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Business Analytics Assignment On Data Mining


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.

Choose 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 be useful for route planning to save on fuel and time, for travel arrangements 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 radiological images, analysis of microarray (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 under utilization 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 big data from different sources and vendors and to utilize it. Business analytics can be used to measure teacher’s effectiveness, overall progress of a student over time and effectiveness of curriculum, 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.


Business analytics can be defined as a set of skills and intelligence which is used by an individual with the help of technologies to explore, investigate and evaluate the past and present business performance and develop future business plans depending on the same. García, Luengo & Herrera (2015) whose work is referred in this business analytics assignment stated that some of the common types of business analytics that are currently being used by the organisations include descriptive analytics, affinity grouping, clustering and predictive analytics. However the process of business analytics or intelligence is incomplete without the process of data mining. García, Luengo & Herrera (2015) stated that data mining is the simple process used the companies to collect data from different sources and summarise the same to form relevant business information that will help in taking strategic business decisions. This business analytics assignment will focus on evaluation of the significance of data mining and business analytics and intelligence on the three different industries namely education, banking and healthcare sector.

Significance of business analytics and data mining applications for chosen industry areas
Dutt e al. (2015) whose work is referred in this business analytics assignment stated that in order to keep pace with the changing global scenarios and also to become competitive every industry is focusing on adoption of data mining and business intelligence techniques because these techniques are useful for development of strategies. The significance of business intelligence and data mining applications in education, banking and healthcare sector will be discussed as follows:

Education sector: Educational data mining (EDM) is important because the tools and statistical techniques are used by the educational departments to procure information from educational records about the students, online logs and examination results in order to reach different conclusions depending on which the future learning strategies of the institutes are framed. Further Roiger (2017) stated that EDM also helps in using the large scale data collected from the educational settings to develop education delivery processes which are easy and better for the students to understand. For instance with the help of EDM processes as discussed in this business analytics assignment, the educational institutes gets the opportunity of assessing the learning behaviour of the students depending on the data collected from the student’s participation rates in school activities, number of chat messages highlighting the level of communication from the student’s end and also the study pattern of the students by analysing their grades and the types of projects being submitted by them. The use of business analytics and data mining in the context of education is also increasingly becoming common. Schubert et al. (2015) stated that the major processes that are used are similar including classification, association and clustering. These major processes however can be differentiated in terms of the education industry by means of the fact that the major technical methods used involve Bayesian Classification, Partitioning Methods and Impressive Association.

Banking sector: In the banking sector, data mining and analytics is concerned with collection of personal data like account details, transaction details, analyse the transaction trends, collect purchase histories and can use these information in order to prevent any kind of fraud taking place in banking transactions. Goswami et al. (2018) opined that use of data mining tools helps in reduction in the level of bank frauds. Further with the help of the transaction data, the banks are able to identity the borrowers who make regular repayment of loan and interest and distinguish them from the individuals who fail to do so. Further data mining tools in banks helps in determining the financial feasibility of an individual to pay loan and thus the bank can take the decision in relation with giving of loan to the individual. Ashouri et al. (2018) stated that there are two types of analytical algorithms which are used as data mining techniques in the banking sector. These are known as the supervised functions and the unsupervised functions. The major process or techniques used under the supervised functions involve Generalized Linear Models, Minimum Description Length, Naïve Bayes and Support Vector Machine. Ward & Hobson (2018) further stated that some of the the major processes or techniques that are used under the unsupervised functions involve the methods of Apriori, k-means, Non-negative Factor Matrix Factorization and One Class Support Vector Machine. The simpler modes of the technical jargons used for the data mining and analytics processes that have been mentioned involve the processes of classification and regression, attribute importance, association, clustering, feature extraction and anomaly detection.

