Project Proposal for McDonald Big Data Analytics

Question

Task:
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
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.

Answer

Executive Summary
Data can be defined as an individual unit of information on which operations are performed. It is considered a valuable asset for any organization to store information related to business, and its operations. However, with the expansion of information day-by-day, the size of data is increasing. This data is known as big data which is in huge size and growing exponentially as time increases. Big data is defined as, information asset that is in huge volume, velocity and variety that provides insights into a business through decision making.

The organization McDonald’s is a fast-food company which was founded by Richard and Maurice McDonald. This business was shifted in the year 1953 and relocated to Phoenix, Arizona. Now, global headquarters is shifted into Oak Brook, Illinois in the year 2018. The company has a speciality of fast food such as burgers, French fries, cheeseburgers, chicken burger, soft drinks, milkshake, deserts, wraps, and breakfast, and fruit items. McDonald’s as per 2018 report served 69 million customers every day in above 100 countries across 37, 000 branches. Furthermore, it is also considered the world’s second-largest private employers with 1.5 million employees working under one roof.

With these types of structured information, big data capabilities could be implemented successfully. There are three types of data on which analytics is operated such as structured, unstructured, and semi-structured. Structured Data refer to the data which is already stored in a database, in an organized manner. Structured data is organized from existing data and is retrieved from programming and computer-related activities. There are two sources of structured data, for example, machines and humans. The data which is received from sensors, weblogs, and financial systems are known as machine-generated data. Consider some examples like medical devices, GPS data, statistics, servers, applications and the huge amount of data.

Introduction
Data is a singular unit taken from huge information on which certain actions are taken. The organization considers it a valuable asset due to information stored in the business and the operations. On the contrary, as information propagates day-by-day volume has also increased. The large volume of data is known as big data, which is larger in volume, velocity and variety. The definition is very simple, big data is a data which is in higher size and demand a cost-effective and innovative way of processing in the information for the businesses.

However, this data is complex and cannot be handled with traditional data management tools. The business organization uses this huge amount of data to determine its competitive market through analytics tools. While it also informs businesses about their customers and client information. For instance, the use of customer relationship management informs about old customers and how the interaction between them should be kept. Therefore, Big data has become an important asset for any organization and should be considered overtime. To understand, Big data capabilities, it is mandatory how it can be used in a business organization. And to gain knowledge on capabilities, what big data is should be first understood. Therefore, the purpose of this proposal is to elaborate on big data capabilities which can be used for McDonald big data analytics.

The structure of this proposal includes sections. Key business priority identifies the operations in the company. Furthermore, it also identifies the strategic objectives of the organization with a small background. Additionally, big data approach analyses the steps for implementing big data. The information sources include information which will be needed to operate to retrieve a big data solution. Section technology expands tools that required to conduct big data operations. The visualizations include how McDonald big data analytics is implemented for the provided information. Lastly, there are adoption issues while implementing big data which will be discussed in the proposal.

About McDonald’s and Their Key Business priority
The company was established in the year 1940 in the city of California. It was founded so that American foods could be provided to the customers in the United States. In the year 1953, the business was relocated to Arizona (Corporate.McDonalds.com n.d.). McDonald’s offers a different variety of foods such as soft drink, chicken and veg burgers, milkshake, dessert, salads, fruits, smoothies and breakfast options. According to McDonald’s revenue report, in the year 2018, the company served 70 million people in more than 100 countries globally (McDonald’s corporation 2018). The company has over 2 million employees working in multiple global branches. Therefore, people like McDonald's because of being better fast-food company across the world. People from every corner across the globe like fast food dishes and prefer quick burger meals.

The business priorities for the company is based on the analysis of the major social and environmental impact of the services on the business. Whereas, customers and employees are kept happy through franchises, food safety and wide stakeholders involved. Additionally, another aspect of McDonald’s is Supply chain, food safety, and quality assurance, Products

Marketing, Customer satisfaction, Intellectual property through trademarks, copyrights and Competitive market. However, according to the company’s revenue report, lack of customer preferences impacts their pricing, promotional and marketing strategies. Therefore, with the help of McDonald big data analytics capability customer preferences could be understood through information sources gathered.

Big Data Approach
The big data approach for implementing the big data solution is based on a set of steps. These steps will be helpful to plan a big data solution of American fast food company such as McDonald’s. As their goals are to achieve customer preferences so that it will increase their revenue. We propose five steps so that McDonald big data analytics capability could be implemented successfully.

Step 1: Identification of Business Challenges for McDonald big data analytics.
The first and foremost task for McDonald big data analytics capability is to understand the company’s business objectives. Hence, as far we know McDonald’s is an American fast-food company who sells fast food to the customers in the United States and across the globe. However, what could be possible business challenges for them. The biggest concern is that customer reach has become smaller due to other competitors (Tabesh, Mousavidin, & Hasani 2019). Hence, based on customer preferences new menus must be defined. However, customers are in billions as well as their needs are too. The huge size of data is difficult to operate and thus McDonald big data analytics capabilities could be used. Therefore, our first step is to gather business challenge ‘customer preferences’ which impacts pricing, marketing and other aspects of the company negatively.

