Deployment Of Enterprise Data Architecture: Summative Assessment

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 assessment, you will be writing a two section report about the use of big data and cloud computing within different contexts. In your first section, you will explore proposing a cloud-based big data system in the context of a scenario based around a fictious insurance company, Webb’s of Cardiff, and their big data project, Thingsure. In the second section, you will appraise the use of cloud computing to achieve a big data use case within the context of your own organisation (3000 words, 100% of module mark, Covering LOs 1, 2 and 3).

2). Assessment Structure

2.1). Thingsure Scenario

For section 1 of the assessment, envisage you have been seconded as a big data system consultant to an insurer, Webb’s of Cardiff (often shortened to 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 high-level goals for 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 through more accurate prediction of risk and improved use of demand-driven staffing levels

3. Reduce the likelihood of insurance fraud by improving the quality and consistency of data used during claims

The key objectives for the project are:

1. The IoT device data can be analysed at regular monthly intervals to improve the predictive accuracy of existing risk models

2. The IoT device data can be used during a claims process to validate or discredit claims

3. The IoT device data can be used to immediately inform management of significant surges in incidents that may lead to an increased numbers of claims to be processed

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 (with an estimated 4 million active users who are also current customers of Webb’s) and Brrring (with an estimated 0.5 million active users who are also current customers of Webb’s).

MoniMotor is an automotive device manufacturer of a 1080p (1920 by 1080 pixels resolution) dashcam, which is 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 for 60 seconds 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 system. Webb’s will receive telematics data and incident notifications for users as a continuous stream directly from the IoT devices and can request video clips at specific times from the devices on-demand.

Brrring is a smart doorbell device manufacturer, allowing householders to remotely monitor their property entrance(s), receive alerts about activity, and respond to callers at their doors. The Brrring device includes a 1440p camera (2560 by 1440 pixels resolution) and microphone, which are activated whenever motion is detected or when the doorbell is pressed – producing on average between 10-50 GB of data a month per device, depending on utilisation – and the data are 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.

This data is stored within a key-value NoSQL database in the cloud and Webb’s will be sent a weekly batch export of its raw content.

Webb’s have recruited you as a big data consultant to analyse of how these goals could be met and to propose a system architecture to do so. They believe their existing aging IT infrastructure would not be capable of meeting the needs of this project and wanted an external perspective to help

them understand how a system managed by a public cloud service provider could be used, as well as the potential costs involved.

Note: Your proposed system should use one (or more) cloud service providers’ services – with the selection of CSP(s) selection left at your discretion, based upon your own preferences and experience – its choice does not require motivation or justification. In contrast, the choice of specific services is expected to be clearly justified.

2.2). Summative Assessment

Your summative submission (a single file, 3000 word limit) will be a two section written report, aimed at a reader familiar with the above scenario.

• In the first section, based on the above scenario and its big data requirements, you should identify and evaluate relevant cloud-based big data services, propose a cloud-based architecture using (some of) those services, and analyse how effectively the proposed services and architecture could meet those big data requirements.

• In the second section, within the context of your organisation, you should identify a potential big data opportunity and appraise the key issues that would be involved with doing it and the use of cloud-based systems to achieve it.

This report should be clearly articulated within the relevant contexts, making clear and coherent arguments, and you should use a range of appropriate academic sources to support your arguments.