Healthcare sector: Similar to the above industries referred in this business analytics assignment, use of data mining within the healthcare industry helps n improving the satisfaction rates of the patients by providing patient centred care and also helps in decrease of the additional operational costs by identifying the operations which are unnecessary in nature. Masters (2018) opined that in order to increase the patient satisfaction rate, data mining tools generally provides information collected from the personal files and data base of the patients which were submitted during the admission, to the nurses and the staffs so that by making positive interactions and engaging into some of the favourite pastimes that will improve the mood of the patients while their stay in the hospitals. The reach of business analytics and data mining has surpassed the barriers of not being applicable and different strategies and techniques are being applied in this industry for predictive results. The major methods that are used for these activities include Descriptive data-mining and Predictive mining model. The process of Descriptive Data-Mining and Predictive Mining Model are used for different purposes and under different circumstances. According to Aggarwal (2015) these major processes however can be differentiated in terms of the education industry by means of the fact that the major technical methods used involve Bayesian Classification, Partitioning Methods and Impressive Association. The reach of business analytics and data mining has surpassed the barriers of not being applicable and different strategies and techniques are being applied in this industry for predictive results. Gandomi & Haider. (2015) stated that the major methods that are used for these activities include Descriptive data-mining and Predictive mining model. Dutt, Ismail & Herawan (2017) opined that the process of Descriptive Data-Mining and Predictive Mining Model are used for different purposes and under different circumstances within the healthcare sector.

Research findings on business analytics and data mining applications for chosen industry
An application of data mining and analytical tools in different industries shows that the industries are using the tools for their benefits and for adding value to the business. The following research findings on the applications of data mining tools in the three selected industries in this assignment on business analytics will highlight the beneficial factors that are being derived by these industries:

Education sector: The illustration of the application of the data mining techniques in the education sector is still an area of research. However KS & Kamath (2017) stated that in studies that the major uses of data mining involve managing and monitoring student performance and student involvement areas. Data mining has greatly helps in maintaining records of productivity of the students and to increase it in a calculated manner. It has also helped in allocation of work and activities and responsibilities amongst the faculty involved in an educational institutions. Thus the division of work ensures that the students education quality and education facilities are maintained effectively.

Banking sector: Shmueli et al. (2017) stated that one of the most important methods of retaining and creating new customer bases is that of the use of data analytics for the same process. Further the data mining method named the classification method helps the banks in understanding the level of risk associated with a particular customer and thus helps the bank in deciding whether to retain the customer or not. Apart from this Sammut & Webb (2017) stated that fraud detection and prevention are some of the major concerns of the banking customers and the banking authorities. With the help of data mining applications, these instances can be prevented. Credit card approval and supervised learning methods for the banking personnel are also developed with the help of data mining tools. With the help of predictive model and descriptive model, the banking authorities are able to eliminate the real time fraud and thereby increase the confidence of the customers on the banking management. Hassani et al. (2016) stated that with the help of clustering process, the banks are able to combine the transactions of the individual with same type of behaviour and nature of transactions. The use of prediction tools helps in predicting the instances of fraud. Here the money is considered to be the independent variable and the fraudster is considered to be a dependent variable. Gandomi & Haider (2015) stated that banking management based on the availability of the historical data are able to develop a regression curve showing the number of frauds attempted in one particular transaction process and can develop firewalls which will help in prevention of the fraud activities.

Healthcare sector: The various methods in the healthcare sector that use data analytics and data mining include knowledge discovery, formation of Electronic Health Databases, detection of diseases and developing technological methods of curing diseases. Chiang et al, (2018) stated that with the help of data mining techniques there are vast number of diseases that are cured with the help of data mining include diabetes, liver diseases, psychiatric diseases, heart diseases, chronic diseases and even diseases like Parkinson disease and breast cancer.

Discussion on value added by business analytics and data mining applications for generating new business opportunities
Dutt, Ismail & Herawan (2017) stated that previously when data mining tools were not present, industries were not able to satisfy the customers and were not able to derive the best value of their business within the targeted community. The following values in this assignment on business analytics have been added within the different industries by usage of the data mining tools.

