Big data and cloud computing

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BPP Coursework Cover Sheet

Please use the table below as your cover sheet for the 1st page of the submission. The sheet should be before the cover/title page of your submission.

Programme
Module name
Schedule Term
Student Reference Number (SRN)
Report/Assignment Title
Date of Submission (Please attach the confirmation of any extension received)


Declaration of Original Work: I hereby declare that I have read and understood BPP’s regulations on plagiarism and that this is my original work, researched, undertaken, completed and submitted in accordance with the requirements of BPP School of Technology. The word count, excluding contents table, bibliography and appendices, is ___ words. Student Reference Number: Date:
By submitting this coursework you agree to all rules and regulations of BPP regarding assessments and awards for programmes. Please note, submission is your declaration you are fit to sit. BPP University reserves the right to use all submitted work for educational purposes and may request that work be published for a wider audience. BPP School of Technology

MSc Management with Data Analytics

Big Data and Cloud Computing

Coursework Assessment Brief

Submission mode: Turnitin online access

1. Assessment Brief

This assessment brief gives you an overview of the formative and summative assessments that are part of this module. The learning outcomes below will be tested in the assessment contained in this brief.

1.1. Module Learning Outcomes (LOs)

1. Design an architecture which supports the collection of complex data sets 2. Critically evaluate a range of data storage solutions from an enterprise systems perspective 3. Critically appraise the issues involved in the deployment of enterprise systems

1.2. Assessment Overview

In this assignment you will be writing a report about the use of big data and cloud computing in the context of a scenario based around a fictious insurance company, Webb’s of Cardiff, and their big data project, Thingsure.

In your report, you will propose a cloud-based architecture to enable the collection, storage, and analysis of big data, evaluate a range of relevant cloud-based solutions on their ability to meet the scenario’s requirements, and appraise the issues that would be involved with their deployment (3000 words, 85% of module mark, Covering LOs 1, 2 and 3).

Throughout your report, you should:

• Demonstrate appropriate academic skills (including clear structure, well-reasoned judgements, intellectual originality, and coherence)

• Use references to support your arguments, using the required Harvard format • Utilise personal research skills to find additional sources and critically evaluate their ability to support your findings

These aspects will be assessed over the entirety of your report (15% of module mark). Please note: ensure you read the general assessment guidance at the end of this document.

2. Assessment Guidance

2.1. Scenario

You have been employed as a big data solution consultant to an insurer, Webb’s of Cardiff (or simply Webb’s). This well-established company is currently undergoing a digital transformation to replace their aging IT infrastructure and become a more data-driven organisation. As part of this process, they have initiated the Thingsure project within their research and development (R&D) team. The project aims to offer new and existing insurance customers’ the ability to connect various Internet of Things (IoT) devices to their insurance account, enabling Webb’s to collect relevant data from them, in return for a potential discount on their insurance premiums.

The key goals of the Thingsure project are to:

1. Provide their 10 million+ customers worldwide with competitive pricing, to reduce customer churn and attract new business

2. Achieve more consistent profit margins

3. Reduce the likelihood of insurance fraud

The key objectives for the project are:

a) The IoT device data can be analysed at regular intervals to improve the predictive accuracy of existing risk models

b) The IoT device data can be used during the claims process to validate or discredit claims.

Webb’s currently offers a wide range of insurance products, including life, vehicle, health, travel, and home insurance. These products currently use self-reported data and claim histories of their customers to predict the risk of future claims, and hence price their product on a per customer basis. However, self-reported data is often limited in scope and relies in part on honesty from the consumer, and claim histories are often incomplete, or claims are too infrequent but significant in value when they do occur. Webb’s has initially engaged two IoT technology partners for Thingsure: MoniMotor, and Brrring.

MoniMotor is an automotive device manufacturer of a 1080p dashcam, which can also be connected to the car’s internal systems to monitor various driving telematics data, including speed, acceleration, and GPS location. The device also attempts to detect incidents (e.g., sudden breaking, sharp turns, potential collisions), and records and stores video data after such occurrences, regardless of whether the vehicle is powered or in motion. The video data is stored on removeable memory on the device, whilst the telematics data is immediately transmitted to MoniMotor’s on premises web application programming interface (API). Webb’s receives telematics data and incident notifications for users as a continuous stream (i.e., WebSockets) from the API, and can request video clips at specific times.

