Page 1 of 6 Big Data Basics INFS 5095 2018 Student's Assignment Guide (Internal and External/Online) Assignment 2 Management Proposal Big Data Capabilities 50% 3000 words Due: Sunday, 25 November...

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Page 1 of 6 Big Data Basics INFS 5095 2018 Student's Assignment Guide (Internal and External/Online) Assignment 2 Management Proposal Big Data Capabilities 50% 3000 words Due: Sunday, 25 November 2018, 10.00pm Late assignments: 10% per day deducted Last updated: 13.9.2018 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 6 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 Page 3 of 6 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% Page 4 of 6 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 each of 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. 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. For instance, the sample template includes a Table of Contents and Executive Summary, you can leave these out if you want. 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 5 of 6 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: • Headings • Direct quotes (you will gain more marks by writing using your own words than using lots of direct quotes) • Summary/Executive Summary (if you chose to include one) • Diagram headings and captions These are excluded: • Title page • Table of contents • References • Footnotes • Text within diagrams Referencing Referencing is important for assignments to: (a) expand your knowledge of the assignment topic and (b) provide evidence to the claims you make and (c) demonstrate you know what you are talking about to make a convincing proposal and (d) provide other examples or case studies The general rule is if you are using information or data that is not of your own creation then you need to acknowledge it. Not only is this for academic integrity but to add weight to your recommendations – to show they are just not opinions and that decision makers who use your recommendations would less at risk of a failed project. This includes the screenshots, data you use and points taken from the presentations. How many references? That depends on how many points you are making. Generally, more is better because you have used more sources to understand the topic and reinforce your points. A minimum of 5 references is required. Just adding as many references as possible without using them in the assignment won't earn maximum marks. If you plagiarise (ie. copy from references and don't include it in quotes or include a reference) you will be penalised – students have fail assignments for doing this! We want your understanding on the topic, not copied words from experts – this only demonstrates that you can research well, not apply your learning. Reference quality The type (quality) of references makes a difference and this is considered in the marks as well. Feel free to use the readers and links from the lectures and Course Outline. Page 6 of 6 Avoid marketing/vendor sites and general websites - the quality
Answered Same DayNov 10, 2020INFS 5095University Of South Australia

Answer To: Page 1 of 6 Big Data Basics INFS 5095 2018 Student's Assignment Guide (Internal and External/Online)...

Ankit answered on Nov 15 2020
147 Votes
3
                Student_Id
                Student_Name    
    Proposal for management of McDonald’s to implement Big Data capabilities
    Executive Summary
     3 to 4
    Business priority
    4 to 7
    Big data approach
    7 to 10
    Information and sources
    11
    Big data technologies
    11 to 13
    Big data visualisation examples
    13 to 15
    Big data adoption challenges and governance
    15 to 16
    References
    17
                Table of Contents
Executive Summary
Recently, organizations and public firms are implementing big data for extensive information analysis. Many organizations are giving different services to their users or clients via data analysis after impleme
nting big data frameworks.
The organization McDonald’s is monstrous worldwide food chain retailer having more than 34.000 local restaurants serving in excess of 69 million individuals in 118 nations every day. Their day by day client movement is around 62 million clients and they offer around 75 burgers consistently. Their yearly income is $ 27 billion and over 750.000 workers McDonald's is a big organization. Americans alone expend one billion pounds of hamburger at McDonald's annually. It may be certain that they produce tremendous amount of information but there is great need of big data implementation to leverage their data.
Companies use big data for various data analysis as listed below:
· Customer sentiment analysis
· Behavioural Analytics
· Predictive support
· Fraud detection
· Customer Segmentation
There are different steps that company McDonald's would need to implement big data capability such as dealing with growth of data, producing insights in a timely way, recruiting and retaining big data talent, merging disparate data sources, data validation, big data security and organizational resistance.
Big Data visualization is among the most extreme imperative parts of performing with different Big Data analytics arrangements. When the stream of crude data is spoken to with pictures, basic leadership turns out to be considerably less demanding. With the end goal to meet and surpass the client's desires, the apparatuses for Big Data representation ought to give a specific arrangement of highlights:
· Ability to execute numerous sorts of incoming information
· Ability to apply different channels to modify the outcomes
· Capacity to connect with the informational collections amid the investigation
· Capacity to interface with other programming to get incoming information or give contribution to them
· Capacity to give coordinated effort choices to the clients
There are various big data adoption challenges such as financial challenges, poor framework, and absence of expert staff etc. are normal to most developing nations and hole a gap in the adoption of big data when contrasted with developed nations which is equivalent to the digital separation. Data governance is the system for empowering this change, paying little heed to the information condition. Nonetheless, big data situations, for example, data lakes, are especially powerless to fundamental issues around information quality, information heredity, and fitting utilization and significance, given the prevalence of unstructured and semi-organized information.
The challenges that the present wide based data governance enables associations to survive with those of information availability, ease of use, which means, and quality that all expansion exponentially in the realm of big data.
Business priority
The organization McDonald’s is monstrous worldwide food chain retailer having more than 34.000 local restaurants serving in excess of 69 million individuals in 118 nations every day. Their day by day client movement is around 62 million clients and they offer around 75 burgers consistently. Their yearly income is $ 27 billion and over 750.000 workers McDonald's is a big organization. Americans alone expend one billion pounds of hamburger at McDonald's annually. There is need of big data analytics for McDonald's to gain good understanding to enhance tasks at its different outlets and upgrade client encounter. [Datafloq]
McDonald's big data framework will analyzed information on different cases, for example, hold up times, data on the menu, the extent of the requests, requesting examples of the clients to improve the tasks of its outlets at particular areas.
Figure 1 [Dezyre]
Companies use big data for various data analysis as shown in figure 1 and listed below:
· Customer sentiment analysis: The universe of big data is occupied with discussions, client surveys, inputs and remarks. With expanding number of client correspondence channels such as online media, item survey gatherings, and so forth – it is essential for associations to comprehend and break down what clients say in regards to their items or services to guarantee consumer loyalty. The channels of big data and social media together aid in analysing client sentiments that gives associations a clear cut image on what they have to do to beat their rivals.
Customer sentiment analysis is important for McDonald’s as it not just encourages them to react rapidly to developing issue yet it additionally causes them successfully interface with their clients and improve understanding on what item and services their clients observe to be important. [Dezyre 2015]
· Behavioural Analytics: The big data beauty lies in knowing client behaviour. Companies are bridling the intensity of big data via behavioural analytics to convey huge incentive to organizations. The firms which utilize behavioural analytics to foresee client behaviour have just gone up tenfold in adding profit to their business.
Behavioural Analytics is important for McDonald’s as it help them to monitor’s behaviour of client by tracking “How many people visit the McDonald outlet and which item they buy in, how long they stay at the outlet.” [Dezyre 2015]
· Predictive support: Presently organizations need to look into future to support incomes. Organizations are creating predictive frameworks as a best need by utilizing big data analytics. Predictive support is important for McDonald’s as it uses big data to support predictive marketing that aids McDonald’s to build brand loyalty by boosting their product service revenues.  McDonald's also uses predictive support analytics to determine the trade-offs of the changes done in order to provide the optimal...
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