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Business Intelligence Analytics And Data Mining


Executive Summary: Precision is inevitable for every business organisation, irrespective of its market position or brand image. A business organisation has to be precise with every aspect of it; the decision it makes must be based over data or information. The information has to be materialistic and effective at the same time. Any organisation intends to maintain a database that holds massive datasets. These databases are further used for retrieving information for future references. Data Mining procedure does have the potential to assist an organisation to make the most effective and efficient decision ever. Information is best used when the future decisions are based on such information. Data mining is a revolutionary step forward that has made significant improvements and changes that would help organisations, medical professionals and banking professionals to ensure better and effective quality services.

Introduction: The paper focuses over the concept or data mining in combination of its application on different domains or areas. The domains or areas that have been selected are- Healthcare industry, Banking industry and Customer Relationship Management. As mentioned earlier, information or data holds a massive importance irrespective of the nature, size or market position of the business (Aburrous, 2010). Healthcare is a domain that can hardly be considered as an industry; patients and professionals deal with life and death situations on daily basis in this domain. Miscalculations will certain the uncertainties; using information with precision can help track terminal patients or patients with chronic health conditions. It can also helps in making discoveries about "patterns of radiological images" and many more. Banking industry is another domain that can be highly benefitted with the usage of data mining. Using data mining techniques would certainly elevate financial institutions' ability track and detect any fraudulent activities related with its core services (Aggarwal & Zhai, 2012). As for the CRM (Customer Relationship Management) is concerned, data mining does provide effective tools for analyzing customer information. This is further used for making effective and error free business decisions. Furthermore, data mining can also help in analyzing consumer buying patterns, marketing strategy determination, consumer segmentation and etc. The paper below is entirely focusing over the how data mining can be of great use for the above mentioned industries or domains.

Question: Create a written report that will review the applications of business intelligence analytics and Data Mining in different industry domains in decision making contexts. How business intelligence analytics and Data Mining techniques revolutionize businesses today.

Answer: Topic 1: Healthcare Industry: Data mining as mentioned earlier holds massive importance on medical terms. This does have the potential to help healthcare professionals to design systems for using data systematically to identify adversities (Berry & Linoff, 1999). This also helps in designing the best possible solution for enhancing the quality of care treatment along with reducing cost by best possible means. According to some experts nearly 30% of the overall healthcare expenditure can be saved while trying to reduce the overall cost and enhancing quality simultaneously. However that is the most ideal situation, where there is a ‘win-win' situation for everyone. The industry today lags behind due to various reasons; the industry is highly complex and significantly slower in adopting technological advancement (Bhambri, 2011). The list of healthcare applications includes the following-

Discovering patterns in radiological imaging: Data mining typically facilitates an examination of radiology data in multiple dimensions. This does have the potential to convert vast datasets and patient image into vital and useful information, which helps in enhancing the quality of treatment and facilitating informative reports to the medical professionals. What "Data mining" does is it evaluates data residing with the entire mechanism of radiology information system and the hospital information system with the help of special software (Cios, 2012). The software then analyzes to see if there is any agreement and relationship among available information. It becomes easier for the radiologists to make informed and error free decisions using similar data analysis tools and techniques. It automatically becomes easier for them to make efficient predictions about the future (Esling & Agon, 2012).

There are different types of data mining including- clusters, classes, sequential patterns, associations, classifications, predictions and decision tress. Data mining has been considered to be effective in terms of making the healthcare more affordable.

• Analyzing microarray experimental data to cluster genes

• Tracking high-risk patients

• Tracking chronic patients

The healthcare sector depends significantly over the big data. Physicians today rely significantly over the medical health record of their patients, which certainly is about gathering massive amount of data that too from numerous patients (Han, 2012). The situation can easily go out of control if not managed properly due to the large volume of information. The degree of growth of EHRs or the electronic health records is massive. The information that has been previously recorded on paper is being converted into digital form and is stored inside a computer for developing predictive models afterwards. Quality can never be compromised when it is about patient care, which is where data mining does play the most important role (Hormozi & Giles, 2004). There are several benefits of using data mining within the healthcare sector including-

• Medical treatment becomes more precise and affordable

• Identifying best care solution and effective treatments

• Data mining makes it easy to identify

• It becomes easy to make patient related decisions


Real life example-

Case Study: WHO (World Health Organization) holds massive volume of data that was once used in Saudi Arabia. The purpose was to obtain datasets about NCDs (Non Communicable Diseases) risk elements in Saudi Arabia. The datasets were further analyzed in order to obtain a close enough result that can be substantially useful. Furthermore, ODM (Oracle Data Miner) was also employed as the software mining equipment, which is to be used for considering and predicting different modes for treating diabetes. The dataset was further analyzed for identifying efficiency of various treatment options considering the age diversity (Koh & Tan, 2011).

