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Big Data and New Decision-Making: Dominos Australia

COIT 20253 Assignment 3 Creating a Big Data Strategy 

The tasks 

 In recent years, the ability of Business Intelligence (BI) technologies to provide historical, curre nt, and predictive views of business operations based on the collection, extraction, and analysis of business data to improve decision has been the basis of several studies. More recently, “Big Data” and “Big Data Analytics” have further stirred the interest of researchers and practitioners alike. You are required to conduct market research and write a report on how Big Data can be used in Decision Support and Business Intelligence (DS&BI). 

In this assignment you are required to write a research report focusing on one of the following topics (which you have already chosen for presentation and have discussed with your tutor) 

  1. Big data and new decision-making techniques/models/approaches; 
  2. Organisational and cultural issues of the ‘Data-driven’ organisation; 
  3. Leveraging big Data for enhancing decision making and creating new business models 
  4. Social networks for exploiting knowledge or creating intelligence; 

The report should be well researched. Business strategy should be mapped clearly to business initiatives, objectives and tasks. You should able to define required technology stack and required data and analytics architecture for Big data for DS&BI including the Master Data Management (MDM). You should able to address advanced analytics requirements necessary to support the business strategy they have selected. And the role social media plays in organisations decision making process. You are required to discuss Big Data Value creation process. The report should address the followings: 

  1. Identify, create and discuss Business Strategy for a Big Data use case 
  2. Identify and align business initiatives, objectives and tasks with the developed Business Strategy. 
  3. Identify and discuss the required Technology Stack 
  4. Discussion on Data Analytics and MDM to support DS&BI 
  5. Discuss support of NoSQL for Big Data Analytics. 
  6. Discussion on different NoSQL Databases and its use in Big Data use case you have chosen. 
  7. Role of Social media in organisation's decision making process 
  8. Discussion on Big Data Value creation process. 

The length of the assignment is 3000 words. You are required to do extensive reading of more than 10 appropriate and relevant chosen topics in Big Data use case. Please do in-text referencing of all chosen readings. Newspaper and magazine reports should be limited to a maximum of 2. A comprehensive report covering all key aspects of the topic selected is required. Report should be extremely well supported with relevant case studies. Any assumptions made are clearly noted. The report structure should be clear, easy to read and logical, directly addressing the question. Suitable headers should be used throughout the report. Good use of graphics and charts should be made. No spelling, punctuation or grammatical errors. 

The Report (Important: The report makes up 100 marks - scaled to 40%) 

You will produce a report that aims to explain your Decision making using Big Data as per the layout and submission guidelines mentioned below. 

Report Layout and Submission Guidelines 

 Students are advised to observe the word count limit to 3000 words. 

  • Title Cover Page with clear details of course title/name, student number/name, lecturer &tutor 
  • Introduction: Introduction must clearly define the aims of the report. Students should include following in the introduction: 

Students need to define what they are going to talk about. Students need to show what they are trying to do with the selected use case ---the topic - the direction. Students should show what they are going to cover (and what they not, if required). Students need to give background information necessary to understand the report. Introduction must identify the project objectives. 

  1. Identify, create and discuss Business Strategy for a Big Data use case 
  2. Identify and align business initiatives, objectives and tasks with the developed Business Strategy. 
  3. Identify and discuss the required Technology Stack 
  4. Discussion on Data Analytics and MDM to support DS&BI 
  5. Discuss support of NoSQL for Big Data Analytics. 
  6. Discussion on different NoSQL Databases and its use in Big Data use case you have chosen. 
  7. Role of Social media in organisation's decision making process 
  8. Discussion on Big Data Value creation process.
  9. Reflections and Conclusions 
  10. Present your reflections about the decision making using Big Data (e.g. achievement and constraints). 
  11. Provide a brief summary of your findings 
  12. References (must complete by showing 10 references with their related in-text citations)



Executive Summary

Big Data is required in an organisation to get access of large volume of data. In the present study, Dominos, operating in Australia is taken into consideration. The study highlights business strategies that are taken by Dominos for Big Data use case. A proper analysis is done based on the business objectives, initiatives as well as tasks of the developed business strategy. Furthermore, the study deals with different technology stack that can be used by the present organization. Data Analytics as well as MDM is shown for supporting DS and BI. In addition to this, the study sheds light in supporting NoSQL database in the Big Data Analytics, besides showing different NoSQL databases that are used in Big Data. Social media role is highlighted that helps in making decision-making and process of data value creation is discussed broadly. 


Big Data can be defined as a large set of data that are analyzed computationally in order to reveal patterns, associations and trends. Big Data aids an organization to know more about the competitors, besides helping the company to adapt effective business strategy. Big Data possesses extreme volume, variety and velocity of a large amount of data. Big Data is not the only concern of this study but at the same time, it is supposed to analyze effective business strategies with case reference to Dominos, Australia and how the company is using Big Data. This current study will throw light on the influence of big data for providing better quality of food, market analyses and making decision for running the business efficiently. 