Education sector: In the education industry, data mining has helped in increasing the productivity of students and hence helped them to better achieve goals and score higher. Further García, Luengo & Herrera (2015) the data mining system has also made it more convenient for the teachers to interact with the students on a one on one basis and provide specific reviews to their parents. This is adding more value to the overall education system and grooming of the students and the interaction of the parents with the teachers are making it easier for the parents to also contribute to the value of the students. This has in turn helped in full capacity utilization of the students in the different educational institutes that use data analytics and data mining techniques. Sammut & Webb (2017) added that data mining has also helped in enhancing the methods of data storage and hence increased time saving for this industry. It can be noticed that for years and years the educational institutes are required to store data related to personal data of the students, copies of their results and also the projects and previous years’ questions for future study purposes. Data mining has thus made it easier to access such huge data storage. All of these benefits and activities have helped for generating higher business returns and in framing business value propositions.

Banking sector: Gandomi & Haider (2015) stated that the banks have been experiencing issues with protection of the online transactions and internet banking transactions. With the rise in the rate of the banking frauds is making the overall problem persistent by reducing the trust of the customers on the banks and thereby the banks especially the private and non nationalised banks are loosing on the customer base. Thus with the reduction in banking frauds using data mining tools the banks have been able to customer engagement and customer retention directly help the banks to generate value proposition. Moreover, the facilities of predictive analysis provided by data mining have also helped banks to make better investment and allocation decisions which has in turn increased business revenue generation for these banks.

Healthcare sector: In case of healthcare sector use of data mining techniques have been the most beneficial and has added huge value to the industry since use of the techniques have ensured high level of patient satisfaction which has added value to the healthcare industry. The business value propositions have automatically increased for the healthcare institutes because of the curing of a larger number and variety of diseases by the application of these techniques. Sammut & Webb (2017) added that data mining tools have ensured easier identification of the diseases as well as the probability of curing has helped the healthcare organizations to attract higher number of customers and increase the faith invested by the patients on these institutes. García, Luengo & Herrera (2015) stated that the major value that the data mining tools have added to this industry is the affordable and satisfactory healthcare service for the individuals. This happens when healthcare officials use data mining programs to identify and observe high-risk patients and chronic diseases and design the right interventions needed. These programs also reduce the number of claims and hospital admissions, further streamlining the process. The frauds related to medical insurance can also be avoided nowadays because of the data mining processes. Tan (2018) stated that data mining tools are helping the insurers to identify the unusual claim patterns and decide on the authenticity of the claims and process the claims accordingly. Thus financial benefit is also derived from usage of this tool.

Challenges associated with application of business analytics and data mining techniques
In terms of assessment of the challenges that the industries are facing for application of data mining tools shows that all the three industries that have been mentioned in the report include the high costs of applying and implementing these techniques. The other challenge that is specific to the healthcare industry involves the inter-relationships in between the variables and the attributes which need to be carefully studied, failing which serious consequences can occur that can even lead to the death of patients. In case of the education sector, the application has only been scarce and so previous developing new techniques of application can sometimes be tough. Dutt, Ismail & Herawan (2017) thus argued that although data mining is a very powerful tool however there are challenges with respect to its implementation related to performance, data methods and techniques. Apart from that Tan (2018) whose work is referred in this business analytics assignment stated that data mining involves dealing with huge amount of data and hence issues like scalability, measurement and evaluation errors, lack of effective knowledge of using data mining tools and issues of storage bottle neck are being faced by these industries.