Data Mining Techniques.jpg

Challenges for data mining techniques in healthcare: Healthcare as discussed earlier does have the potential to generate massive volume of data on daily basis including knowledge about various aspects. The data generated on daily basis is often heterogeneous and are gathered from different sources including- patient conversation, lab results, interpretation from doctors and etc. The likelihood of crash while dealing with such massive amount of data remains high constantly, which certainly would affect the quality of treatment. This must be taken into consideration on a serious note every time for compromising quality of the treatment cannot be tolerated at any possible cost.

Topic 2: Banking Industry

One of the most technologically advanced phenomena in the banking sector has been the "Online banking". This certainly has a significant impact over the banking industry all over the world. However, there is one factor that is directly connected with the sustainability aspect that is "a clear and precise understanding of the consumer, their investment nature along with their demands". Therefore the concept of "Data mining" holds a strategic importance for the banking sector (Liao, 2012). The core purpose or the essence for data mining does not change, what changes however is its application. The management gathers information from various sources followed by a thorough analysis and concluded with a summary of valuable information. Data mining on a larger note assists financial institutions to identify hidden pattern along with discovering unfamiliar association among data. Apart from the vast pool of benefits that data mining has to offer, this can be used for- detecting financial fraud, money laundering, corporate and credit card fraud (Lin & Cercone, 2012). Moreover, banks aren't apart from facing cut throat competition, the only key to ensure sustenance and survival in this market is customer relationship (Liu & Motoda, 2012). Banks that consider using knowledge as their asset rather than financial resources as their excellence are certain to strive in the most effective manner (Low, 2012). Banking industry is filled with chances of being victimized by fraudsters, which makes both customers and financial institutions vulnerable. One of the most common forms of fraud in banking sector is "credit card fraud". The most common type of fraud associated with credit card is a situation where, the card holder doesn't make the payment he made with the credit card. This however can get much severe; a fraudster can obtain funds from an individual's account in an unauthorized manner (Miner, 2012).

Financial Fraud: This can be defined as an illegal act of dishonesty or trickery that involves financial deal for personal gain.

Credit card fraud: This is an umbrella term, which includes different forms of fraudulent acts associated with credit card. This can be an unauthorized use of a credit card of another holder for making purchase. Credit card can as well be used for withdrawing money in a dishonest manner, which again is another form of fraud linked with credit card.

Corporate Fraud: This occurs on a broader note in comparison to a "credit card fraud", which involves an organisation/company undertaking fraud activities, designed precisely to offer advantage to the committing company or individual.

Money laundering: This is another form of fraud, which involves covering up the origins of money obtained in an illegal manner. This typically involves money transfers to foreign banks or may even be lawful businesses.

Banking Industry.jpg

Real world example-

Case Study 1: This is an incident that occurred during the year 2012, which happened with one of the most renowned American multinational organisation "Adobe Systems". Information about nearly 40 million cards were leaked or compromised, which also included consumer or card holder's names and other details. The Adobe Systems was hacked and valuable detail was leaked, the list of such valuable information included encrypted payment details of the card along with numbers and encryption numbers and finally information about different orders and etc. This was a major attack to one of the most secured and protected organisations in all over the world. This drawn significant amount of attention towards ensuring security of the card in an indiscriminate manner (Moin & Ahmed, 2012).


Topic 3: Customer Relationship Management

Continuous and significant technological advancements have brought the concept of "relationship management" into reality. Technologies like data mining, campaign management and data warehousing have made consumer relationship management an area of interest for organisations. This in particular area would help the organisation to gain a competitive advantage over their competitors (Siemens & d Baker, 2012, April).

This is the era of globalisation, business organisations are free to flock to places with maximum opportunities. This has elevated the level of competition by severe proportions, which is even intensified due to the fact that the market today is consumer oriented. Organisations cannot afford to commit mistakes like overlooking consumer demands and preferences (Srinivas, 2010). In a situation like this, information plays the most vital role for organisations. Organisations rely over the information they gather on daily basis about customer buying behaviour, spending nature, product preference and etc. The gathered data is then analyzed to produce valuable information that is then used for gaining competitive advantage over market competitors. The information can even be used for enhancing organisational ability including- better understanding of their customers. Similarly, data mining in customer relationship management can as well be equally effective in making decisions, determining marketing strategies and consumer segmentation.

Analyzing buying pattern: Consumers do have unique buying pattern according to their demands, spending ability and product preferences. This has to be identified and analyzed effectively for an organisation has to be compact and precise in terms of offering their products.

Determining marketing strategies: a marketing strategy is the valuable plan of action that is often used for promoting the product and selling eventually.

Consumer segmentation: Post segmenting the market, organisations must segment their consumers as well, which is often categorized as- demographic, psychographic, geographic and etc. This is a process of dividing the entire consumer group into smaller clusters on the basis- age, sex, product preference, financial ability etc. This certainly enhances the precision especially when it is about knowing the customers.

