DAT7001 Data Handling and Decision Making Assignment Sample
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.
Date for Submission: Please refer to the timetable on ilearn
(The submission portal on iLearn will close at 14:00 UK time on the date of submission)
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. Utilizes 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
Please note that exceeding the word count by over 10% will result in a reduction in grade by
the same percentage that the word count is exceeded.
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 organization’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 assignment help
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 analyzed 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 analyze it for any potential gaps.
Please turn over for the questions
Perform data gap analysis for an organization or project of your choice. Your response should include:
• Brief background to the organization or project in question.
• Identification of the key data sources and datasets available to the organization.
• Inspection of data integrity and current or potential gaps in data analytics and data protection.
Using the findings of Task 1.1, recommend improvements to the organisational data analytics processes. These should be centered around the following:
• Reorganization 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.
Explain how the proposed big data analytics can be used in the organizational 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 analyze this decision systematically in Part 2 of this Assignment.)
Assignments submitted late will not be accepted and will be marked as a 0% fail.
Your assessment should be submitted as a single Word (MS Word) or PDF file. For more information, please see the “Guide to Submitting an Assignment” document available on the module page on iLearn.
You must ensure that the submitted assignment is all your own work and that all sources used are correctly attributed. Penalties apply to assignments which show evidence of academic unfair practice.
The subsidiary company of Google is Alphabet Inc. which was renamed in 2015. Alphabet Inc. allowed the expansion of Google into domains outside of the internet search. Alphabet advertises Google's services to become a technology conglomerate. The company is working on a big breakthrough and providing super-fast internet services (CNN 2021). Alphabet invests in long-term technological trends. The company manages the data of a huge range of consumers, which aims to improve the security of data.
Alphabet manages the data of six subsidiaries of Google, where the information related to the browsing history of consumers, location data, and business intelligence data are sourced. The shares of Google are transferred to the Alphabet stock, where the information related to the new company trades are specific datasets. Alphabet also researches health data where the focus is mainly given up on the management of Google investment (Franek 2019). Around 40 subsidiaries are related to investment, where Google LLC is the core part. However, the company also manages the data related to stock prices, press releases, company news, contact information, and board member executives. The unprecedented mass of data of the company's consumers makes it more ambitious in resource management.
Investigation of a current potential gap in data analytics and data protection
Alphabet Inc. integrates the data of consumers for promoting security and privacy of data by managing the network integrity. In the track records of Alphabet Inc., an unfair and deceptive practice related to data privacy and data security has been discovered. It has highlighted a data gap between the FFC's evident lack of remedial security and data privacy authority and Google's evident security and data privacy recidivism (Cleland 2018). The data gap analysis shows that FTC does not enforce any authoritative power to deter Google Alphabet. Around 17 pieces of evidence of major business practices have been discovered to support the argument. The personal data of consumers show an evident gap where the information of consumers is not secured. The data gap shows that the company has seriously harmed consumer welfare.
Reorganization of current data-driven processes
The company Google Alphabet Inc. needs to promote the security of data where FTC has to reinvestigate the privacy and reinforce data security authority from the Congress to identify the company's rampant recidivist deceptive privacy and security practices. To promote security to the consumer data, the company has to build strong resilience in the internet platforms. Proper implementation of the data analytics process for decision-making regarding the security and privacy of data is necessary (Karthiban and Raj 2019, p. 130). The company uses Web Index to match the potential queries and to produce results where the data from trusted sources are driven. However, the accuracy in the machine learning process helps the company to manage reliability. Google Alphabet Inc. can manage the security issues by promoting data analytics like AI or block chain technology which will help to manage the data security of consumers by protecting the data rights. Operationalizing through XOps to create business value can help better decision-making and make analytics an integral part of security. Data-driven creatives may help the company in the decision-making process. Big data analytics helps monitor data breaches to ensure data security (Tang, Alazab, and Luo 2017, p. 318). For managing the information security of consumers, the company has to speed up the recovery process of data breaching and automate the big data analytics process to prevent further data privacy issues. Operating data and analytics processes as a core business function and promoting engineered decision insights may help the company manage data security. However, the FTC has to track the record of the company's data management process to analyze the gap and support the company to maintain data privacy.
Roadmap to the development of big data infrastructure
Compliance aspects of proposed changes in data analytics
The data security compliance regulation related to the company's data gap issue may help the company to achieve security, integrity, and availability of sensitive data and information systems. The GDPR helps protect personal data with proper legal enforcement (Blix, Elshekeil, and Laoyookhong 2018). This can help the company to manage the proper security of consumer information. The regulatory compliance in the data analytics process helps in managing the storage mechanisms and governing the respective data and semantics. However, the regulatory compliances related to security management of the information of the consumers may help to resolve the evident gap. With specific compliance analytics, the company will enforce policy for protecting the data from misuse. The FTC will help in reinforcing the security compliances to manage the data gap.
Identification of business decisions
Enhancement in the data analytics process in Google Alphabet may lead to changes in financial decisions. The organization has to plan for cost estimation for the implementation of data analytics. To improve the operational efficiencies, engineered decision intelligence will be taken in guidance. Data analytics will help in the decision-making process regarding security management (Rassam, Maarof, and Zainal 2017). In this case, the company can implement AI or block chain for maintaining the privacy of data of the consumers. However, the analytics will comply with the regulatory policies of data security management, which will help the company manage the existing data gap. However, security experts should be hired for managing the potential risks in consumer data management. The organization has to make changes in system management to promote proper access control and authentication of data. Implementation of the data analytics decision-making process will encourage the company to document relevant information regarding the system operation.
The selected decision for the organization
Implementation of data analytics tools for information security can be an effective solution for the organization. AI or block chain tool implementation in the system network will help to monitor the data and to maintain the privacy of customer information. Consumers are significant stakeholders of the organization. Security of the available data related to the stakeholders will be possible through data analytics tool implementation.
Blix, F., Elshekeil, S.A. and Laoyookhong, S., 2018. Designing GDPR Data Protection Principles in Systems Development. Journal of Internet Technology and Secured Transactions (JITST), [Online], 6(1).
Cleland, S., 2018. A Case Study of Alphabet-Google’s 2004-2018 Privacy Track Record of Evident Unfair and Deceptive Over-collection of Consumers’ Personal Data Exposes an Evident Gap in the FTC’s Remedial Authority to Protect Consumers. [online], Available from: https://www.ftc.gov/system/files/documents/public_comments/2018/07/ftc-2018-0052-d-0005-147574.pdf [Accessed 2 July 2021].
CNN, 2021. What is Google’s new Alphabet? - CNNMoney. [online] Available from: https://money.cnn.com/interactive/technology/what-is-googles-new-alphabet/index.html [Accessed 2 July 2021].
Franek, K., 2019. What Companies Google & Alphabet Own: Visuals & Full List. [online] Available from: https://www.kamilfranek.com/what-companies-alphabet-google-owns/ [Accessed 2 July 2021].
Karthiban, M.K. and Raj, J.S., 2019. Big data analytics for developing secure internet of everything. Journal of ISMAC, [Online], 1(02), 129-136.
Rossum, M.A., Maarof, M. and Zainal, A., 2017. Big Data Analytics Adoption for Cybersecurity: A Review of Current Solutions, Requirements, Challenges and Trends. Journal of Information Assurance & Security, [Online], 12(4).
Tang, M., Alazab, M. and Luo, Y., 2017. Big data for cybersecurity: Vulnerability disclosure trends and dependencies. IEEE Transactions on Big Data, [Online], 5(3), 317-329.
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