Implementing Big Data Approach for McDonald's Business Growth Plan

This report proposes how McDonald’s can implement Big Data approach in solving some of their emerging business needs and create more opportunities. It identifies the varying customer and market trends and how McDonald's can adopt big data solution to benefit the business if implemented well.

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Big Data
Student Name
Institutional Affiliation

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Executive Summary
Big data have been attributed to recent technological advances and breakthroughs that have
minimized the cost of data processing and storage exponentially. It is now possible for
organizations and business enterprises to make more precise and accurate decisions because of
the accessibility and availability of large volume of data. In the past years, McDonald’s has been
considered to be behind in embracing technology as compared to its competitors. In 2017,
McDonald’s developed a business growth plan with the view of enhancing customer experience
through the use of technology and enhancing digital capabilities within the business. This was
regarded as the business priority. One of the ways to improve customer experience is by use of
big data analytics in order to determine the trends and customer changing preferences. The
business priorities that McDonald’s seeks to achieve is to improve and customize customer
experience and to have a digital menu that utilizes data. Identify the varying customer and
market trends. McDonald’s should consider adopting big data solution because it will greatly
benefit the business if implemented well.

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Table of Contents
Executive Summary.........................................................................................................................2
Introduction......................................................................................................................................4
Business Priority..............................................................................................................................4
Big Data Approach..........................................................................................................................6
High-Level Components..............................................................................................................7
Information and sources..................................................................................................................9
Big data technologies.....................................................................................................................11
Apache Spark.............................................................................................................................11
NiFi............................................................................................................................................11
Cloud Dataflow..........................................................................................................................11
Big Data Visualization...................................................................................................................12
Jupyter........................................................................................................................................12
Tableau.......................................................................................................................................13
Google Chart..............................................................................................................................14
Big Data Adoption Challenges and Governance...........................................................................15
Conclusion.....................................................................................................................................16
Reference List................................................................................................................................17

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Introduction
Big data can be described as a large set of data that us more complex and has been collected
from new sources. The data is vast and voluminous that can not be handled by the traditional
data processing software (Oracle, 2016). However, this data can be analyzed and used to solve
emerging business problems that could not be addressed before. In big data, the data amount
matters because it involves processing low-density, high volume of unstructured data. Data has
intrinsic value and until it is discovered it cannot be used. Additionally, it is necessary to
determine the reliability and truthfulness of the data. In today’s business environment, data has
become capital which should be analyzed constantly to develop new products and improve
efficiency. Big data have been attributed to recent technological advances and breakthroughs that
have minimized the cost of data processing and storage exponentially. It is now possible for
organizations and business enterprises to make more precise and accurate decisions because of
the accessibility and availability of large volume of data. This report seeks to propose how
McDonald’s can implement Big Data approach in solving some of their emerging business needs
and create more opportunities.
For several years, McDonald’s has been considered to be behind in embracing technology as
compared to its competitors. In 2017, McDonald’s developed a business growth plan with the
view of enhancing customer experience through the use of technology and enhancing digital
capabilities within the business. This was regarded as the business priority (McDonald's, 2018).
One of the ways to improve customer experience is by use of big data analytics in order to
determine the trends and customer changing preferences. One of the aims of embracing the use
of big data is to increase efficiency and reduce the cost of operations. The business serving more
than sixty-nine million customers daily and operating in 188 countries means that it collects vast
amount of data but what matters is how they use the data in yielding positive results.
Business Priority
One of the business priorities that McDonald’s seeks to achieve is to improve and customize
customer experience. The business has an intuitive mobile application that allows the customers
to order for their meal, pay, and get exclusive offers. When the customers are interacting with the
app, the business can be able to get vital information about the customers for instance, when,