Store/product segmentation: This is quite similar to the idea of consumer segmentation. This can be defined as "grouping products"; product segments include products, which have been designed with a precise combination of attributes. Similarly, store segmentation is about grouping multiple stores having common features, which certainly enhances store's ability and footsteps.

Customer Relationship Management.jpg

Decision making: Data mining is certainly helpful for organisations to make decisions about multiple aspects such as- supply chain related decisions, financial planning related decisions, internal operation and etc. Every customer is unique and has as unique style or pattern of buying or spending their money. This has to be studied and recorded effectively, which would then be converted into a valuable piece of information.

Marketing strategies: One of the best possible use of data mining for any organisation is about enhancing organisational data storing capacity in their databases. Marketing strategies can be the best possible tool for organisations to position and launch their product in the market in the best possible manner. However, positioning the product or brand in such competitive manner requires a thorough knowledge about the customers and the market in general. Marketing strategies have to be based over solid and materialistic information that will give the strategy the perfect shape and effectiveness (Tomar & Agarwal, 2013).

Consumer Segmentation: Market segmentation is another aspect of marketing that has to be completed in the most effective manner. An organisation cannot serve an entire market; it has to create clusters or smaller groups that it would dedicate its products to. This again depends largely over the quality and sort of information the organisation has stored in their databases. Data mining therefore plays the most important role in this position, marketing professionals can use customer related information in order to study customer's buying behaviour, their product preferences along with their spending nature. This would ensure that the segmentation is done in the best possible manner. Similarly, segmentation of stores and product are also necessary; segmenting the entire market would help the organisation to determine the location of their stores. In the same manner, market segmentation would help the organisation to understand the product preferences, which again is vital information for placing their products across different locations of the market.

Maintaining a relationship with customers needs an in-depth understanding of their buying behaviour, their product preferences, spending nature and etc. Having a clear and precise understanding about customers would also help organisations to be responsive to each and every requirement of their customers (Yoo, 2012).

Challenges for data mining in CRM: In the similar manner as the healthcare sector, data in CRM domain also comes from numerous sources. This automatically enhances the importance of an effective integration of gathered data. In case of CRM data comes from various organisational departments; multiple data patterns span across various data sources, which is why it is vital to ensure a strategic integration of data ahead of exploration of data mining starts. There are some other issues as well including- confidentiality and privacy. Issues like legal considerations do have the potential to influence "the type of data must be available for the purpose of data mining".

Case Study: Tesco Plc is a British origin multinational retail organisation that has its history linked back to early 1900th century. The organisation initially was started as a tiny shop, which gradually came up as the strongest grocery seller across London. The organisation soon grew overseas, which happened successfully because data mining was at the key role play position.

Future of Data mining: The healthcare sector has witnessed a massive and revolutionary change in the manner data or information is kept secured. The change is quite visible and can be seen making significant improvements to the healthcare and treatments to the patients. Maintaining electronic health records has made it possible for the healthcare professionals to share and use health information across every professional attached to the link. This in turn certainly helps in reducing any potential medical error and finally improving satisfaction for patient in care. Apart from the healthcare sector, data mining has made significant and revolutionary improvements for the banking and customer care relationship management as well. Banking and CRM both sectors need a better and effective understanding of their customers along with market trend (which however is applicable for customer relationship management only). This would help the organisation to make best of their resources and human capital.

Conclusion: The banking sector has certainly witnessed massive degree of technological advancements. However that isn't substantial for there are loopholes that can always cause devastating results. Loss of financial and private information is certainly a breach to the top most security effort being made to make everything secured. This would certainly act against the organisational reputation and brand image. Adobe Systems for example is one of the most reputed and well renowned organisations in all across the globe. The fraudsters can always come up with malware that can help them breach the security systems easily. Therefore, it is necessary for the organisations to adopt technologies that can help them ensure top-notch security of their overall system. Similarly, data mining is vital for the healthcare industry as well. Healthcare institutions and organisations nowadays generate vast volume of data regarding their patients, disease treatments, diagnoses, resources, different medical devices, patient records and so on. The place data and information is stored often regarded as data warehouses. These warehouses represent particularly the raw data that has to be analyzed and processed for extracting valuable information or knowledge that would further be used for enhancing the quality of treatment. The information generated in this manner would certainly be materialistic and useful for making decisions and saving cost at the same time. Lack of sufficient data can no longer be regarded as an issue but the lack of capability or talent to generate vital information from gathered data. An organisation has to be aware of the mental state of their customers, which includes their product preferences, their spending ability and etc. This would certainly help the organisation to get rid of their inability to generate substantial volume of data to be further processed into valuable information. The entire procedure of generating valuable information demands a support from the top level management in terms of understanding the information needed for policy making, governance and the organisation overall.


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