Brrring is a smart doorbell device manufacturer, allowing householders to remotely monitor their property entrance, receive alerts about activity, and respond to callers at their door. The Brrring device includes a QVGA (320×240 resolution) camera, which will be activated whenever movement is detected or when the doorbell is pressed, and its video data is streamed to Brrring’s cloud-hosted infrastructure. Users can connect to Brrring’s service to see their collected video data and will receive notifications about interactions via their preferred messaging service. The data is stored within a NoSQL key-value database in the cloud and Webb’s R&D team has been granted access to it.

Whilst the Chief Technology Officer (CTO) and many other members of Webb’s executive board are supportive of the Thingsure project, some have expressed uneasiness about it, including the Chief Information Security Officer (CISO), Chief Financial Officer (CFO), and Chief Reputation Officer (CRO). They have highlighted prior examples of big data project failures, including data breaches and possible high costs and poor returns on the investment, and have requested a written briefing about the relevance of any such issues for this project and their potential impact on its viability.

2.2. Formative Assessment

Your formative submission (a single file, 1000 words) will be a draft of your summative assessment. At a minimum, it should outline the following in the context of the above scenario:

• The big data requirements you have identified for the Thingsure project

• The relevant solutions that you intend to evaluate in your report, which of those you include in your proposal, and why you have chosen them

• Any relevant issues you have identified in the deployment of cloud services and big data for this specific use case

2.3. Summative Assessment

Your summative submission (a single file, 3000 words) will be a written report, aimed at senior management within Webb’s, which identifies and evaluates relevant cloud-based big data solutions, proposes a cloud-based architecture for the big data solution and analyses how it could meet the requirements, and appraises the key issues involved in the deployment of cloud-based big data systems for this scenario. This report should be clearly articulated within the relevant context and you should use a range of appropriate academic sources to support your arguments.

3. Assessment Structure

Your summative report (a single file, maximum 3000 words) should include:

• A completed cover sheet (not included in word count)

• Introduction (approx. 300 words): Set out your report and assumptions you have made about the scenario

• Big Data Requirements & Solutions (LO2, approx. 800 words):

o Identify a range of cloud-based big data solutions and evaluate their use in the context of the scenario, considering their ability to achieve the scenario

requirements and the costs involved in their use

• Proposed System Architecture (LO1, approx. 400 words, plus relevant diagram(s)): o Propose a cloud-based solution architecture for the big data scenario, selecting appropriate cloud-based big data solutions, using appropriate modelling tools and visualisations to present the structure of your proposed architecture, and analyse its overall capability to meet the scenario requirements

• Project Risks & Issues (LO3, approx. 1200 words):

o Identify and critically appraise a range of issues that may be associated with deploying a cloud-based big data solution for this scenario, and analyse any potential mitigation approaches that might be relevant for the issues you identified

• Conclusion (approx. 300 words): Based on your findings, propose a route forward • References (not included in word count): highlighting the academic research undertaken in your project

• Appendices (not included in word count): these are not directly marked, but may support your report

4. Marking Criteria

Criterion

0-29% 30-39%

Fail

40-49%

Low Fail

50-59%

Pass

60-69%

Merit

70-79% 80-100%

Distinction

)

(

Critically

evaluate a

range of data storage

solutions from an enterprise systems

perspective

Inadequate and often

implicit knowledge base

with some omissions and/or lack of theory of big data storage solutions and its ethical dimension.

No clear identification of relevant big data solutions.

Weak and often implicit knowledge base with some omissions and/or lack of theory of big data storage solutions and its ethical

dimension.

Some generic big data

solutions identified, but

lacking contextualisation or clear understanding of

relevance.

Limited and increasingly explicit knowledge base that begins to explore and analyse the theory and

ethical issues of big data storage solutions.

Identifies a limited range of big data solutions, with

some attempt at

justification for their

selection (i.e., capacity).

Satisfactory knowledge base; explores and explicitly

analyses the discipline, its theory and ethical issues with some originality, detail, and autonomy.

Identifies a limited range of relevant big data solutions, clearly justifying their

consideration for the scenario (i.e., capacity, functionality).

Good knowledge base,

exploring and analysing big data storage solutions, its theory and ethical issues with considerable

originality and autonomy.

Identifies a range of

relevant big data solutions, evaluating their selection for the scenario (i.e.,

capacity, functionality,

costs).