Step 2: Make priority for Topmost Issues
The business problem of customer preference is identified now issues should be considered. The pricing and marketing strategies depend on what customers like or dislike in fast food. A lot of people are becoming aware of what type of raw quality food is used in making burgers. Therefore, checking preferences which are healthy is the current issue. Additionally, pricing and marketing strategies improvement is another important issue for the company. Hence, we will use McDonald big data analytics and learn what customers really like.

Step 3: Data source Identification
Big data is all about a huge amount of data. The identification from which source data should be taken is the third step (Import.io 2018). For example, structured, unstructured or semi-structured are three options. The unstructured data from social media pages could be considered a good option so that information could be retrieved. Hence, it should be considered a data source for doing McDonald big data analytics on the information. For example, people accessing McDonald’s social media page shows their preferences.

Step 4: Plan Tools for Big Data
There are several tools which are offered for Big data analytics. However, tools chosen from open source platforms could be preferred. The cost of big data tools is also considered before making any decisions.

Step 5: Identify Adoption Problems and Its Possible Solutions
The last step is to identify adoption problems and possible outcomes so that people could be prepared for big data capabilities. Further issues such as volume, velocity, data security, and governance should be highlighted.

Information and Sources
The information sources for McDonald big data analytics solution will be internal as well as external. The internal will include data presented from inside of the company. For example, customer preferences can be predicted through website logs. The people who have made the same order for a couple of times could be identified. On the contrary, external sources include social media and official statistics. With these types of structured information, big data capabilities could be implemented successfully.

There are three categories of data on which analytics is operated such as structured, unstructured, and semi-structured (Gandomi, and Haider 2015). Structured Data refer to the data which is already stored in a database, in an organized manner. Structured data is stored in tabular form and programming such as RDBMS is performed. The structured data is in two forms like machines and humans. The data which is received from sensor, weblogs, and financial information are known as machine-generated data. Consider some examples like medical devices, GPS data, statistics through servers and applications and the huge amount of data from traditional methods (Big Data Framework n.d.). The structured related to humans include in form of data inserted into a computer. Furthermore, these types of data are used in customer relationship management for customer information. Whereas, unstructured data is that which is not at all organized. It is in raw form and requires proper conversion methods to insert into computerized databases. Unstructured data is again divided into captured data such as images, documents, and videos. On the contrary, user-generated from social media platforms is another unstructured data example. Lastly, unstructured data is in the middle of structured and unstructured data only.

Big Data Technologies
To identify big data technologies, Garter hype Cycle will be used so that identification for big data tools could be done (Prinsloo, and Van Deventer 2017). Furthermore, these cycles introduce five phases for emerging technology identification. These phases are innovation triggers, the peak of highest expectations, the slope of enlightenment, disillusion, the plateau of productivity (Efimenko, and Khoroshevsky 2017).

• Innovation Trigger: The technology should be trigger innovative concept for the companies. For example, technologies which are famous and available on the internet and research platforms are quite popular. Thus, considering them will be more effective as compared to technologies which did not exist in research or has any records for innovation activities.
• The Peak of Highest Expectations: In the Hype Cycle, the peak is that saturation point where everyone believes in the emerging technologies (Dedehayir, and Steinert, 2016). The companies will consider this important and take actions accordingly.
• Disillusion: The failures for a possible technology is available. It should be considered before implementing any emerging big data technologies. Because the failure rate identifies issues that occurred during implementation and therefore, technologies could be improved.
• The slope of Enlightenment: It defines how emerging technologies will benefit the organizations completely. However, certain big data analytics technology if does not offer good outcomes they should be completely and effectively terminated.
• Plateau of Productivity: Lastly, how much technologies are helping must be considered. Whether a company has produced in terms of technologies should be acknowledged. Additionally, understand whether companies are working properly or not should be also be considered on these plateaus.

High-Level Architecture
The high-level architecture is adopted from Oracle and shows how big data analytics could be used. Block 1 identifies what types of data sources McDonald big data analytics will use to analyse customer preferences. For example, social media will define how customer choose fast food trends. Next, block 2 determine what technologies are effective some of them are Notebook, Spark and data catalogue. Additionally, big data with huge volume will be stored in object storage effectively. Lastly, Block 3 reprises data visualization with certain analytical tools for the big data of companies.

Source (Prichard 2018)

Big Data Visualisation Examples
Data visualization is an important aspect for big data analytics for representing the information is large volumes. The data in huge amount is difficult to operate and brings misinterpretations in statistics; therefore, visualization helps to improve data quality and define the understanding of relationships among them (Fiaz et al., 2016). As we want to determine customer preferences for McDonald’s menu. The data sources are from social media platforms in the structured and unstructured manner presented on the Internet. Two big data visualization techniques will be used for the McDonald big data analytics of customer preferences across the world. We propose the following examples with snapshots to get a better understanding.