1. Discussion of business strategies for a Big data with use case reference to Dominos, Australia 

Dominos is now the world's largest pizza delivery chain having more than 10,000 outlets. Each year, Dominos delivers more than millions of pizzas. This hugely successful business has been possible only by the strategic management of the organization (Wang et al. 2016, p 750). However, the company is boosting up their management system and business strategies by applying smarter technologies. Big Data are the data that has huge volume which cannot be easily captured or analyzed by any common software. The data are generated from various sourcesand are structured as well as non-structured. Use case shows that the company is having issues regarding unstructured data. The reasons are the company has the presence of Omni-channel while driving sales, a large base of customers and had many touch-points to serve customers. The company also expanded their order and delivery process by telephone orders, online orders etc (Chen and Zhang 2014, p. 321)

Dominos multiple channel

Figure 1: Dominos multiple channel

(Source: De Mauro, Greco and Grimaldi, 2015, p. 101)

In order to resolve these issues, the company takes help of software named LOB, for replacing its age-old use of APM tool to enhance operational intelligence. This software helps the company to segment six different applications. These are interactive mapping, real feedback, the updated process for payment and supporting promotional activities. LOB software is used by present organization, Dominos for business transaction. Interactive mapping helps the company to calculate a total number of orders from all over Australia and count customer satisfaction (Bello-Orgaz, Jung and Camacho, 2016, p. 52). Feedback from employees helps to judge employee satisfaction. Dashboards help to keep track of set targets on monthly basis. Secured online payment helps the company to increase trust among employees. When a customer is using Dominos' service, LOB immediately records discounts that to be given, calculates the time of responding and mode of order- online or in-store. These are data is forwarded and LOB indexer relocates relevant data regarding customer response, purchasing time, number of sales etc in no time (De Mauro, Greco and Grimaldi, 2015, p. 101). Promotion supports tracks how effectively promotional discounts are influencing customer choice. Performance reviewing helps to improve sales activities and service quality.

Information system:

Big Data policies have helped Dominos to determine effective business strategies. Information system plays an important role in the market study and proper market study determines the future scopes of the business (Cascetta et al.2015, p. 30). Collection of information is also a part of strategic management. Point of sales system and data enrichment has added up to 85,000 of both structured and unstructured data sources in the company's information system. This is another example of their successful business initiative.

New stores and products:

In order to reach more number of customers, the organization has the plan to open a number of stores with more variety of .food items. That is why they have used the strategy for segmentation and targeting (Wamba et al. 2015, p. 241). This segmentation is mainly based on demographic and geographic factors. Big Data policies are effective to understand the taste of the customers of both Australia and other countries. Therefore, the company produces their food items according to the taste of the customers.

2. Analysis of business initiatives, objectives and tasks with developed Business strategy 

In order to progress in a business, the organization needs to take proper business initiatives by confirming specific goals and objectives. Domino's has pushed its brand through new and developing technologies consistently.

Multi-channel approach:

The organization has used the multi-channel approach to interface their customers for capturing a huge amount of data (Xu, Frankwick and Ramirez, 2016, p. 1564). Sensors such as RFID, Smart Devices and IOT and so on are used as sensors and machine to machine such as Chatbots are used by the mentioned company. It helps in generating data anywhere and at anytime. Moreover, the present company uses Big Data to enhance the food sector and make on-time delivery and making of foods. The data received from Big Data can be semi-structured, nano-structured and structured. For instance it is obtained as Csv files, Audio, Video, xml, json, Document and so on. 

Target customer:

The management of Domino's has identified their target customer in Australia so that they can provide quality service and modify their productivity. In this sector, they have set their target on the unified customer because it is easier to know the buying pattern of the customers residing in a household rather than each customer (Hashem et al. 2015, p. 111).

Digital platform:

Digitalization is the biggest business initiative of the Domino's and they have utilized almost all the digital platforms to advertise their brand including social media, such as Twitter, Facebook, Instagram, LinkdIn and so on. The company has now the infrastructure to deliver 55 to 60 percent of their order via online in various parts of Australia (Pedrycz and Chen, 2015)

3.     Identification of required Technology Stack

As stated by Wamba et al. (2017), technology stack stands as the list of technology that is used for running a building single application. Dominos might use Hadoop sas technology in order to store bulk amount data regarding processing power by using computing nodes and specially information from social media platform. Using these nodes help to restrict hardware failure and store data automatically without processing (Wang et al. 2018). By implementing Hadoop, Dominos is expected to large scale data, for example, 250 GB in just a single machine by using statistical languages, like R, Python etc. Moreover, each machine has 32 cores. This makes the procedure more efficient in map-reducing work (Gandomi and Haider, 2015). Though the technology has some drawbacks, for example, MapReduce programming is not the ultimate solution to every problem because it relies on file-intensiveness. Another issue is that as map-reducing requires advanced knowledge on analytic computing; it requires skilled employees and becomes difficult for entry-level programmers (Sivarajah et al. 2017). However, as the process is low cost and could be enlarged as much as required to add more data by increasing nodes, it becomes less administrative.   

4.     Data   Analytics and MDM for supporting DS and BI 

Introducing MDM in the enterprise has a positive effect on database and business intelligence (Wamba et al. 2017). Example of MDM is DevOps. It stands for the architecture of the classic data. If the organization does not possess the system of MDM and data analytics, then the manager of the Dominos finds the data fractured in stores of multiple data. