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INFS5095 Big Data Basics

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Course Code: INFS5095
University: University Of South Australia

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Business intelligence and analytics: from big data to a big impact.
Data-intensive applications, challenges, techniques, and technologies:
How big data is different MIT Sloan Management Review.
Communication for preventing reputational risk McDonald’s strategy.
A big data framework for u healthcare systems utilizing vital signs.
Big data and human geography: Opportunities, challenges, and risks.
McDonald’s India–plotting a winning strategy. Emerald Emerging Markets Case Studies, 
Big data technologies
High level architecture diagram
Big data visualisation examples
Big data adoption challenges and governance


About McDonald’s
McDonald’s is a fast food company based in American which was founded in the year 1940 just as a simple restaurant under the management of Richard McDonald and Maurice McDonald in California. The business had been initially rechristened as a hamburger stand but later transformed into a franchise. However, Ray Kroc joined the partnership in 1955 as a franchise agent and later purchased it from the two McDonald brothers.  McDonald’s is among the largest restaurant chains globally which serve over 69 million customers in over 90 countries daily. Although this company is famous for its main product, hamburgers, it’s also sold cheeseburgers, breakfast items, wraps, French fries, desserts, milkshakes, chicken products and soft drinks (Pradhan, 2018, p.20). Recently, the company has incorporated new items in its menus like salads, fruits, smoothies, and fish as a response to the negative backlashes regarding unhealthiness in its food. Mainly, the company’s revenue comes from sales from company restaurants, fees from its franchisees, royalties and company rent. According to the current statistics, McDonald’s has been ranked second in terms of private employers just behind Walmart employing 1.9 million people, 1.5 million who work for franchises.
Despite these achievements of McDonald’s, the company has had its own fault lines which have made to continually lose its past glory. For instance, its once loyal customers have continued to turn down their heads and divert to the company’s upmarket competitors such as Chipotle, Shake Shack and Five Guys. At the other end of the spectrum, the company has realized that its operations have mainly been targeted on the high-income customers and who is less compared to low-income customers (Crawford, 2015, p.11). For that reason, the company has continually lost out to its traditional rival, Burger King. The reputation of the company has also deteriorated as a result of frequent protests from its workers due to poor payments to its staff and that has resulted in poor company PR.  Food safety has also been an issue of concern to the company in certain countries like China which has recorded the latest scandal where it was accused of supplying rotten meat and falsifying the expiry date of its meat. Lastly but not least, the company has realized that its menu has turned out to be too complicated making some customers wait for long when being prepared.
These and other challenges facing the company must be addressed fully if the company will wish to restore its past glory and compete favorably with its rivals in the industry.  The company will have to look for ways of getting back its once-loyal customers who have diverted to its rivals like Chipotle, Shake Shack and Five Guys, revisit its operations in order to take care of both the high-income class as well as the low-income class and assuring its customers of its food safety (Elena, Cecilia, and Chiara, 2016). To be able to solve these challenges, the company will require a deep analysis of its customer demands as well as employees in order to be in a position of identifying the root cause of its problems. This paper proposes a Big Data approach for the company to be able to obtain the required data in regard to the aforementioned concerns.  
The Key Business priority
As outlined above, the McDonalds Company is facing a number of challenges which have made it loose its past glory as well as its competitive advantage. To retrace back its initial competitive position, the company’s 2018 report has set out some key steps which will enable the company to arise and shine as it used to be. Such steps have been recorded as the key business priorities of the company. They include but not limited to:
Winning back the customers who fall out of love of the company products
As recorded in the history of the company, it had loyal customers who made the large market coverability of this company. However, the trend began to take a reverse direction with many of them getting absorbed in the company’s upmarket rivals like Five Guys, Chipotle and Shake Shack. This has led to a decline in the company’s market and hence leading to low revenues (Elena, Cecilia, and Chiara, 2016). The first step in reigniting the customer loyalty affair will be to get collect data on customer views about the company products which can only be achieved mainly through big data technology.