Two Dimensional 2D Areas: The two-dimensional 2D areas are usually used to represent symbols map on geographical distance. The map with location and region can be seen through such map visuals. We could use this data visualization technique so that customer preferences for McDonald’s branches could be seen. For example, visuals for customer choice could be found in disperse fast food branches across the world.

Source (Fedak 2018)

Social Media Comment Analysis: The social media comments are quite useful to gather what customer feels for fast food. For example, various comments on a burger could be identified from online platforms.

Source (Ivanov 2018)

Big Data Adoption Challenges and Governance
Big data is considered by organization McDonald’s so that they could capture customers for new changes. However, McDonald big data analytics adoption brings several challenges that a company must take seriously. In this section, we offer various adoption challenges to consider.

• Interoperability: The integration of business intelligence and analytics with big data seems difficult. Further, investments are too costly for business analytics tools and thus cannot be supported by the companies (Baig, Shuib, and Yadegaridehkordi 2019). Thus, integration with other platforms is difficult for while using big data. There is no flexibility and support for different types of data. Hence, combability with other platforms is the key concern and McDonald’s should consider this situation before implementing this technology.
• Security: Data, while getting generated and being accessed, need to be controlled properly in terms of the organization’s context.

The information when is generated from structured or unstructured data should have control of security for the organization. However, if security is not considered compliance issues, data loss, and exposure of data to hackers will appear anytime soon.

• Data Storage: As big data is huge, storage becomes a huge problem in terms of volume, velocity and variety. The Storing of big data on traditional physical storage is difficult for example, as hard disk drives (HDDs) often failure (Ugur, and Turan 2019). Therefore, storage is the biggest concern for how McDonald’s could use to store information. Data storage has become a concern before implementing big data capability. Hence, it should consider each organization.
• Lack of Skills: Another adoption barrier for Big Data is the lack of skills in retrieving information. The organizations are helpless because they cannot find employees with data analytics experience (Weibl, and Hess 2018). Furthermore, education on big data is not provided to the employees recruited in an organization. It makes impossible to use big data analytics for the customer preferences and other important goals.
• Cost: The McDonald big data analytics capability is good in a theoretical sense. However, practically it is impossible to apply. The big data projects require huge infrastructure for supporting a huge amount of data. Besides, data sources must be managed effectively which is again a drawback. Additionally, big data software and analytics tools are much costly and cannot be afforded by small scale companies. Hence, before implementation, it is necessary to look after the cost and budget for the project.
• Relevant information search: The information for analyzing customer preferences is difficult. The data present on the internet is unstructured and difficult to gather for the organization. For example, customer transaction data, documentation, sensor data, social media profiles, emails, images cannot be formed into a logical structure (Arunachalam, Kumar, and Kawalek 2018). Therefore, analyzing customer information search becomes more difficult to process. The data volume is the biggest concern and it becomes possible to adopt big data capabilities. Therefore, it is important to consider how data will be extracted to fulfil customer preferences effectively.
• Governance: Data governance looks after data privacy concern. As information of customers is confidential and should not be taken advantage of. The company using the information for their own purpose is considered bad. The governance issue for who data is watching and using is another adoption barrier.

Conclusion
To summarize, data is complex when it is in a larger size as compared to a smaller one. However, McDonald big data analytics capability could be used to identify many business priorities. One such concept is how to increase customer preferences effectively. Therefore, the purpose of this report was to demonstrate how customer preferences could be increased on a global level. McDonald big data analytics will identify from data sources and retrieve information from data visuals effectively. For example, customers like good food with proper safety and healthy menu. Further, customer preference increase revenue, marketing developments and global reach. Therefore, to gather and overcome these priority five key steps were identified respectively. For instance, business objectives, data sources, and other planning tools for data visualization.

The business problem of customer preference is identified now issues should be considered. Big data is all about a huge amount of data. The identification from which source data should be taken is the third step. There are several tools which are offered for Big data analytics. The cost of big data tools is also considered before making any decisions. The last step is to identify adoption problems and possible outcomes so that people could be prepared for big data capabilities. Further issues such as volume, velocity, data security, and governance should be highlighted. The information sources for big data solution will be internal as well as external. To identify big data technologies, Garter hype Cycle will be used so that identification for big data tools could be done. McDonald big data analytics is considered so that they could capture customers for new changes. However, big data adoption brings several challenges that a company must take seriously. The integration of business intelligence and analytics with big data seems difficult. As big data is huge, storage becomes a huge problem in terms of volume, velocity and variety. Therefore, adoption has become the biggest concern for how McDonald’s could use to store information.

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