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where, and how often they go to the restaurant. Do they go into the restaurant or do they use
drive through, and what they buy frequently? Based on the information collected the company
can use it to come up with complementary products and promote them via the app (Marr, 2018).
Secondly, the company seeks to have a digital menu that utilizes data. McDonald’s has
progressively and constantly provided new digital menus. These menus are not just fancy but
change depending on the real time data analysis and change depending on the weather patterns or
time of day. For instance, on a hot day, the menu promotes cold refreshing drinks that sooths the
needs of the customers at that particular period.
Thirdly, the company seeks to identify the varying customer and market trends. McDonald’s will
be able to get greater insights on the performance of the different restaurants by adopting a data-
driven culture that can also be employed in identifying best practices that can be implemented
(Watrous, 2014). The experience and food consistency are very important because the company
has adopted the franchise business model. As such, it is necessary that customers have the same
experience and taste of food in any of the McDonald’s restaurants regardless of the owner or
location.
These priorities seek to provide McDonald’s with a strategic framework for global growth. This
will enable the business to allow the customers to customize and personalize their meals to
present more opportunities both to the company and the consumer (Pal, 2017). This can be
achieved by further exploring more opportunities such as introducing fresh ingredients and
bolder flavors which can be derived through big data analytics.
The global digital strategy adopted by the business seeks to not only enhance customer
experience through mobile payment and ordering but also to involve and engage the customers
on various social medial platforms. Additionally, the company provides in-store wireless internet
access for its customers and have implemented global point-of-sale system. Customer
convenience is a very crucial factor that McDonald’s seeks to achieve by adopting the big data
technology.
The final priority that McDonald’s aims at achieving is establishing brand loyalty and customer
trust through social responsibility and sustainability efforts. In 2016, the business launched the
sustainability and social responsibility report which is McDonald’s first global targets and

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framework. The framework revolves around five major pillars including people, community,
food, planet, and sourcing which mirror that sectors that greatly matter to the business and
customers. In order to address this opportunity areas, McDonald’s need to adopt big data as a
business intelligence strategy because there is no single solution or silver bullet (Pramanick,
2013). McDonald’s in their 2017 annual report agrees that it is important to sequence elements
optimally and combine the various solutions available by using the customer-oriented initiatives
and implementing them at a higher level that it was done then.
Big Data Approach
Business intelligent solutions has no defined single method of deployment that addresses the
needs or problems of a specific company or business. One effective approach that increases the
possibility of success and minimizes risks is Big Data Approach. There exist numerous ways that
a business can use to implement big data solution in improve the efficiency and operations of the
company (Rack, 2018). However, as stated earlier, there is no single approach in implementing
big data strategies. It is important to develop an implementation plan beyond just understanding
infrastructure requirements. The implementation plan will aid in successful implementation of
the big data project. The following 8 steps that can be used to implement big data capability
successfully for McDonald’s.
Step 1: Get approval and sponsorship from the executive level. Big data project requires detailed
and fleshed proposal (Datameer, 2018). Therefore, there is need to have a dedicated team and
unlimited support from McDonald’s top management. This will increase the chance of having a
successful big data solution.
Step 2: Augmentation. Begin with the available data warehouse and determine and prioritize
extra sources of data and identify the appropriate hub-and-spoke technology. This is the stage
where the available options are analyzed and evaluated to get the right technology for
McDonald’s business needs.
Step 3: Prioritize customer value and experience. After identifying and prioritizing the sources of
data, connect them to customer preferences and needs. For instance, if a customer likes beef
burger and is passing by a new McDonald’s restaurant that offers beef burger, it would be
prudent to provide free vouchers to entice the customer to come in and try.

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Step 4: Work on incremental releases. After establishing the project team and the business
priorities, integrate new data hubs one at a time and work on incremental releases. This will
allow the business to adjust its processes incrementally and get greater knowledge on how that
data can be used to make decisions and influence business actions. The fundamental principle
here is start slow, learn, adjust, and integrate another step, and this makes it easier for the
business to understand all the possibilities.
Step 5: Connect company process to customer data. The ways products and services are
delivered by McDonald’s are changed by the opportunities presented by each new dataset. All
the decisions made at every business level should be data-driven from development of the
product, packaging, pricing, marketing, and advertising.
Step 6: Create Iterative action paths and processes. One of the challenges that should be
addressed when integrating new data sets is the need to execute a single report to solve the
problems without linking the solutions to actions. It is recommended to evaluate and assess the
various approaches before incorporating data sets. Brainstorm and ask the project team or
stakeholders the benefits that is associated with the particular data set and the steps that should
be taken form the lessons.
Step 7: Measure, test, and learn. Test the assumptions with every data set. For instance, the
mobile app or marketing system that McDonald’s uses should allow pushing customized and
personalized marketing messages. Big data, when used appropriately, will enable the business to
identify the adds that are doing well instantly and enable the company to optimized them (Moe,
2013).
Step 8: After successfully implementing the big data capability, start mapping big data
requirements and get more creative at every step of customer life cycle by asking questions such
as how customers identify new products, where customers get information to discover new
products, can the business promotional activities be linked to customer activity? (Adair, 2018)
High-Level Components
Figure one below describes how big data can be used to make real-time decisions.

