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Discuss about the Model For Data Mining Project.



One of the significant elements that should be searched for in a data mining application is to determine the reason for which the study is implemented. Data mining is utilized for proliferation of novel forms of knowledge from the existing business knowledge. The existing process model for data mining is reliant on the use of CRISP-DM reference model and the life cycle of the data mining project involves the distinct phases involved in the project, the relevant tasks involved in the phases and the interrelationship between the different tasks. Relationships among the different tasks in a data mining project are profoundly dependent on the goals as well the background of the data mining project and the significance of the data to the user (Bhattacharyya, 2015).

From the perspective of a data mining consultant, the illustration of this report could provide credible insights for the AIH in determination of problem areas that could result in failure of its initiative known as, “We can pay you to study now”. The foremost process involved in the data mining process is business understanding which emphasizes on comprehending the project objectives and the requirements from the perspective of a business. Business understanding could also be apprehended as the comprehension of the business about its background and objectives (Fleisher & Bensoussan, 2015). The business understanding process is also associated with translation of information related to project objectives and requirements into the definition of a data mining problem. The data understanding process follows the business understanding process which involves collection of initial data present in the existing database of the institution. Data understanding provides insights into the data and the quality issues that could be identified in the student information.


The data preparation tasks are required to be performed multiple times involving the transformation of the existing data into a new format. The following stage of the business understanding refers to modelling which could be realized in the case of AIH through establishing specific parameters according to optimal values. Evaluation stage in the business understanding could be associated with the review of the model’s capacity to address business objectives of AIH (Foorthuis & Brinkkemper, 2015). The results obtained from the data mining model should be reviewed in terms of their application in context of the business objectives. The final stage of business understanding would involve deployment which refers to the organization of the data and representing in a feasible manner to the institution.

The deployment stage should be executed by the institute as they have to define approaches that could help them in aligning the data mining model for accomplishing business objectives (Gandhi & Armstrong, 2016). The concerned case of initiative followed by AIH would involve the final outcome in the form of a report presented to the top management by data mining analyst. The report would comprise of the recommendations for the top management to adopt a repetitive data mining process in order to ensure sustainable operations of the “We can pay you to study now” initiative.     

The different processes of business understanding include determining business objectives, assessment of situation, establishing data mining objectives and presenting a project plan. In order to devise a data mining project for the initiative of AIH to provide financial assistance to students undertaking degree courses in the institution, the institute should consider it as a technological advantage that could ensure feasibility of the initiative (Jenkins & Williamson, 2015).  


Business objectives:

The data mining process must initiate with an interpretation of the background for the project. In this case, the background of the data mining process is vested in the novel initiative of AIH College to provide financial help to the students without depending on financial support from the government.

The institution intends to devise new financial programs with existing financial institutions. Therefore, AIH has devised a set of objectives which must be reviewed in the initial stages of the business understanding process (Jain & Srivastava, 2013). The perception of the business objectives involves determination of problem area, describing the primary objective of AIH and the success criteria that determine the extent to which the project would ensure successful outcomes from the perspective of the client.

The problem areas which could be observed in the case of AIH could be observed in business development and marketing. The problem could be described generally in terms of the impact of the uncertainties and risks pertaining to financial aspects. The organization does not have an existing data mining framework to acquire information related to students and potential market research that could provide insights into the feasibility of the initiative (Khan et al., 2014).

The primary motivation for the project could also be characterized as a prerequisite for the project and can be observed in encouraging higher enrolments in the various courses and degrees provided at AIH. The target group that must be identified for the project result include the management of AIH and the financial institutions with which the college intends to develop the new financial programs. The expectations of the users from the project include references to the information related to market demographics and student information. The market demographics data mining would provide the institution with a clear impression of the specific market segments in which the institute has acquire substantial enrolments (Larose, 2014).

The data mining of student information would also provide an illustration of the performance of students and observe the degrees or courses in which the college has acquired higher enrolments thereby providing an interpretation of the marketing potential of different courses provided by the institute.

The business objectives that could be perceived from the perspective of the client refer to improving student enrolment; provide financial assistance for students to ensure their academic fees and living expenses (Witten et al., 2016). The primary business objective could be identified in improving student enrolments which would promote its marketing potential.

The secondary objectives could be identified in provision of financial aid to students for their living and study. AIH intends to facilitate a new opportunity to students for higher academic involvement through the provision of financial assistance. The financial aid provided to students would also ensure their minimal involvement in jobs during academic semesters thereby indicating the explicit improvement of academic performance.

The success criteria that could be presented in context of the data mining project from the business perspective would be in the increased student enrolments and financial performance of the institution.

The specific business success criteria that could be identified for AIH include the improvement of enrolment rate by 30% which is subject to evaluation by the top management of the institution. The financial performance of the business could also be assumed as a specific business criteria which can be identified by the extent to which loss in terms of interest on the capital provided to students is offset by improvement in the marketing image of AIH as well as enrolments (Zhao, 2015).


Assess situation:

Inventory of resources:

The resources available to the data mining project for AIH could be identified in terms of the computing resources, software, personnel and data which have to be illustrated explicitly in context of this report.