In case of hospitals and nursing industries the use of data mining tools are very complex since in majority of the hospitals and nursing institutes the employees are not well trained and well educated especially in case of low paid institutes and hence the use of data mining strategies is difficult. Furthermore Tan (2018) stated that poor data quality like noisy data, dirty data, inadequate data, missing values and incorrect data are also posing challenge for the industries to use the data sufficiently in order to add value to the operational processes. In case of banking industry in many cases the fraud prevention system fails since the management is not able to get access to the right data of the customers. In majority cases it is seen that the KYC details of the customers are missing which hampers the overall firewall system of the bank. Dutt, Ismail & Herawan (2017) opined that in case of data mining the companies are required to deal with huge amount of data sets which requires the companies to use distributed approaches and the knowledge of using distributed approaches are very less among the employees working in the different companies. Moreover Gandomi, & Haider (2015) commented that the recruitment and training of the employees for the purpose of handling the data mining tools and business analytics method is also high and it is difficult for the companies with low level of profits and small scale companies to get the same. Another major challenge as stated by Gandomi & Haider (2015) shows that the companies in case of data mining are required to use non static, unbalanced and cost sensitive data for the purpose of assessment of the different results. Further the cost of training and recruitment is not the only cost, for the banking sector different types of software, storage systems, hardware and servers are required to be purchased which are used for the purpose of storing and analysing with the help of data mining tools. Thus these things are also highly costly in nature. Sammut & Webb (2017) stated that for the education sector using data mining effectively becomes challenging because the education institutes are required to process large amount of data and frame them into structure manner which becomes difficult for the educational institutes along with keeping pace with formation of effective academic courses for each year. Dutt, Ismail & Herawan (2017) stated that similarly in the banks with the ongoing transactions every second, the banking managers are required to handle the high data velocity since new data are formed every second when a transaction is being made. Thus Tan (2018) stated that although data mining and business analytics may seem an advantageous process for the companies operating in these three industries however the high number of challenges generally threatens the overall implementation of the same.

From the overall discussion in this business analytics assignment above it can be stated that data mining and business analytics is a vast technological field which has helped in adding value to the industries. The examples taken from banking, healthcare and education sector shows that the industries are highly benefiting from the application of data mining tools, however challenges are also a part of the application of these data mining tools. In order to conclude it can be asserted that the use of data mining has become increasingly important and comes with a few costs. However, the timely and meticulous application of these methods can help in solving a wide gamut of problems. Thus it is concluded in this business analytics assignment necessary that the banks, healthcare institutes and education institutes use the tools wisely in order to gain the advantage. Business analytics assignments are being prepared by our IT assignment help experts from top universities which let us to provide you a reliable urgent assignment help service.

Reference list
Aggarwal, C. C. (2015). Data mining: the textbook. Springer.

Ashouri, M., Haghighat, F., Fung, B. C., Lazrak, A., & Yoshino, H. (2018). Development of building energy saving advisory: A data mining approach. Energy and Buildings, 172, 139-151.

Chiang, R. H., Grover, V., Liang, T. P., & Zhang, D. (2018). Strategic Value of Big Data and Business Analytics.

Dutt, A., Aghabozrgi, S., Ismail, M. A. B., & Mahroeian, H. (2015). Clustering algorithms applied in educational data mining. International Journal of Information and Electronics Engineering, 5(2), 112.

Dutt, A., Ismail, M. A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5, 15991-16005.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.

García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (pp. 59-139). New York: Springer.

Goswami, S., Chakraborty, S., Ghosh, S., Chakrabarti, A., & Chakraborty, B. (2018). A review on application of data mining techniques to combat natural disasters. Ain Shams Engineering Journal, 9(3), 365-378.

Hassani, H., Huang, X., Silva, E. S., & Ghodsi, M. (2016). A review of data mining applications in crime. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(3), 139-154.

KS, D., & Kamath, A. (2017). Survey on Techniques of Data Mining and its Applications. Masters, T. (2018). Data Mining Algorithms in C++. Apress, New York.

Roiger, R. J. (2017). Data mining: a tutorial-based primer. Chapman and Hall/CRC.

Sammut, C., & Webb, G. I. (2017). Encyclopedia of machine learning and data mining. Springer Publishing Company, Incorporated.

Schubert, E., Koos, A., Emrich, T., Züfle, A., Schmid, K. A., & Zimek, A. (2015). A framework for clustering uncertain data. Proceedings of the VLDB Endowment, 8(12), 1976-1979.

Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.

Tan, P. N. (2018). Introduction to data mining. Pearson Education India.

Ward, J., & Hobson, A. (2018). U.S. Patent No. 9,894,088. Washington, DC: U.S. Patent and Trademark Office.


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