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ISY310 Business Analysis-Data Mining

The first objective of the analyst is to thoroughly understand, from a business perspective, what the client really wants to accomplish. Often the customer has many competing objectives and constraints that must be properly balanced. The analyst’s goal is to uncover important factors at the beginning of the project that can

influence the final outcome. A likely consequence of neglecting this step would be to expend a great deal of effort producing the correct answers to the wrong questions.

identify the problem area (e.g., Marketing, Customer Care, Business Development, etc.). Describe the problem in general terms. Check the current status of the project (e.g., Check if it is already clear within the business unit that we are performing a data mining project or do we need to advertise data mining as a key technology in the business  Clarify prerequisites of the project (e.g., what is the motivation of the project Does the business already use data mining. Identify target groups for the project result (e.g., Do we expect a written report for top management or do we expect a running system that is used by naive end users.  Identify the users’ needs and expectations.

Describe the customer’s primary objective, from a business perspective, in the data mining project. In addition to the primary business objective, there are typically a large number of related business questions that the customer would like to address. For example, the primary business goal might be to keep current customers by predicting when they are prone to move to a competitor, while secondary business objectives might be to determine whether lower fees affect only one particular segment of customers.

Informally describe the problem which is supposed to be solved with data mining. Specify all business questions as precisely as possible.  Specify any other business requirements (e.g., the business does not want to lose any customers).  Specify expected benefits in business terms. 

Describe the criteria for a successful or useful outcome to the project from the business point of view. This might be quite specific and readily measurable, such as reduction of customer churn to a certain level or general and subjective such as “give useful insights into the relationships.” In the latter case it should be indicated who would make the subjective judgment.  Specify business success criteria (e.g., enrolment rate increased by 20 percent).  Identify who assesses the success criteria. Each of the success criteria should relate to at least one of the specified business objectives.

Answer:

The procedure of searching significant knowledge by analyzing and evaluating huge amount of data is known as data mining (Braha 2013). These huge amounts of data are recorded in the databases or several data warehouses. The person or the individual who performs data mining in several organizations is called a data miner.

Data mining is performed with the various techniques like the statistics, machine learning and artificial intelligence. AIH will be offering financial help in education for the students. They have hired a data miner for performing data mining in the organization (Grossman et al., 2013). The data miner or the business analyst may have various objectives for the business of AIH. The objectives are given below:

  1. i) The first business objective of a data miner is to know and understand completely about the client objectives from business perspective.
  2. ii) The second objective of the data miner of this case study is to balance all the constraints of completion and business objectives of the client (Liao, Chu and Hsiao 2012).

iii) The final objective of the data miner is this case study is to discover and unveil the major factors influencing the outcome.

Activities: Problem Area

AIH will be offering any type of financial help to


the students for their education. They would be developing their programs for financing with all of their existing and remaining institutions (Baker and Inventado 2014). Although, it will be providing such huge advantages, there are certain threats and risks in their program. The major risk in their program is the collection of the bad debts from this particular scheme of supporting the students financially in their education. The major initiative of AIH is that they would be paying the expenses of the education and the students can complete their education without any type of tension (Siemens and d Baker 2012).

They have hired a business analysts or data miner for performing a project of data mining in their organization. This project would be extremely beneficial for the organization for achieving the goals and organizational objectives. The main motivations of this particular project are those individuals and students, who cannot complete their education for the sake of expenses. AIH is not using the process of data mining in their business yet, however they are planning to do so.

A typical written report or document is expected from the management bodies for the permission of organizing this type of project in the business. The project is assumed to be the key technology for this organization (Witten et al., 2013). The members of the organization are the users  of the project.  Te major requirement and the expectation of all the users of the project is to mitigate and reduce the risk of finance in the project with the help of data mining and to achieve the goals of the business.

Output: Business Objectives

AIH will be offering any type of financial help to the students for their education. They would be developing their programs for financing with all of their existing and remaining institutions. The main point of this organization is that AIH would not utilize any government sponsored program (Romero and Ventura 2013). They would be becoming the major guarantor of all the funds and would be sourcing all the funds from their existing and remaining financial institutions. The goals and objectives of AIH are as follows:

  1. i) Increasing students enrolment in the organization.
  2. ii) Providing help with the fees of education of the students.

iii) Providing help in the expenses for the daily requirements of their students.

  1. iv) Providing help to their students for studying more and working less during the time of their semesters (Witten et al., 2013).
  2. v) The fifth objective of the organization is that they would be encouraging every low income earners, holidaymakers, potential groups, professionals, rural residents and the international students for continuing with their education with worrying about the expenses.
  3. vi) The final objective is to perform any type of financial management within the organization.

Output: Business Success Criteria

The success criteria for the business for this particular organization are as follows:

  1. i) Financial Management: The financial management is the most important criteria of any organization (Liu and Motoda 2012). This is important for AIH as they are not taking any help from the government.  They are sponsoring their project with the organizational funds. This is also considered as their business objectives.
  2. ii) Product Management: The second major criteria for the success in the business of AIH are the product management. For this particular project, the product is the service (Larose 2014). The service that is provided to the students by AIH is the product and this is to be managed with utmost seriousness and so that the students would be getting benefitted easily.

The above mentioned success criteria of the business are mainly assessed by the top level of management of a particular organization.

