Page 1 of 7 Big Data Basics INFS 5095 2019 Student's Assignment Guide (Internal and External/Online) Assignment 2 Management Proposal Big Data Capabilities 50% 3000 words Due: Sunday, 7 July 2019,...

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Page 1 of 7 Big Data Basics INFS 5095 2019 Student's Assignment Guide (Internal and External/Online) Assignment 2 Management Proposal Big Data Capabilities 50% 3000 words Due: Sunday, 7 July 2019, 10.00pm Late assignments: 15% per day deducted Last updated: 8.5.2019 Develop a proposal for management of a nominated organisation to implement Big Data capabilities Include a high-level architecture and recommendations of which Big Data technologies and methodologies should be introduced and why. Page 2 of 7 About this Assignment This assignment is giving you practice in bringing together the knowledge you have acquired in this course, applying it to a business need and being able to communicate that. Imagine that you are presenting your proposal to the senior management team of your chosen organisation. Assume that the audience know little about big data, but they want to make better use of their data which is why you have been invited to submit a proposal. However, the assignment is not just a sales pitch – you must demonstrate that you know what you are talking about, back up your arguments with evidence, communicate new concepts and demonstrate to the audience that you would be worth engaging. You are being assessed on demonstrating your understanding and applying it, not just finding and presenting information of ‘experts’. This assignment requires you to work things out yourself as well as making use of research. Note: You are recommending what would need to be done, not actually doing it – ie. you don’t have to build any big data capacity or do big data analysis. Nominated Organisations Choose one of these: • Bunnings Hardware • McDonald’s • Salvation Army Or choose your own, but check with the lecturer first. If you choose your own, select an organisation you’re personally interested in. Please do not contact the organisation. Business priority Identify a key business priority of your chosen organisation - this shows the audience you understand their needs. You can use their strategic plan or annual report to identify this. Some priorities will be issues or threats the organisation is facing, some will be opportunities or initiatives they are pursuing. Big data is useful in both situations – specially to discover opportunities and issues the organisation isn’t currently aware of. The business priority should be significant enough to impact the organisation as a whole – to justify why the organisation should invest in big data now and in an ongoing basis. Otherwise the risk is your proposal would be seen as a once off solution to an existing opportunity or problem. See the Microsoft resources around the questions ‘Is big data the right solution?’ and ‘Determining analytical goals‘ here: Planning a big data solution https://msdn.microsoft.com/en-us/library/dn749858.aspx Examples of business priorities can be found in the ‘Big Data Fundamentals’ topics. Also assume that the chosen organisation has no big data capability currently. So, don’t research what they do actually have in place. Big data approach Outline the steps you would use to implement the big data capability. See the ‘Big data analytics approach’ in the ‘Big Data Analytics - Overview and Challenges’ presentation and the ‘Big Data Initiatives - Implementation and Case Studies’ topics (including discussions in the recordings). Keep https://msdn.microsoft.com/en-us/library/dn749858.aspx DELL Highlight DELL Highlight DELL Highlight DELL Highlight Page 3 of 7 in mind the iterative and discovery nature of big data, plus that it can be an expensive undertaking requiring many different skill sets. Information and sources Outline the information and information sources that would be needed to deliver on the big data solution. They can be described in general terms such as ‘customer sentiment from social media’. Also explain the categories of data (see ‘Big Data Analytics - Overview and Challenges’). Big data technologies Provide brief explanations of the technologies required to deliver the big data capability and an example of each one technology (eg: processing of streaming data – Apache Spark). The technology choices will depend on the data types of your information. If you wish, use the Gartner Hype Cycles to recommend particular types of technologies, but don’t focus on a specific tool or vendor (much like the first assignment). See the ‘Big Data Technologies – Techniques’ presentations. High Level Architecture – your proposal should include a diagram of a high level architecture showing the different technologies and how they fit together. Big data visualisation examples Provide two examples (screen shots) of big data visualisations to give the audience an indication of what you would be providing them (or if you had built a prototype). Explain the visualisations. If you wish, build your own visualisation and include that as one of the screenshots. The more relevant to the business priority and organisation the better. The visualisations should be clearly based on big data, not small data. Big data adoption