Revisiting its product affordability 
Among the current weaknesses of the company is its products cost which favors mainly the high-income class and discriminate the low-class income. This has made the company lockout low-class income citizens who would be a key factor towards the company success considering the fact that low-class income citizens are the majority in the current economies (Crawford, 2015, p.11). For that matter, the company has stipulated in its current strategic plan reports that it will be looking for ways through which it would be able to take care of the low-class citizens as well. To achieve the goal, the company must have basic facts on the approach to use since the issue might as well harm the business if not analyzed keenly.
Solving the problem of poor payments to its staff members
The company has undergone frequent protests and employee strikes which have led to huge loss considering the fact that the company operates on some perishable products like fruits and meat.  The root cause of the strikes has been the poor payments of its employees which can be linked with poor management and poor allocation of company resources (Elena, Cecilia, and Chiara, 2016). The company has outlined in its current evaluation report that it will be coming up with a once for all solution to this problem. This will entail proper resource allocation and also adjusting the human resource within the organization. This is an approach that can impact the company differently if not approached with great care and therefore thorough data analysis must be done before the decision can be made. This will definitely call for a big data approach if the company wishes to be on its safe side.
Sorting out the issue of product safety to avoid a bad reputation
A recent scandal in China where the company was accused of supplying rotten meat and falsifying the expiry date for its products highly compromised the company reputation to its loyal customers.  Although the matter has never come out of what had really happened, the company has taken it as its initiative to ensure such and other similar cases are not evident in future (Crawford, 2015, p.12). Such cases may take place due to various reasons like counterfeiting or dishonest employees. For that matter, for this company to be in a position of getting a valid solution for this problem must invest highly on background data which will enable it to cover all the loopholes and this will definitely call for big data technology.
Slimming down the company menu
Most of McDonald’s customers have expressed their disappointment with the menu offered by the company arguing that the menu has been too complicated which has been making kitchen operations too complex and time consuming hence making them wait for more than they expect (Elena, Cecilia, and Chiara, 2016). So, in an attempt to make things simpler across its stakeholders and also to offer healthier alternatives to its common products, burgers & fries, the company has resolved to revisit its menu. However, much as this may sound easy, the company will need to have statistics on its customer demands which are expected to vary greatly and hence will have to approach the matter through the big data technology.
Big data approach
Considering the fact that implementation of big data technology has proved to have 50/50 chances of success, it has always called for calculated moves when implementing the technology in an already established venture because missing the mark is likely to cost the organization heavily. Here are the steps I will follow to implement big data capability in McDonald company
Identification of the organization needs
Because my end goal has the biggest impact on shaping the entire implementation strategy, I will first decide on how the current problems outlined in the company report. Those problems are deteriorated company reputation, minimal coverability, employee relationships and complexity of company menu. I will ensure that my goals are certain, direct and precise bearing in mind that if I will limit my purpose to exploring the possibilities only will end up in a total confusion (Oswa and Koul, 2013, p.223). Based on my goals, I will then select a methodology, choose the right data sources and hire the right employees. So, I will base my goals on the SMART (Specific, Measurable, Attainable, Relevant and Timely) framework in order to come up with appropriate plans.
Leveraging on the Proven Big Data Strategies
Basically, there are four proven approaches in creating an effective and working big data strategy based on the end goals and the availability of data. Because my goals are wide in the implementation, covering employees, management, customers and third parties, I will have to apply the four strategies in collaboration. The first strategy would be performance management, which will entail using the data history of the company like customer purchases, inventory levels, and turnover levels to make decisions in regard to solving the major challenges which have been identified as root causes of the company’s stagnated progress. The second strategy will be data exploration which will heavily major on data mining techniques and research in order to come up with solutions and correlations which are not easily observable using in-house data. Thirdly, I will use social analytics strategy which will entail measuring non-transactional data from different media platforms like Twitter, Facebook, Instagram, and Google+. I will be majorly interested in the comments which are left by the reviewers of McDonald products to get customer opinions on new services and how they are served (Michael and Miller, 2013, p.23). Lastly, I will apply a decision science strategy to the data that I will have collected from the previous three approaches by analyzing and experimenting non-transactional data such as customer reviews, ideas, and other generated content. This step will be mainly exploring possibilities and not measuring the known objectives.
