person. You must not allow other students to copy your work and must take care to safeguard against this happening. More information about the plagiarism policy and procedure for the university can be found at http:federation.edu.au/students/learning-and- study/online-help-with/plagiarism. http:federation.edu.au/students/learning-and-study/online-help-with/plagiarismhttp:federation.edu.au/students/learning-and-study/online-help-with/plagiarism5ITECH7406- Business Intelligence and Data Warehousing Research Report Marking Guide Sem3-2018   Criteria Marks Significance of Business analytics and Data Mining applications for the chosen industry areas 5 Research findings on Business analytics and Data Mining techniques specific to the chosen domains 10 Discussion on how business analytics and Data Mining techniques added business value and generated new business opportunities within the chosen domains 15 Report challenges that associated with the application of the usiness analytics and Data Mining techniques in the chosen domains 5 Introduction and Conclusion - An interesting, well written summary of the main points. For conclusion, an excellent final comment on the subject, based on the information provided. 5 References - Coect referencing (APA). All quoted material in quotes and acknowledged. All paraphrased material acknowledged. Coectly set out reference list. 5 Report Presentation Style – Spelling &grammar, length, originality, eport layout , (Points will be deducted for exceedingly long or short eports. 5 Total /50 Marks /15 General Comments: 6ITECH7406- Business Intelligence and Data Warehousing Research Presentation Marking Guide Sem3-2018 Criteria Marks  Introduction - chosen industries and significance of analytics for the them10 Industry specific Business analytics and Data Mining techniques /10 Applications of the business analytics and Data Mining techniques that added business value and generated new business opportunities within the chosen domains  15 Challenges that associated with the application of the above business analytics and Data Mining techniques for the chosen domains5 Conclusion - an excellent final comment on the subject based on the esearch findings.  5 Presentation Style e.g. layout, clarity, engagement /5 Total /50  Marks  10 General Comments:

Ahmedali · Accepted Answer

Business Analysis & Data Mining
Business Analysis & Data Mining 
Business Analysis & Data Mining		Banking, Healthcare, and Transportation		4/29/2019			
Table of Contents
Introduction	2
Business Analysis & Data Mining in Banking	2
Applications	2
Benefits & Added Value	3
Challenges	4
Business Analysis & Data Mining in Healthcare	4
Applications	4
Benefits & Added Value	6
Challenges	7
Business Analysis & Data Mining in Transportation	7
Applications	7
Benefits & Added Value	9
Challenges	10
Recommendations & Conclusion	10
References	11
Introduction 
Business Intelligence (BI) is an umbrella of technologies and concepts that make use of the data sets and provide the outputs after analysing these abundant sets of data. The information provided is actionable and assists the business organizations in decision-making and problem-solving activities. The end-users, that is, the employees of a business firm can make informed decisions on the basis of these information sets. 
Business Analysis and Data Mining are two such technological concepts under the domain of BI. Business Analysis under BI is the process in which business data and information is analysed to determine associated trends and patterns. There are automated tools that are available for the purpose of data and business analytics. Data Mining is the technique that provides the companies with the ability to analyse abundant sets of data for the purpose of pattern recognition. These recognized patterns and trends are then used to determine the associated business decisions. These technologies are used in numerous business sectors. Three such business sectors and industries include Banking, Healthcare, and Transportation. 
Business Analysis & Data Mining in Banking 
Applications
There is a lot of competition in every industry sector. Banking is one such industry that has a lot of competition and there are new entities that are entering the market. 
Business Analytics and Data Mining techniques are being used by the banking firms to make correct business decisions. Customer retention has become a primary cause of concern for the banking firms in the present time. This is because of the increase in the competition in the industry. It has become necessary for the banking executives to be aware of the customer demands and expectations to allow them to remain engaged with a particular bank. Therefore, customer relationship management and customer retention is the primary application of business analytics and data mining in the banking sector (Hk, 2017). There are data mining techniques, such as clustering is used in the process. K-means is the distance-based clustering algorithm which is used to partition the data sets in to a number of clusters. For instance, the demand of banking and financial services and applications vary from customer to customer. There are groups of customers that utilize these services for personal savings and assistance. There are also professional entities that are highly interested in investments and profits. The use of the clustering algorithm can segment the customers in the different groups and categories and determines their choices and preferences. The services provided to the customers are accordingly regulated (Whitelock, 2018). 
Applications of Data Mining in Banking Sector (Hassani, Huang & Silva, 2018)
The number of frauds and cybersecurity issues are being increasing at a rapid pace in the banking industry. Some of the areas that are impacted by these frauds include credit card services, internet banking, ATM transactions, loans, and likewise. It has become the primary need of the banking firms to put a check on these frauds and malicious activities. Business analytics and data mining techniques are used in this area. The banks use data warehousing technique to tap the data sets of the third-party. The business analytics tools and data mining algorithms are then applied to understand the patterns and trends associated with the frauds. Bank’s own internal information is also used and analysed to determine the patterns and trends associated with the fraud (Priyadarshini, 2017). It is not sufficient to detect the frauds that may be associated with the banking sector. It is also necessary to control and prevent the frauds that may come up. The fraud prevention activities are carried out by applying the data mining techniques, such as decision tree and logistics regression. The technique with the best results is applied to prevent and treat the fraudulent measures. 
Benefits & Added Value 
There are numerous benefits that have been provided with the use of the technologies in the banking sector. Also, business value is added with the utilization of these techniques. 
It is necessary for the banking firms to be aware of the customer requirements and expectations in order to offer them with the desired set of services and solutions. The in-depth analysis of the customer demands and associated trends can be conducted with the aid of the data mining techniques and business analysis tools. This has resulted in the enhancement of the overall customer engagement and satisfaction rates. 
There are numerous services and solutions that are offered by the banking firms to the customers (Pavlovic, Reljic & Jacimovic, 2014). Some of these service lines may provide higher revenues while there may be certain streams that may suffer from losses. It is necessary for the banking firms to maintain dynamic approach in terms of these service lines to make sure that the profitable services are included in the revenue stream. The overall revenues and profits for the banks improve as an outcome. 
It is essential for every business firm to market its services and solutions correctly. The use of data mining and business analytics techniques can be done to understand the market trends and patterns and the execution of the marketing strategies is done accordingly. There are targeted online marketing campaigns that can be conducted on the basis of the information available. There is increased use of social media by the banking firms and the application of the analytical tools and data mining techniques on the data sets can provide enhanced profits. 
Challenges
There are also certain challenges that have been observed with the increase in the use of business intelligence technologies in the banking sector. The performance of the business analysis and data mining tools rely completely on the data sets that are collected by the banking firms. There are scenarios wherein the banking executives succeed in collecting large volumes of data. However, the data sets are unreliable and invalid that does not provide the determined results. This leads to the performance issues and challenges (Goodman, 2011).

1 ITECH7406- Business Intelligence and Data Warehousing Research Report Group Assignment Sem1-2019 Overview For this assignment, the students will work in team and create a written report that will...

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