Big Data and Cloud Computing, Management with Data Analytics

Assessment Title - FarmFlow FarmOptix Cloud-based Architecture Project

Learning Outcome 1: Design an architecture which supports the collection of complex data sets

Learning Outcome 2: Critically evaluate a range of data storage solutions from an enterprise systems perspective

Learning Outcome 3: Critically appraise the issues involved in the deployment of enterprise systems

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 agricultural technology (agro tech) company, FarmFlow, and their big data project, FarmOptix.

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 (2500 words, 90% 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

Assessment Scenario
FarmFlow, a well-established player in the agricultural sector, that provides climate change and crop monitoring services to farmers in the UK, and now wishes to expand internationally to take advantage of its local success. They have employed you as a big data and cloud solution consultant as they recognise that their aging infrastructure and manual processes would hinder the planned expansion into international markets and from being a data-driven decision-making company. To address this, they've initiated the FarmOptix project within their Research and Development (R&D) team.

Project Overview

The FarmOptix project aims to leverage the benefits of big data using the Internet of Things (loT) technology with the help of technology partners to enhance crop monitoring and risk management for farmers that take their services. By connecting various loT devices to farmers' farms , FarmFlow can collect real-time data from fields, orchards, and greenhouses. In return, farmers receive discounts on machinery hired from FarmFlow, insights, recommendations, and potential cost savings advice on farming practices.

The key goals of the FarmOptix project are:
Empower Farmers: Provide actionable insights to FarmFlow's vast network of about 10 million+ prospective farmers worldwide. This will help them optimize crop yield, reduce losses, and make informed decisions on customer churn.

Steady Profit Margins: FarmFlow aims for consistent profitability by minimizing crop losses due to pests, diseases, or adverse weather conditions by providing early warning alerts to their customers.

Fraud Prevention: By validating data directly from loT devices, FarmFlow intend to reduce the risk of fraudulent claims related to crop failures.

The key objectives for the project are:

a) Data-Driven Risk Models:
FarmFlow intends to capture and analyse loT device data from farmers at regular intervals. This would improve the accuracy of risk models, allowing better prediction of crop health, yield, and potential threats.
For instance, soil moisture sensors, weather stations, and drone imagery contribute to real-time risk assessment.

b) Claims Validation:
During the claims process, FarmFlow uses loT data to validate or reject claims, for example, if a farmer reports crop failure due to extreme weather, FarmFlow can cross-references weather station data to verify their claim.
FarmFlow has engaged two specialist agro loT technology partners for the FarmOptix project: AgriMetrix and DataHarvest.
FarmFlow's loT Partners

1. CropCam by AgriMetrix:
FarmFlow collaborates with AgriMetrix, a leading loT manufacturer to provide a CropCam service, a high-resolution aerial camera system, used for capturing detailed images of crop fields. It monitors plant health, detects anomalies, and assesses overall crop conditions.

2. SmartHarvester by DataHarvest:
DataHarvest, another strategic partner, provides SmartHarvester devices, a family of solar-powered sensors. These solar-powered sensors measure soil moisture, temperature, and humidity. They help optimize irrigation schedules and prevent water wastage. All data about soil and weather conditions are to be collected and stored for processing.

Stakeholders
The Chief Information Officer (CIO) and many other members of Fa rmFlow's executive board are supportive of the FarmOptix project, however, some have expressed reservations 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.

3.2. 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 FarmOptix 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

3.3. Summative Assessment
Your summative submission (a single file, 2500 words) will be a written report, aimed at senior management within FarmFlow, 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.

Assessment Structure
Your summative report (a single file, maximum 2500 words) should include:
A completed cover sheet (not included in word count)

Task 1. Big Data Requirements & Storage Solutions (L02, approx. 1000 words) (30%): o Introduction (approx. 200 words): Set out your report and assumptions you have made about the scenario

Identify and outline the Big Data requirements from the scenario that your proposed solution will address
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

Task 2. Proposed System Architecture ((L01, approx. 500 words, plus relevant diagram(s)):
Design a system architecture diagram of your proposed system architecture showing the essential components from data source to reporting, using appropriate modelling tools and visualisations to present the structure of your proposed architecture
Propose a cloud-based solution architecture for the big data scenario underpinned by your architecture diagram, selecting appropriate cloud-based big data solutions, and analyse its overall capability to meet the scenario requirements and the storage solutions from Task 1

Task 3. Project Risks & Issues (L03, approx. 1000 words) (30%):
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, taking into consideration the requirements you and storage solutions that you outlined from Task 1 and your proposed system architecture solution in Task 2
Conclusion (approx. 200 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.