Establishing a Talent Pool
Considering the critical role of Human resource in coming up with Big Data strategy, I will embark on getting specialists who will be able to assist in the whole process right from data collection to analysis and presentation of the results. The pool will have several categories (McAfee et al, 2012, p.61): there must be statisticians who will bring sense to the raw data, business analysts who will be in charge of communication towards the decision makers and lastly key decision makers to lead the whole team. Without proper and organized teams, the efforts geared towards implementation of this technology will revolve around jargons that will not be clear to either of the teams.
Information and sources
The research will utilize different sources of information in order to realize its main objectives and goals. Below are some of the sources it will utilize
Customer sentiments from Social media
McDonald’s has its own pages in all the available social media platforms like Facebook, Twitter, Instagram, and Telegram. Under these platforms, people have been granted the permission of commenting and leaving their own opinions as well as reviews of the company products. This platform will, therefore, act as a major source of information because of the diversity of ideas from one person to another. It is a data which can be considered reliable because no person is influenced by the others to comment on the products but gives out his or her genuine views.
McDonalds company databases
In order to know the company progress for future prediction purposes, all the stored data concerning both consumer and staff changes must be consulted. The information will be extracted right from the launch of the company to where it stands today. This kind of information will help in plotting its progress in order to understand the company progress. It also enables the analytical team to link some of the challenges which are currently eating into the company progress.
Public web 
Under this platform, since all the members of the public are allowed to express their opinions regardless of whether they are customers of the company or not, different views from different people will be gathered then filtered to give useful information on how the company operations, services as well as its products can be improved.
Other Business apps 
McDonald’s is not the leading company in this industry and therefore looking at the success factors of the leaders will enable the company to gains new insights which may enable it to regain its past glory. Companies like Chipotle, Shake Shack and Five Guys which have taken over the industry lately have their own approaches in the market. Analyzing their success factors through research on their business applications will enable the company management to adopt those skills and approaches to improve.
Big data technologies
The only differentiator between a market leader and loser is the manner with which data management is done and any business organization that cannot handle the influx of its data and uses it appropriately becomes the looser. Here are some of the trendy technologies in big data analytics which will be used in my case of McDonald’s.
Apache Spark
A lot of information which I will require in this implementation process is mainly from online sources like the public web, social media platforms like Facebook, Twitter, Hangouts, and Instagram. So, this basically implies my core reliance on streaming modules. Apache Spark is, on the other hand, the fastest and the only general engine which will enable me to build streaming modules, venture into machine learning and carry out SQL support (Kitchin, 2013, p.265). Also, it’s a preferable technology under this circumstance because it supports all important big data languages like java, python, Scala and R. Also, considering the fact that speed is very important in data processing, this technology will help me reduce the waiting time queries and time is taken to execute programs.
The fact that I will be collecting data from different sites and platforms signals the need to have a tool with the adequate capacity to store and process data from those platforms with minimal coding. For that purpose, I will leverage on NiFi technology which has the ability to store and process data from different sources. Additionally, the tool will enable me to automate data flow between those systems. Its data security level is also commendable (Kim, Park, Yi and Kim, 2014, p.495).
With the initial mentioning of some of the basic technologies that I will be using in my analytics, Kafka technology comes in to integrate those other technologies with third-party tools. Also, the technology will enable me to handle data streams efficiently especially in real time basis. The technology is scalable, extremely faster and fault tolerant hence a safe option for me in this process.
Apache Samza
I will make use Apache Samza technology to extend the capabilities of Kafka technology capabilities and strengthen my analysis with features like fault tolerance, simplification of API, message durability, extensibility, and scalability. It will, therefore, act mainly as a distributed stream processing framework in my case.
Big data visualization examples
This is a visualization sample which indicates the growth of McDonald’s company right from its inception to where the company stands at this time. Visualization of this data will enable me to predict the future of the company in terms of progress and hence look into threats to its health as well as leveraging on the available opportunities to ensure its expansion.