Excellent information and

knowledge base which deeply explores and analyses big data storage solutions, its theory and ethical issues with clear originality and autonomy.

Explores a range of relevant big data solutions, evaluating their selection for the scenario (i.e., capacity, functionality, costs).

Outstanding information and knowledge base which deeply and extensively explores,

critiques, and analyses big data storage solutions, its theory and ethical issues with clear

originality, innovation, and

autonomy.

Explores a broad range of

relevant big data solutions,

critically evaluating their

selection for the scenario (i.e., capacity, functionality, costs).

Design an

architecture

which supports the collection of complex

data sets

Inadequate introduction to a basic appreciation of

designing cloud-based big data solutions with little or no clarity and precision to the thoughts and practices related to the required

discipline indicated.

No clear identification of a realistic solution capable of any storage and processing requirements.

Weak introduction to a

basic appreciation of

designing cloud-based big data solutions with some clarity and precision to the thoughts and practices

related to the required

discipline indicated.

An attempt at a design for a cloud-based solution, but one that is unlikely to be effective, or not clearly

documented.

Limited knowledge base;

Some appreciation of a

basic design for a cloud

based big data solution with clarity and precision to the thoughts and practices

related to the required

discipline indicated.

A design for a cloud-based solution is outlined, which has the potential meet

some relevant storage and processing requirements.

Satisfactory appreciation of and explicit links to a design for a cloud-based big data solution.

Emerging application of

thoughts and practices at the forefront of the discipline.

A clearly documented cloud based solution, which seems likely to be able to meet a range of relevant storage and processing requirements.

Good and clear

understanding of, and

explicit links to, some

aspects of a design for a cloud-based big data

solution.

Application of current and emerging thoughts and practices from the

discipline.

A clearly documented

cloud-based solution,

which would very likely meet a range of relevant storage and processing

requirements, with

consideration of its

effectiveness.

Thorough and deep knowledge and understanding of

designing cloud-based big data solutions and explicit evidence of the wider contexts of the topic with coherence and the ability to synthesise

appropriate principles by

reference to appropriate

primary sources.

Excellent and detailed usage of recent emerging thought at the forefront of the discipline and/or practices from a range of appropriate disciplines.

An effectively documented cloud-based solution, with a clear analysis of how it could meet a broad range of storage and processing requirements.

Thorough, balanced, and deep knowledge and understanding of designing cloud-based big data solutions and explicit

evidence of the wider contexts of the topic with coherence and the ability to synthesise

appropriate principles by

reference to appropriate

primary sources with no areas of weakness.

Outstanding and extensive usage of recent emerging

thought at the forefront of the discipline and/or practices from a range of appropriate

disciplines.

An effectively documented cloud-based solution, using state-of-the-art approaches, with critical analysis of how it could meet or exceed a broad range of storage and processing requirements and its associated costs.

Criterion

0-29% 30-39%

Fail

40-49%

Low Fail

50-59%

Pass

60-69%

Merit

70-79% 80-100%

Distinction

)

(

Critically

appraise the

issues involved in the

deployment of enterprise

systems

Inadequate introduction to a basic appreciation of the benefits and issues, with little or no clarity and

precision to the thoughts and practices related to the required discipline

indicated.

No clear appraisal of

relevant benefits and risks of a cloud-based big data solutions.

Weak introduction to a

basic appreciation of the benefits and issues, with some clarity and precision to the thoughts and

practices related to the

required discipline

indicated.

Some generic issues for a cloud-based big data

solution are identified but lack clear appraisal and

contextualisation.

Limited knowledge of the benefits and issues;

Some appreciation of a

basic wider field with clarity and precision to the

thoughts and practices

related to the required

discipline indicated.

Several issues for a cloud based big data solution are identified but with limited appraisal or

contextualisation.

Satisfactory appreciation of and explicit links to the

benefits and issues.

Emerging application of

thoughts and practices at the forefront of the discipline.

Several relevant issues for a cloud-based big data solution are identified and appraised, with some contextualisation.

Good and clear

understanding of, and

explicit links to, some

aspects of the benefits and issues.

Application of current and emerging thoughts and practices from the

discipline.

A range of relevant issues for a cloud-based big data solution are effectively

appraised in the

organisational context.

Thorough and deep knowledge and understanding of the

benefits, issues, and

mitigations and explicit

evidence of the wider contexts of the topic with coherence and the ability to synthesise appropriate principles by

reference to appropriate

primary sources.