The inventory of resources would imply references to the personnel such as data mining personnel that would be involved in the process of data mining for AIH. The data aspect of resources in context of this project includes references to the access to operational data related to student enrolments and the revenue earned l enrolments (Foorthuis & Brinkkemper, 2015). The computing resources that are available in context of the existing situation would be observed in terms of the hardware resources in the institution for maintaining database of student information.

Sources of data and knowledge:

The data sources that could be identified in context of the data mining project involve written documentation and the information stored in the institutional database. The knowledge sources for implementing the data mining project would also be observed in the availability of tools and techniques as well as background knowledge pertaining to the intended initiative of AIH to provide financial assistance to students. The type of data source in the case of AIH is written documentation which can be identified in the paperwork completed by students during enrolment.

The market information related to AIH could also be accounted as another data source. The online sources pertaining to the data mining process could also be accounted as a valid knowledge source. The information related to successful examples of implementation of data mining could also be accounted as viable knowledge sources (Gandhi & Armstrong, 2016). The tools and techniques that could be implemented in context of AIH for acquiring data involve market research reports and demographic analysis. Information in context of the accounts of AIH could also be considered as credible type of data source that can be implemented in the data mining process. The formal description of the background would involve references to the availability of information related to standard models of data mining through the secondary information.

Requirements, assumptions and constraints:

The project would be executed over the course of a week which could be used to describe the schedule of completion. The individual stages of the business understanding in data mining have to be understood distinctly in order to allocate the time required for completion. The schedule of the project can be presented in the form of a chart as follows.

Stage of the project


Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Business understanding








Data understanding








Data preparation

































The quality of the results and the interpretation of their application in context of AIH could be based on the assumptions made regarding data. The existing data available for the project in terms of student information could be assumed as sources which have not been updated. Data obtained from the online sources pertaining to the basics of data mining and the examples of successful cases of data mining projects as well as standard data mining packages could be accounted as checkable assumptions that could be reviewed during the process of data mining on the basis of data quality (Jain & Srivastava, 2013).

The business related assumptions could be observed in the intended outcomes from the project such as improvement of the marketing image of AIH which could be possible through the initiative. This can be considered as a non-checkable assumption since the initiative for providing financial assistance to students could be related to the marketing potential of the institution.

The constraints which are involved in case of AIH include the lack of resources for implementing the project alongside the minimal timeframe allocated for completion of the project (Gandhi & Armstrong, 2016). The prominent constraints are observed in the data understanding process which was subject to issues related to perception of quality of the data. The ethical constraints established in context of the data mining project are observed in the lack of access to information related to personal background of students. The legal constraints involved in the project could be observed in the form of limitations on the information sharing privileges of the institute with the involved financial organizations.

The risks that could be identified in context of the project could be classified into different categories such as business risks, financial risks, technical risks and organizational risks. The business risks posed for the project involve the possibilities of irrelevance of the data mining outcomes for the objectives of AIH. Therefore the contingency plan that could be presented to address this risk would be vested in selected of optimal parameters for data organization.

The organizational risk that can be observed in context of this case study refers to probabilities of resistance from institution management on the basis of ambiguities related to funding for the additional project (Bhattacharyya, 2015). The contingency plan for the organizational risk refers to communication of data mining as a source of competitive advantage to the management and informing them about the test outcomes of data mining in AIH. The data risks are also identified in context of lesser quality which can be addressed through research and reorganization of the data.

Data mining goals:

The data mining goals could be described in the form of expected outputs from the project which affect the business objectives. The data mining goals refer to the identification of the share of individuals that have a degree in a specific population and the success degree of students in AIH.

The success of students in academic performance at AIH could be integrated as a viable data to identify the institution’s ability to project its marketing image. The demographic classification of students could also be accounted as a data mining goal in context of this project. This objective would be helpful for AIH to determine the market segments belonging to lower income class thereby acquiring an interpretation of the potential market for AIH.  



Bhattacharyya, S.C., 2015. Mini-grid based electrification in Bangladesh: Technical configuration and business analysis. Renewable Energy, 75, pp.745-761.

Fleisher, C.S. and Bensoussan, B.E., 2015. Business and competitive analysis: effective application of new and classic methods. FT Press.

Foorthuis, R. and Brinkkemper, S., 2015. Best practices for business and systems analysis in projects conforming to enterprise architecture. Enterprise Modelling and Information Systems Architectures, 3(1), pp.36-47.

Gandhi, N. and Armstrong, L., 2016, March. Applying data mining techniques to predict yield of rice in Humid Subtropical Climatic Zone of India. In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on (pp. 1901-1906). IEEE.

Jenkins, W. and Williamson, D., 2015. Strategic management and business analysis. Routledge.

Jain, N. and Srivastava, V., 2013. Data mining techniques: a survey paper. IJRET: International Journal of Research in Engineering and Technology, 2(11), pp.2319-1163.

Khan, M.M.H., Le, H.K., Ahmadi, H., Abdelzaher, T.F. and Han, J., 2014. Troubleshooting interactive complexity bugs in wireless sensor networks using data mining techniques. ACM Transactions on Sensor Networks (TOSN), 10(2), p.31.

Larose, D.T., 2014. Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.

Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

Zhao, Y., 2015. Data mining techniques. 


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