This visualization screenshot shows the way different data collected from different platforms will be channeled into different analytical experts through combination, integration, and grouping so that sensible results can be realized.
Big data adoption challenges and governance
Considering the fact that handling big data has always been a complex task; challenges have always been there when implementing the technology. Some of the common challenges which will be expected in the adoption of big data technology at McDonalds Company are, the uncertainty of data management, the big data talent gap, syncing across different sources of data, the big data structure involved, solution cost and skill availability (Katal, Wazid and Goudar, 2013, p.405).
Uncertainty of Data Management 
Among the few disruptive facets of big data management is its use of diverse innovative frameworks and data management tools whose designs are dedicated to supporting analytical and operational processing. For instance, the NoSQL frameworks used in this technology are totally different from the traditional relational database management systems and which are designed to meet the performance requirements of some of the applications of this technology like quick responses and management of huge amounts of data (Davenport, Barth and Bean, 2012). The NoSQL framework has many approaches like hierarchical object representation and key-value storage. The diversity of this framework and market status will, therefore, result in data management uncertainties.
Getting Data into Big Data Structure
The process of transmitting, accessing and delivering data collected from different sources and then loading it into a big data platform is a complex process. These intricate aspects are likely to pause challenges considering the fact that data which has been collected in this scenario is diverse and from many different sources.
Extraction of Information from Big Data Integration
Data collected in the initial stages of data collection keeps on fluctuating from time to time. For instance, the demand increment may spike at any time in response to different aspects of the company process cycle (Chen and Zhang, 2014, p.315). This, in turn, will become a challenge to integrate the data and ensure the right time data available to the consumers.
Miscellaneous Challenges
Merging data that is neither similar in source nor structure is likely to demand high resource investment considering the number of different specialists who will be needed (Chen, Chiang, and Storey, 2012, p.1170). Processing large amounts of data at a reasonable speed also calls for high investment in terms of processing machines. These machines are also delicate and very expensive.
Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: from big data to a big impact. MIS quarterly, pp.1165-1188.
Crawford, A., 2015. McDonald’s: A Case Study in Glocalization. Journal of Global Business Issues, 9(1), p.11.
Chen, C.P. and Zhang, C.Y., 2014. Data-intensive applications, challenges, techniques, and technologies: A survey on Big Data. Information Sciences, 275, pp.314-347.
Davenport, T.H., Barth, P., and Bean, R., 2012. How big data is different. MIT Sloan Management Review.
Elena, C., Cecilia, C. and Chiara, C., 2016. Communication for preventing reputational risk. McDonald’s strategy and its impact on the Italian market.
Katal, A., Wazid, M. and Goudar, R.H., 2013, August. Big data: issues, challenges, tools, and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.
Kim, T.W., Park, K.H., Yi, S.H. and Kim, H.C., 2014, June. A big data framework for u- healthcare systems utilizing vital signs. In Computer, Consumer and Control (IS3C), 2014 International Symposium on (pp. 494-497). IEEE.
Kitchin, R., 2013. Big data and human geography: Opportunities, challenges, and risks. Dialogues in human geography, 3(3), pp.262-267.
Keim, D., Qu, H. and Ma, K.L., 2013. Big-data visualization. IEEE Computer Graphics and Applications, 33(4), pp.20-21.
Lopez, D. and Manogaran, G., 2016. Big data architecture for climate change and disease dynamics. The human element of big data: issues, analytics, and performance, pp.301-331.
McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J. and Barton, D., 2012. Big data: the management revolution. Harvard business review, 90(10), pp.60-68.
Michael, K. and Miller, K.W., 2013. Big data: New opportunities and new challenges [guest editors’ introduction]. Computer, 46(6), pp.22-24.
Oswal, S. and Koul, S., 2013. Big data analytics and visualization on mobile devices. In Proceedings of National Conference on New Horizons in IT-NCNHIT (p. 223).
Pradhan, S., 2018. McDonald’s India–plotting a winning strategy. Emerald Emerging Markets Case Studies, 8(2), pp.1-25

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