Reference no: EM132711679
DAT7001 Data Handling and Decision Making - Arden University
Part 1: Essay
Assignment Brief
As part of the formal assessment for the programme you are required to submit a Data Handling and Decision Making essay. Please refer to your Student Handbook for full details of the programme assessment scheme and general information on preparing and submitting assignments.
Learning Outcomes (LO):
After completing the module, you should be able to:
1. Analyse methods of auditing data holdings and gap identification.
2. Critically analyse theoretical and core processes of data manipulation and mining.
3. Utilise and evaluate basic statistical concepts.
4. Appreciate ethical issues and their importance to data handling and decision making.
5. Develop a practical ability with data analysis and data mining methods to analyse and interpret data sets.
6. Make recommendations based upon the findings from data analysis.
7. Graduate Attribute - Effective Communication
Communicate effectively both, verbally and in writing, using a range of media widely used in relevant professional context.
Maximum word count: 1,000 words
Assignment Task - Essay
Assignment Part 1: Data Gap Analysis
Data gap analysis can be referred to as the process of inspecting an existing or planned big data infrastructure with the aim of identifying issues, risks and inefficiencies associated with the use of data in organisation's operations. Such analysis requires an integrated view on technical, managerial and legal aspects of organisational data. This activity represents a key initial step towards implementation of data-driven business decision-making.
For this assignment, you are required to demonstrate the data gap analysis for a case study of your choice.
Case study choice. You are encouraged to relate this assignment to your workplace, so that the outcomes can immediately be applied to improving its data analytics processes.
However, if you have no immediate workplace to analyse or its use for the assignment purposes is not possible, then you have an option of adopting another project (such as a commercial start-up, community project or social enterprise) which would take advantage of data driven decision-making. Either an existing or prospective project can be discussed. In the latter case, the data infrastructure might not exist yet, however, you have an opportunity to propose its design and analyse it for any potential gaps.
NB1: You can use any publicly available datasets, e.g. historical data or national statistics (a sample list of public data sources can be found on the News and FAQs forum of the module). However, these datasets must be applied to in-depth analysis of your workplace or another specific project or activity which you are fully familiar with. Generic analysis of any third- party company is not acceptable for this assignment.
NB2: Organisation name, other business or stakeholder particulars and any personally identifiable information can be fully anonymised.
NB3: If you are re-sitting this assessment for the first time, you may re-work your original submission if you wish. However, if this is your third attempt at this assessment, you must submit a piece of work which is substantially different from your first two attempts.
Task 1.1
Perform data gap analysis for an organisation or project of your choice. Your response should include:
• Brief background to the organisation or project in question.
• Identification of the key data sources and datasets available to the organisation.
• Inspection of data integrity and current or potential gaps in data analytics and data protection.
Task 1.2
Using the findings of Task 1.1, recommend improvements to the organisational data analytics processes. These should be centred around the following:
• Reorganisation of the current data-driven processes to streamline and enhance the data analytics and decision making.
• Roadmap to the development or enhancement of the big data infrastructure.
• Compliance aspects of the proposed changes in data analytics.
Task 1.3
Explain how the proposed big data analytics can be used in the organisational decision making. This includes the following:
• Identification of a range of business decisions that can be supported by the enhancements in data analytics proposed in Task 1.2.
• Formulation of a single decision of your choice out of those identified, in terms of the related business question to be solved, involved stakeholders and data available for its support. (You will then be required to analyse this decision systematically in Part 2 of this Assignment.)
Part 2: Report
Assignment Brief
As part of the formal assessment for the programme you are required to submit a Data Handling and Decision Making report. Please refer to your Student Handbook for full details of the programme assessment scheme and general information on preparing and submitting assignments.
Learning Outcomes (LO):
After completing the module, you should be able to:
1. Analyse methods of auditing data holdings and gap identification.
2. Critically analyse theoretical and core processes of data manipulation and mining.
3. Utilise and evaluate basic statistical concepts.
4. Appreciate ethical issues and their importance to data handling and decision making.
5. Develop a practical ability with data analysis and data mining methods to analyse and interpret data sets.
6. Make recommendations based upon the findings from data analysis.
7. Graduate Attribute - Effective Communication
Communicate effectively both, verbally and in writing, using a range of media widely used in relevant professional context.
Maximum word count: 4,000 words
Assignment Task - Report
Assignment Part 2: Data-Driven Decision Support
Data-driven decision support encompasses a range of the most essential processes of data analytics, including data preparation and integration, modelling using statistical and/or machine learning techniques, and data presentation. The aim of this activity is to empower the organisational decision-making with statistically tested and systematically evaluated decision options. These options can be ranked using inferential models, such as forecasting, prediction and/or classification.
In Part 1 of this assignment, you have identified a case study - an organisation or project of your choice, as well as various data sources and datasets available to it, and an important organisational decision. In this part, you have an opportunity to demonstrate how this specific decision formulated in Part 1 in the context of your chosen case study can be supported with data analytics.
Task 2.1
Discuss data preparation process, including
• Explanation of data collection, filtering and integration procedures.
• Analysis of data representativeness.
• Statement on generalisability and limitations of the integrated dataset.
Task 2.2
Perform data modelling, which should specifically demonstrate:
• Selection and justification of the inferential and/or machine learning models, most relevant to the objectives of your case study.
• Application of statistical tools such as Excel, SPSS and/or Weka, to your model and reporting on the initial outcomes of your modelling.
• Explanation what the decision in question should be, based on these outcomes.
Task 2.3
Present further outcomes, in support of the decision obtained in Task 2.2. This discussion should be visualised with a range of charts and tables showing the identified and analysed relationships. It should also be accompanied with detailed interpretation of the demonstrated results.
Task 2.4
Propose recommendations on the implementation, acceptance and assessment of the decision arrived at in Tasks 2.2 and 2.3, and discuss how this decision can contribute to strategic management of the chosen organisation or project.