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Figure 1: Big Data real-time decision-making infrastructure
(Source: Dijcks, 2012)
Figure 2 shows the batch processing infrastructure.
Figure 2: Batch processing Infrastructure
(Source: Dijcks, 2012)

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Figure 3 describes how McDonald’s can use big data to create model for buying behavior of the
customers.
Figure 3: creating Model for buying Behavior
(Source: Dijcks, 2012)
Information and sources
Big data solutions require massive amounts of data and it is important to identify the relevant
data sources. Many businesses are realizing that data that has been generated internally has not
be used optimally to its full potential in the past years (Joshi, 2017). Internally generated data
forms one of the data sources that will be used for McDonald’s big data solution. The company
collect huge amount of data daily from the mobile applications and this data can be analyzed to
gain greater insights and determine new trends and opportunities. The business can also leverage
new tools from unutilized unstructured data sources such as sensor data, customer service
records, emails, and security logs. However, more interest can be focused on identify new
external sources such as social media.
Social media is the most common source of big data. It gives valuable and deeper knowledge
about the changing trends and preferences of the consumers. It is the fastest and easiest way for
the business to gather detailed overview of their target population since it crosses all the

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demographic and physical barriers and is self-broadcasted and the business can be able to draw
conclusions, identify patterns, and improve their decision-making abilities (Gandomi & Haider,
2015). Social media platforms such as Twitter, Facebook, Google Plus, Instagram, YouTube, and
other interactive platforms provide qualitative and quantitative understanding on each user
interaction aspect.
The cloud can be used as another source of information for big data solution. This is because,
currently many companies and business enterprises are moving their data to cloud storage and
shifting away from the traditional data storage centers so as to accommodate unstructured and
structure data and allow the users to access data real-time form any location. Scalability and
flexibility are the major attributes of cloud computing facilitating storage of big data which can
be efficiently sourced on private or public cloud via servers and networks (Staff, 2016).
McDonald’s can be able to generate vast amount of data from the various cloud platforms and
analyze them to get greater business insights and identify new business opportunities (Hurwitz &
Kaufman, 2017).
The web is another popular source of big data because it is easily accessible and is widespread.
Internet/web data is commonly available to companies and individuals. Wen enormity ensures
diverse usability and is advantageous to many business enterprises because they would not need
to wait to come up with their own big data rather use the easily available internet data.
McDonald’s can also use the amalgamated modern and traditional databases to get relevant big
data. This amalgamation provides a way to create a hybrid model that requires low
infrastructural and investment costs. The databases can also be deployed for numerous business
intelligence reasons. They provide crucial information that when critically analyzed can be used
to improve business operations which translates to more profits.
Big data has been categorized into three major types including: structured data (data stored in
data bases), unstructured data (data with no clear storage format), and semi-structured data
(unclear and most of the time considered unstructured at a glance) (Sunil & Davies, 2016). Many
researchers have stated that big data have a defined value to any business and can help them
attain dramatic growth and reduce the cost of operations.

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Big data technologies
Data is exponentially growing because the world we live in currently is driven by data that it is
changing how organizations, businesses, and people carry out their daily activities (Olavsrud,
2016). Big data has been considered one of the driving changes in the business. However, its is
still a challenge to many institutions even the biggest ones who needs to streamline business
processes, improve customer experience, and enhance business decisions (Bayliss, 2016). There
are several big data technologies that can be adopted by McDonald’s. They include:
Apache Spark
This is a general and fastest engine that is used for processing big data because of its in-built
modules for machine learning, SQL support, streaming, and graph processing. It has the ability
to support all the big data languages such as Java, Scala, R, and python. Apache Spark takes a
shorter time both to execute and process data. This big data technology can be employed by
McDonald’s to track fraudulent transactions in real-time. This will enable the business to detect
any credit card that has been reported has lost and has been used in one of their payment points
(Hopping, 2017).
NiFi
This is a scalable and powerful tool that has the ability to process and store data from several
sources using a comfortable user interface and with minimal coding. It also has the ability to
automate flow of data easily between separate systems. It also allows writing own processor
using Java code. One of the ideal functionalities of NiFi is data filtering and extraction and has a
high-level security feature.
Cloud Dataflow
This is a cloud data processing service provided by Google that is integrated with model of
programing for streaming data processing and batch-based processing tasks. This tool is very
reliable in resource management and performance optimization and thus the business will not
have to worry about operational tasks. It supports dynamic resource allocation to minimize
latency and maintain high utilization efficiency. It supports continuous and batch stream
processing that makes is simple to express requirements for computations without having worries
about the sources of data (Oussous, et al.2018). Data engineers require these technologies to