1.2 Assess Situation

Activities: Inventory of Resources

The resources for any project are the mixture of the software, data, computing resources and personnel (Larose 2014). The details of these resources are given below in a tabular form

Personnel Details

Several Data

Several Computing Resources

Different Software

1. Management bodies

1. Access to Live Warehoused

1. Processor

1. Several Data Mining tools like MATLAB, , Rattle, StatSoft Statistica

2. Data Miner

2. Fixed Extracts

2. Monitor

2. Operating system

3. Technical Team

3. Operational Data

3. Keyboard

3. MS SQL Server

Activities: Sources of Data and Knowledge

A data miner performs the process of data mining. He analyzes or evaluates huge amounts of data. The data that is analyzed is usually collected from several sources. For the case study of AIH, the data that are absolutely relating to the project must be collected from all the written documentations (Romero and Ventura 2013). Moreover, there are certain risks and issues in this project, relating to the finance. The several sources of knowledge for this particular project must be the experts of finance as they have the ability to guide in any financial risk.

There are few tools and techniques that help in the process of data mining. Almost all of them are available plenty in the market. The business analysts or data miner of this particular project can opt for data mining tools like StatSoft Statistica, MATLAB and Rattle (Shmueli and Lichtendahl Jr  2017). MATLAB would be the best data mining tool for this project. It is nothing but a programming language that allows all types of functions like manipulations of matrix, implementation of algorithm and plotting of data.

Activities: Requirements, Assumptions and Constraints

There are few important requirements for this particular project for the schedule of completion. They are as follows:

  1. i) Any sort of delay will not be tolerated.
  2. ii) The project must be completed within the given five months of time.

The basic requirements for the quality of the results of the project are as follows:

  1. i) There will not be any degradation in the quality of the project (Siemens and d Baker 2012).
  2. ii) As they are dealing with education sector, the career of the students should be their first priority.

The basic requirements for security are as follows:

  1. i) Security of the students should be the first priority.
  2. ii) The requirement is the security of the organizational employees.

The basic requirements for the issues of legal are as follows:

  1. i) Strict laws and regulations must be present in the project (Freitas 2013).
  2. ii) Violation of laws and regulations should be penalized.

It is assumed that every individual associated with the project will follow the mentioned requirements.

There are several assumptions of any project. They define the guidelines for the probable outcome of the project (Shmueli and Lichtendahl Jr  2017). The assumptions for the case study of AIH are as follows:

  1. a) Representation of Data: The results of a particular project comes out to be perfect if the data is represented well.
  2. ii) Utilization of Data: The utilization of data is the second assumption of this project. It must be used properly.
  3. c) Output as Planned: The output of the project must be as per planning is done.

It is assumed that the output of the project will be extremely beneficial for AIH (Bhardwaj and Pal 2012). In spite of all there, there are certain limitations and constraints within this project. They are as follows:

  1. i) Lack in the Resources: As it is dealing with the career of the students, no limitation in the resources is tolerable. Any type of limitations in the resources, can stop the data completely.
  2. ii) Issues in Ethics: Ethical issue is another major limitation for this project. Education sector must be ethical at every aspect.
  3. ii) Issues in Legal Sector: The final limitation for this particular project is the issue in legal sector (Witten et al., 2013). Any type of violation in the laws and regulations will be considered as illegal.

The various sorts of risks in an organization include organizational, business, data and technical risks.

Business Risks: Competition between two organizations is the source of this risk. It occurs when both of them are producing similar services. It can be reduced with the help of strategies.

Organizational Risks: Lacking in the amount of skilled workers is the main risk. The second type is with the department that do not provide funding (Bhardwaj and Pal 2012). It takes place when there is less amount of skilled employees. It can be eliminated by hiring employees who have experience.

Data Risks: It includes all types of the sources of the data in details (Freitas 2013). It takes place when the data is retrieved and can be reduced by having security approaches in the organization.

Financial Risks: This includes the major lacking in the funding of the project and the department will give more funds after observing the initial result (Liu and Motoda 2012). It takes place when the management does not approve more funds required for the project and can be reduced by taking help from the experts in finance.

Technical Risks: It includes all the types of issues and problems in the technical assets. It can be reduced by the help of the technical team of the organization.

1.3 Data Mining Goals

Task: Determination of Data Mining Goals

The data mining goals state about the objectives of a particular project in terms of technology (Witten et al., 2013). The possible output of this project allows al the objectives that are needed to be achieved.

Activities: Translation of Business Questions to Data Mining Goals

The department of finance team of the organization can be sub divided into the guidelines that are undertaken from the collected data and from the experts in finance.

Various problems of the process of data mining are segmented in three distinct parts (Liu and Motoda 2012). They are as follows:

  1. i) Mining Methodology and User Interaction
  2. ii) Issues in Performance

iii) Issues in Diverse Data Types 

References

Baker, R.S. and Inventado, P.S., 2014. Educational data mining and learning analytics. In Learning analytics (pp. 61-75). Springer New York.

Bhardwaj, B.K. and Pal, S., 2012. Data Mining: A prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418.

Braha, D. ed., 2013. Data mining for design and manufacturing: methods and applications (Vol. 3). Springer Science & Business Media.

Freitas, A.A., 2013. Data mining and knowledge discovery with evolutionary algorithms. Springer Science & Business Media.

Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V. and Namburu, R. eds., 2013. Data mining for scientific and engineering applications (Vol. 2). Springer Science & Business Media.

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

Liao, S.H., Chu, P.H. and Hsiao, P.Y., 2012. Data mining techniques and applications–A decade review from 2000 to 2011. Expert systems with applications, 39(12), pp.11303-11311.

Liu, H. and Motoda, H., 2012. Feature selection for knowledge discovery and data mining (Vol. 454). Springer Science & Business Media.

Romero, C. and Ventura, S., 2013. Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), pp.12-27.

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

Siemens, G. and d Baker, R.S., 2012, April. Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.

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

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