AIH Planning And Business Intelligence

Discuss about the Data Mining and Business Intelligence.

 

 

Introduction

AIH is planning to offer financial help to the students by developing its own financial programs. The primary aim of this project is to offer low interest financial help to the students to encourage more students to enroll with the organization. The business goal of this initiative includes increase in the number of students, offering assistance to the students in fees and other living expenses and further encouraging the senior citizens, home makers, low income earners to study without worrying about a the cost of studying. However, there are certain risks associated with this plan, which includes the risk of accruing bad debts and so on. The business understanding of this scheme is elaborated in the following paragraphs.

Business Understanding: Business Objective

The main objective of this project is to provide a financial help for the students without using the government sponsor. This organization is planning to implement this by developing its own financial programs by linking with the existing financial institution. This noble initiative is named as “We can pay you to study now” by the organization. The idea and objective behind implementing this project is to increase the students’ enrolment, offering assistance to the students with their fees, offering assistance for the living expenses of the students and providing avenue for students to encourage them for studying more without worrying.  

Problem Area

The major problem area associated with the implementation of this project is its business development. Since the project deals with the development of financial help for the students by developing its own financial programs by linking with the existing financial institution, a major problem will occur in the implementation of the project if the existing financial institutions refuse to render their support. 

Data mining is important for this project as it will give an idea of the students’ structure and details of the people who although are interested in undertaking a course, are not being able to join it due to financial issues (Shmueli & Lichtendahl Jr, 2017.).

The prerequisite of the project is definitely a rough calculation of the probable number of students who might be interested in undertaking a course if given this opportunity. Therefore, the business definitely use data mining (Larose, 2014).

The target group for this project is mainly the low-income earners, rural residents, international students and homemakers. A running system can definitely be expected as the organization is planning to upgrade its scheme from government financial help to providing own financial programs.

The users’ needs and expectation includes, studying without worrying about paying the course fees.

Business Success Criteria

The successful implementation of this project may help in meeting the business objectives identified for this project. The different business success criteria associated with the project are listed below-

1) The project would be considered successful from a business point of view only if the enrollment of students is increased by 20% on an average.

2) Students are benefited by this project and proper student assistance is achieved

3) The project offers assistance for the students’ living expense that should be decreased by at least 5%.

4) The enrollment of senior citizens, professionals, homemakers and low income earners are increased by 10 %.

Assess Situation: Inventory of Resources

The resources available for the project are listed below-

1) The Project manager appointed for the project who is responsible for developing a proper project plan

2)  A data-mining expert who is responsible for doing the market research about the success factors of the project

3) A high authority member of the organization to set the project and the business goals

4) The data gathered from the extensive data mining process

5) A proper data-mining tool such as Rapid miner and Orange (Hofmann & Klinkenberg, 2013)

6) A technical support expert hired for  proper storage of the data gathered by the process of data mining (Ledolter, 2013)

Sources of Data and Knowledge

The different data sources identified for the project includes –

1) The data collected from the different social media platforms as a result of extensive data mining. This data sources is grouped as online data sources that helps in estimating the needs of this project and its success criteria.

2) Data collected from different institution in order to know about the need of implementation of the project.  

3) The different written documents and the research papers in assuming the probability of success of this project

The different knowledge sources for implementation of this project are listed below-

1) Different online sources, especially the social media that are used worldwide can be a great source of knowledge (Aggarwal & Zhai, 2012)

2) Different research papers may further help in gaining knowledge about the implementation of this project

The most important technique for successful implementation of this project is a data mining and a proper project implementation plan. The data mining tools that can be used includes Rapid miner and Orange.

The background knowledge that is essential for implementing this project is researching about the similar other projects and thoroughly understanding its pros and cons from business point of view. This will help in determining the correct strategy and plan for successful implementation of this project.

Requirements, Assumptions and Constraints

The requirements for the project are listed below-

1) The project must be completed within the set schedule in order to obtain the desired results. The project must be completed within 60 days.

2) The different risks associated with the project, which includes incurring a bad debt for lending money from different financial institution and causing financial hardship for students and the institution as well should be properly dealt with before implementing the project. The failure of this project may not only risk the organization and earn it bad reputation, it may further result in certain legal issues against the organization. Therefore, these conditions must be properly evaluated before the implementation of the project.

The different assumptions that are associated with the project are listed below-

1) The data are mined from reliable sources especially in case of online resources. The authenticity of the data is needed to be ascertained in order to receive an accurate result. the process of data mining holds utmost importance in proper implementation of this project and therefore  ascertaining the integrity and authenticity of the data, which is mined is a must need.

2) The project schedule is sufficient for the completion of the project

3) The resources required for the project will be easily available

4) The project will not go over budget

The project constraints associated with the project are listed below-

1) 25 % of the project work should be completed within the first 20 days of project initiation

2) The resources are limited and therefore the project must be completed with the given resources only

3) The cost of the project cannot be changed

4) The scope of the project should remain intact

The different risks associated with the project and its contingency plan are listed in the following table-   

Associated Risk	Contingency Plan and Risk Mitigation
1) Schedule Risk: The project is taking a longer time than expected, which might ultimately lead in increasing the overall cost of the project	In order to ensure that this risk is not faced by the organization while implementing the project, the project progress must be supervised on a daily basis (McNeil, Frey & Embrechts, 2015).
2) Cost Risk: This risk might be a result of poor cost estimation and accuracy of scope creep	The cost risk can be mitigated by developing a proper requirement analysis of the project and developing the project plan accordingly (Christoffersen, 2012)
3) Performance Risk: This type of risk occurs when a project fails to produce results according to the specifications.	This risk can be mitigated by proper supervision of the project (Lam, 2014).

The business risks associated with this project includes the other institution that may develop similar business strategy in attracting the students.

The financial risks associated with the project is that, if the other financial institution refuses to render help.

The data risks included mining of data from un-trusted sources, which may provide wrong data.

Determining data mining goals

The data-mining goal indentified in implementation of this project are listed below-

1) The process of data mining should be able to predict the possible number of students, who would be interested in joining the organization if this project is implemented (Chen,  Chiang & Storey, 2012)

2) The process of data mining associated with this project should be able to classify the needs of implementing this project and the possibility of its success.

The data mining problem type associated with implementation of this project scalability of the data, that group into clustering problem of data mining. Highly scalable clustering algorithm is required for managing the data mining process (Wu et al., 2014).

Conclusion

Therefore, from the above discussion it can be concluded that the project under discussion can be implemented successfully by the said organization by an extensive research based data mining process and by following a proper project management plan. The report discusses the different requirements, assumptions and constraints associated with this project. The report further elaborates the business objectives and data mining goals associated with the project.

References

Aggarwal, C. C., & Zhai, C. (Eds.). (2012). Mining text data. Springer Science & Business Media.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).

Christoffersen, P. F. (2012). Elements of financial risk management. Academic Press.

Hofmann, M., & Klinkenberg, R. (Eds.). (2013). RapidMiner: Data mining use cases and business analytics applications. CRC Press.

Lam, J. (2014). Enterprise risk management: from incentives to controls. John Wiley & Sons.

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

Ledolter, J. (2013). Data mining and business analytics with R. John Wiley & Sons.

McNeil, A. J., Frey, R., & Embrechts, P. (2015). Quantitative risk management: Concepts, techniques and tools. Princeton university press.

Shmueli, G., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.