Analyze Methods of Auditing Data Holdings and Gap Identification - IT Assignment Help

Assignment Task:

Learning Outcomes (LO):

After completing the module, you should be able to:

1. Analyze 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.

Task 1

Analyse the key sources and flows of data that are collected, processed, stored and taken into account in the organisation’s decision-making process. Your analysis must include:

• A statement of all the current financial and non-financial data the organisation uses in its decision making;

• An analysis of data integrity and identified gaps;

• A map between business functions and data sources.

Task 2

Based on the outcomes of your response to Question 1, propose how decision making in the organisation can be streamlined and better informed based on (a) additional analysis of the currently observed data, and (b) any extra types of data that can be retrieved, stored and processed for augmenting the currently available information. Your response must include:

• Description of data flows between the organization and all of its stakeholders identified and ranked in terms of their relevance to strategic, tactical, and operational decision making;

• An approach to the improvement in data integrity of the proposed data analytics framework;

• Data protection and ethical assurance requirements.

Task 3

Critically analyze a specific decision the organization currently needs to make, and how big data can be expected to improve its quality. Your response must include: • A statement of major strategic decisions and how the current financial and nonfinancial data are currently used to inform the respective decision making;

• Analysis of a particular strategic decision that needs to be made within the organization based on data analytics, and the critical importance of this decision for the organizational competitive advantage or performance improvement;

• Identification of a specific data set to be analyzed for supporting the stated decision. The assignment must utilize a large data set obtained from the chosen organization. 

Task 4

Discuss the specific large dataset discussed in Task 3. Analyse this dataset in terms of:

• The business-related information the dataset represents;

• An explanation of how it has been obtained and what steps are taken for data filtering, cleaning and preparation;

• An analysis of how representative the data set is, and potential limitations associated with its application. (500 words) (10 marks) (LO 2, 7) Page 6 of 9 Task 5 Discuss the data mining procedures performed using Excel, SPSS and / or Weka with the dataset in question that can be used to inform decision making. Include in your discussion:

• Statements of immediate observations of business performance using descriptive data analysis;

• An organizational forecast report based on inferential data analysis and / or application of machine learning techniques.

• Justification and critical evaluation of specific statistical models adopted in the data mining procedures.

Task 6

Present a visualization and interpretation of results as would be expected in business and academic reporting – this interpretation must be supported extensively by tables, charts, and diagrams demonstrating how the outcomes of analysis performed in the previous task can be interpreted.

Task 7

Present recommendations for decision making based on the analysis described earlier as well as a proposal on the deployment of extra data sources potentially capable of further augmenting the organizational big data framework.

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