Answer To: 1 ITECH7406- Business Intelligence and Data Warehousing Research Report Group Assignment Sem1-2019...
Soumi answered on Apr 29 2021
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BUSINESS INTELLIGENCE ANALYTICS AND DATA MINING TECHNIQUES IN MANUFACTURING, HEALTHCARE AND CUSTOMER RELATIONSHIP MANEGEMENT
Introduction
Huge amounts of quantitative data stored in the data base of organisations in different industries use business analysis and data mining process to formulate their understanding of past and present market. The information, generated through data mining process gives business organisations to ability to predict the future trends and take measures beforehand to sustain the change as well as generate new business opportunities. In the current presentation, the three industries, namely – healthcare, manufacturing and customer relationship management (CRM) are chosen.
Business Analytics and Data Mining Techniques Used in Chosen Industries
Business Analysis Techniques
SWOT Analysis
PESTEL Analysis
CATWOE Analysis
MOST Analysis
Case Modelling
In terms of business analysis, SWOT, PESTEL, MOST and Business Process Modelling (BPM) are used. In the healthcare sector, the internal assessment of the organisation becomes more important that the external condition assessment, therefore, majority of the healthcare organisations use SWOT analysis as their preferred business analysis technique for business refinement. As mentioned by Phadermrod, Crowder and Wills (2019), with the use of SWOT analysis internal strengths, weaknesses, opportunities and threats are identified, based on which accurate and effective business strategy can be formulated.
In case of manufacturing industry, the internal assessment is necessary but not enough for business development, as the market response and trends identification becomes important as well, therefore in case of manufacturing industry, SWOT, PESTEL, CATWOE analyses are used. As assessed by Tsangas, Jeguirim, Limousy and Zorpas (2019), the use of SWOT analysis gives idea about the status of the internals of the manufacturing companies, while the PESTEL analysis gives a comprehensive idea about the market in which the manufacturing company is going to perform or is performing. The use of CATWOE aims to explore the external-internal as well as stakeholders’ relationships with business organisations, which helps in assessing the nature of the market.
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Business Analytics and Data Mining Techniques Used in Chosen Industries (contd.)
Data Mining Techniques
Sequential Technique
Classification Technique
Regression Technique
Outer detection Technique
Clustering Technique
Prediction technique
Lastly, in case of CRM, business analysis techniques such as Case Modelling, MOST analysis and brainstorming are used. In case of brainstorming analysis, as stated by Tang and Karim (2018), a diverse range of ideas are introduced, which leads to different approaches towards the market and attaining easy success. In case of Case modelling technique visual representation of the flow chart makes the business functionality better assessed by its management, which leads to better CRM standard attaining and successful business. In case of MOST analysis, as mentioned by Richards, Yeoh, Chong and Popovic (2019), the focus is laid upon the mission, objectives, strategy and techniques applied at the workplace. At the time of developing healthy customer relationships, the prioritisation of the mission and objectives of the workplace is necessary, which MOST analysis ensures.
In terms of the use of data mining in the healthcare industry, the sequential technique of data mining is found in usage. The sequential data mining technique, as mentioned by Shmueli, Bruce, Yahav, Patel and Lichtendahl Jr (2017), caters small amount of sequential data sets within a limited areas of focus and identifies simple patterns for localised and specific circumstance-based information. In the healthcare sector, the sequence of actions lead to the outcome of the care users, therefore, the provided services at healthcare are observed in sequential sets, to formulate limited yet effective information for strategic improvement.
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Business Analytics and Data Mining Based Information in Australian Healthcare
(Source: Safe Work Australia, 2019)
In case of the manufacturing industry, there are multiple data mining techniques applied. Considering the fact that manufacturing industry generated products are highly categorised, in accordance of their requirements, classification, regression and outer detection data mining techniques are used. The classification technique of data mining, as described by Shahiri and Husain (2015), selects only the important data, based on the frequency of usage, occurrence and effectiveness and are used as the core values of strategic planning in manufacturing organisations. In case of regression technique, the relationships between the variables are considered important and patterns are identified on the basis of the connection between the variables. In the manufacturing industry, regression helps in product development, offering the best combinations for highest degree of outcomes. Lastly, the use of outer detection data mining technique combination of variables and their response patterns are considered for information formulation. As mentioned by Ye, Li, Adjeroh and Iyengar (2017), the manufacturing industry manufactures products for the users and therefore, the assessment of the users in respect of their usage of the manufactured products and its prominent aspects lead to safer experimentation, faster research and development, all of which contribute to the betterment of the manufacturing industry.
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Business Analysis and Data Mining Creating Opportunities in Chosen Industries
Business analysis helps healthcare organisations to pivot on their strengths and consider recovering their weaknesses
Reduced the cost of service offering attaining better profiting opportunity
In case of CRM, clustering and prediction techniques are used for data mining. As described by Jain, Hautier, Ong and Persson (2016), with the use of clustering technique huge amounts of data are segregated, based on their nature and response similarities to customer relationship development process. The process of clustering helps in understanding the similarities group of data have and their impact on the customer relationships. On the other hand prediction technique uses the available data in the database of past events and based on the identification of the changes of trends over time, it predicts the trends of business in near future....