Excellent and detailed usage of recent emerging thought at the forefront of the discipline and/or practices from a range of appropriate disciplines.

A deeply contextualised

appraisal of a diverse range of relevant issues for a cloud based big data solution, with discussion of appropriate

mitigations.

Thorough, balanced, and deep knowledge and understanding of the benefits, issues, and

mitigations and explicit

evidence of the wider contexts of the topic with coherence and the ability to synthesise

appropriate principles by

reference to appropriate

primary sources with no areas of weakness.

Outstanding and extensive usage of recent emerging

thought at the forefront of the discipline and/or practices from a range of appropriate

disciplines.

A deeply contextualised

appraisal of a diverse range of relevant issues of a cloud-based big data solution, with analysis of effective mitigations.

)

(

Academic skills

Inadequate academic/

intellectual skills with some difficulties. Largely imitative and descriptive. Some

difficulty with structure and accuracy in expression but developing

practical/professional skills.

Weak academic/intellectual skills with few difficulties. Largely original work with some evidence of reflection and critique. Structure and accuracy in expression

beginning to emerge.

Limited

academic/intellectual skills. Original work with personal reflection and broad

evidence-based critique. Solid structure and accuracy in expression.

Practical/professional skills evident.

Satisfactory

academic/intellectual skills. Wholly original work with good reflection and solid,

well-reasoned judgements forming from evidence-based critique. Consistent structure and accuracy in expression. Practical/professional skills established.

Good

academic/intellectual skills. Demonstrates intellectual originality and imagination

Excellent

academic/intellectual skills. Demonstrates intellectual

originality, integrity,

coherence, and imagination.

Outstanding

academic/intellectual skills. Demonstrates intellectual

originality, integrity, coherence, creativity, and imagination

working consistently in the

higher cognitive domains to a professional standard.

Referencing

Inadequate references and notes but may contain

inconsistencies, errors, or omissions.

Weak references and notes with minor or insignificant errors or omissions.

Limited and full and

appropriate references and notes with minor or

insignificant errors

Satisfactory with precise, full, and appropriate references and notes.

Good with precise, full,

and appropriate references and notes at a high

standard.

Excellent with precise, full, and appropriate references and notes at near-publishing standard.

Outstanding with precise, full, and appropriate references and notes at publishing standard.

Personal

research skills

Inadequate use of a range of personal research which is largely critically evaluated for key conceptual issues although this may not be consistent throughout.

Weak use of a wide range of personal research which is critically evaluated for key conceptual issues and is largely consistent

throughout.

Limited, clear evidence of considerable personal

research and the use of a diverse range of

appropriate sources but may contain problems with consistency in the

conceptual evaluation.

Satisfactory and substantial research and evidence of an innovative use of a wide range of personal research with clear and consistent

conceptual evaluation.

Good evidence of an

innovative or original use of extensive personal

research which has been thoroughly evaluated

conceptually.

Excellent evidence of an

innovative or original use of extensive personal research which has been thoroughly critically evaluated both

conceptually and

methodologically.

Outstanding evidence of an innovative and original use of extensive personal research which has been thoroughly critically evaluated, conceptually and methodologically with deep reflection.

5. General Assessment Guidance

• You are required to submit all elements of your assessment via Turnitin online access. Only submissions made via the specified mode will be accepted and hard copies or any other digital form of submissions (like via email or pen drive etc.) will not be accepted.

• For coursework, the submission word limit is 3000 words. You must comply with the word count guidelines. You may submit LESS than 3000 words but not more.

• Please ensure your student registration number is on your front cover sheet • You are required to achieve minimum 50% to pass this module and must address all learning outcomes.

• You are required to use only Harvard Referencing System in your submission. Any content which is already published by other author(s) and is not referenced will be considered as a case of plagiarism.

• You can find further information on Harvard Referencing in the online library on the VLE. You can use the following link to access this information:

• BPP University has a strict policy regarding authenticity of assessments. In proven instances of plagiarism or collusion students will go through the malpractice process. You are advised to read the rules and regulations regarding plagiarism and collusion in the GARs and MOPPs which are available on the HUB.

• You should include a completed copy of the Assignment Cover sheet. Any submission without this completed Assignment Cover sheet may be considered invalid and not marked. • Please note that any work submitted in the appendices is for information only and is NOT marked. You should ensure you hit all requirements and learning outcomes within the word count set.