challenges and governance Finally include recommendations for how to address the challenges of big data adoption and big data analytics. See the ‘Big Data Fundamentals - Benefits, Challenges, Management and Skills’ and ‘Big Data Technologies - Information Quality and Data Governance’ and ‘Big Data Analytics - Overview and Challenges‘ topics for ideas. The recommendations should also include recommendations for governance, dealing with quality and uncertainty. Marking criteria The assignment will be marked on how well you cover each of the points: Area Weighting Justification for big data being the solution to the business priority 10% Big data approach 20% Information and sources 10% Big data technologies 15% Big data visualisation examples 10% Big data adoption challenges and governance 15% DELL Highlight DELL Highlight DELL Highlight DELL Highlight DELL Highlight DELL Highlight Page 4 of 7 Area Weighting Referencing • Correct referencing as per UniSA guidelines • Quality of references • How recent references are 5% Use of formal business or academic language 5% Correct grammar and spelling 5% Layout and professional presentation 5% Keeping within the word limit 0.5 deducted for each 100 words over/under allowance Late marks 10% per day For these you will be given a rating of ‘Excellent’, ‘Good’, ‘Fair’, ‘Poor’ or ‘None’ (if the section is missing). As a guide, if all ratings are ‘Excellent’ you would receive a High Distinction for the assignment (between 85-100%) or if all ratings are ‘Good’ you would receive a Credit (between 65- 74%). The more you can back up your suggestions with research, examples, etc the higher mark you will receive. Common Feedback Comments This is a sample of the common feedback comments provided to students in past assignments – you can use these as a guide as well “Use of formal business or academic language • At times it wasn't clear the point you were trying to make. One suggestion is to have someone else proof read your assignment before submitting it. • Parts of your assignment were too technical for a business audience to understand. • Need to make sure to explain all technical terms when first introducing them. • You explained technical concepts well for a business audience. • Avoid use of informal words and phrases - need to be specific about what you mean Layout and professional presentation • Layout could have been improved - important to present information professionally (eg: good use of headings, spacing and fonts.). • Good and effective use of visuals/diagrams. • More use of visuals or diagrams to explain the information you were trying to convey. • Visuals/diagrams need to be less complex. • Good to use figures but it's better to explain that in detail about how it can be done in more practical ways. Page 5 of 7 Referencing • Good use of references to back up your points. • References should be from quality and reliable sources - see the Assignment Guide for more information. • Need to always include the sources of your information you have used in your assignment. In various places you've used information but not stated where it's from. • References should be more recent - see the Assignment Guide for more information. • Good use of stats to back up your points. • Reference lists should be according to UniSA standards (eg: in alphabetical order, include all references, author surname first, 'viewed..' for url sources). • In text referencing should be according to UniSA standards. • When using text direct from reference source must enclose in quotes to indicate that it's not your own words.” Feedback One on one individual feedback sessions are available (either face to face or over the phone) to received specific and detailed feedback. These sessions are 10 minutes long. Presentation/structure The structure should be in a logical format that flows well. As a minimum include a title page and section headings. The title page is separate to the assignment cover page. A sample template for the assignment is available on the course website. You don’t have to use this template; you can come up with your own structure. Note: An Executive Summary is different to an Introduction. Since this is proposal for a business audience, it should be presented in a professional format making it easy to read. The use of diagrams and graphs, particularly to show figures will earn more marks – visualisations such as infographics are growing in popularity as a way to explain complex concepts and interactions, but also to see key patterns and relationships – remember the saying “a picture is a 1000 words”. An efficient layout is also important but don’t spend too much time on making it look good and not enough time on the content. Using bullet points are OK occasionally but you'll need sentences for each point (ie. just a bullet point list with no explanation isn’t suitable). Page 6 of 7 Word limit 3000 words +/- 10%. (2700 – 3300 words) Marks will be deducted if the assignment is too short or too long. Keeping to a word limit requires a focus on what the reader most needs to know. These are included in the word count: • The 'body' of the assignment:
Answered Same DayJun 14, 2021

Answer To: Page 1 of 7 Big Data Basics INFS 5095 2019 Student's Assignment Guide (Internal and External/Online)...