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collect, filter, and set data patterns to facilitate data scientists to examine and explore them
critically and create models.
Big Data Visualization
Big data visualization entails data presentation in graphical way to make it easy to interpret and
understand. It not only presents the corporate graphs such as pie charts and histograms abut go
beyond to more complex representations such as fever charts and heat maps to enable the
decision makers to assess the data sets to determine any unexpected patterns and correlations.
Today, businesses and organizations gather and store large volume of data that would naturally
take many years for individuals to read not even interpret. Big data visualization depends on
advanced and powerful computer systems to ingest and process raw business data to generate
graphical representations that enable users to interpret and get insights of large amount of data in
a short time. There exist several big data visualization tools including:
Jupyter
This is an open source big data visualization tool that facilitates evaluation and real-time
collaboration. The user interface contains the fields for users to input codes and execute them to
generate visually-readable images depending on the chosen visualization technique (Fedak,
2018). The figure below shows how Jupyter interface looks like.

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Figure 4: Jupyter Visualization Tool
(Source: Fedak, 2018)
It enables sharing of notebooks between the teams to enhance teamwork and promote internal
collaboration on analysis of data. it supports R, python, Ruby, C#, Go, Java, among others.
Tableau
Tableau is one of the popular big data visualization tools because of its efficiency and
effectiveness in providing data visualization that is interactive for the outcome generated from
big data deep learning, operations, and AI driven applications (Rouse, 2017). The figure below
shows tableau interface

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Figure 5: Tableau
(Source: Fedak, 2018)
This big data visualization tool can be integrated with MySQL, SAP, Teradata, and Hadoop
making it an ideal solution for creating intuitive data representation and detailed graphs.
Google Chart
Google chart is one of the best big data visualization solution and is available freely and
continuous support is provided by Google. It provides visualization plethora from simple time
series and pie charts to multi-dimensional matrixes that are interactive (Rubens, 2017). The
figure below shows how Google chart looks like

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Figure 6: google Charts
(Source: Fedak, 2018)
Big Data Adoption Challenges and Governance
There are several challenges in the adoption of big data solutions for addressing McDonald’s
business requirements. These challenges include:
Interoperability: the business already has already invested in IT infrastructure. Big data
technologies may fail to integrate with the existing infrastructure (Balachandran & Prasad,
2017). This may require the business to reinvest in new infrastructure making the business to
incur more costs.
Manageability: it is a challenge to manage a big group of several nodes because it poses
problems to initial shocker and infrastructure management to organization. Vendors may provide
management, monitoring, and recovery tools of bi clusters but still a comprehensive solution is
unavailable which aids the novice users to rapidly adopt big data (Dhar & Mazumdar, 2014).
Security: data security is still a challenge while generating and accessing it. In an enterprise
context, data need to be controlled properly or otherwise it may give rise to data loss, compliance

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issues, data exposure to unauthorized users, and collection of data that is not of quality (Gohil,
2017).
Maintainability and scalability: lack of deployment, testing, administration tools, and IDEs
renders big data development slow and raises maintenance challenges. Big data requires several
expertise such as data modelling, application logic, and administration of infrastructure.
Conclusion
It is important to determine the reliability and truthfulness of the data. In today’s business
environment, data has become capital which should be analyzed constantly to develop new
products and improve efficiency. Big data have been attributed to recent technological advances
and breakthroughs that have minimized the cost of data processing and storage exponentially. It
is now possible for organizations and business enterprises to make more precise and accurate
decisions because of the accessibility and availability of large volume of data. There are several
big data sources including Social media, cloud, web/internet, and databases. Some of big data
technologies include Apache Spark, NiFi, and Cloud Dataflow. McDonald’s should consider
adopting big data solution because it will greatly benefit the business if implemented well.

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