Shikha answered on Jun 22 2021
137 Votes
Student Name
Student ID        16
Assignment 2 – Big Data Analytics
(McDonalds)
Submitted By
Course
Professor
Date
Table of Contents
1.    Executive Summary    3
2.    Introduction    3
3.    Organization Background    4
4.    Business Priority    5
5.    Big Data Approach    6
6.    Information and Sources    7
7.    Big Data Technologies    7
8.    Big Data Visualization Examples    10
9.    Big Data Adoption Challenges and Governance    12
10.    Conclusion    13
11.    References    14
1. Executive Summary
The immense repository of terabytes of information is created every day by using current information systems or advanced innovations like Internet of Things and cloud computing. Analyzing this
huge data requires many endeavors at various dimensions in order to get meaningful data for better decision making process. Consequently, this big data analysis is a momentum area of innovative work. The main objective of this paper is to analyze the potential effect of big data challenges, open research issues, and various tools that are related with it. Thus, this paper will provide a better understanding to analyze big data at various stages. Moreover, it opens another skyline for the researchers to build up the solution, in view of the challenges as well as open research issues (Acharjya and Kauser, 2016). In this report, we will discuss about McDonalds organization and how the organization can use the approach of big data and convert it into meaningful information for better decision making.
2. Introduction
Big data analytics is characterized as tool or process which can be applied to large datasets in order to acquire meaningful insights, which helps the organization to improve the performance. These innovations help organizations to better comprehend their business sectors and influence the opportunities by accessing large amount of data (Ghasemaghaei and Hassanein, 2015). Organizations that gather information can use the data for generating new revenue schemes. Therefore, the organization should start with a business reason for analyzing their big data so as to decide how information will be gathered, organized or processed for the some chosen analytic form. The main opportunities that are connected into data analysis in numerous associations have created a significant enthusiasm for business insight, that help to create better understanding of the market and furthermore to make accurate decision at correct time (Alsghaier and Akour, 2017).
For the most part, large datasets in the organization are managed using Data warehouses. Some data mining methodologies are certainly not ready to deal with the large datasets effectively. The main objective of this paper is to discuss the effect of data mining methods and structures of big data which is considered as ideal methodology in accomplishing the correctness and timing in quick as well as reliable decision making (Acharjya and Kauser, 2016). In this report, we are discussing the case of McDonalds.
3. Organization Background
McDonald's is the large global food service retailer having 34.000 local restaurants that serve approximately 69 million individuals in 118 nations every day. The daily customer traffic is about 62 million clients and approximately 75 burgers are sold consistently. Their annual revenue is about $ 27 billion. With such large restaurant chain, their generation of data is so large that they need some analytic tool to use this data in meaningful way (Datafloq, 2019).
Adopting data driven culture is likewise imperative to enable McDonald's to comprehend the restaurant performance at every franchise that can be imparted to other franchises in the chain. The consistency of sustenance as well as customer's experience Since McDonald's uses an establishment plan of action, is significant over the establishment. The organization considers multiple data points for making the customer's experience better. For instance, in case of drive-through experience they not just evaluate the structure of the drive-through, yet they analyze the data which is provided to the customer and what's going on for clients holding up in line to order. In order to make customer's experience better, they analyze the data pattern fr predicting and modify the overall structure (Marr, 2019).
4. Business Priority
Currently, the data which is provided by local stores depended on average metrics. Hence, it became challenging while comparing the stores so that some action can be taken for making the results better. In this manner McDonald's uses an average to trend analytics that give significantly more understanding of data in case of local stores. The management integrated datasets and envisioned it to comprehend the reason as well as its effect while making differences. They joined multiple graphs based on the datasets in order to comprehend the correlation. These correlations were utilized for having better and significant actions, that results in saving money as well as time.
This information which is derived from the prescient analytics is utilized to make iterations crosswise over design, data as well as individuals practices. McDonald's at that point utilizes this data analytics in order to discover the trade-offs of the progressions made so as to find the better solution for plan, data and individuals. The big data solution like Microsoft Azure HDInsight can enable the organization to find fundamental data that may have remained covered up in your information—or even been lost for the restaurant. This data can help to evaluate the association's verifiable performance, find new opportunities, recognize operational efficiencies, enhance consumer loyalty, and even anticipate likely results for the coming future (Microsoft big data solutions, 2019).
Besides, McDonald's tracks and analyze this large measures of factors for improving its processes and improving the customer experience. The organization monitor in-store traffic, client co-operations, flow graphs to analyze flow through pattern of customer's orders, purpose of-sales data, video information and sensor information. Results that is derived from this information is utilized to develop iterations in the structure of the restaurant chains, generate menu variations, advance their training program and their supply chain network. Hence, according to data, all restaurant chains around the globe appear to be identical, but there is difference in each chain as they are streamlined utilizing such information for the local market. Likewise, McDonald's uses operational information to computerize and enhance the...
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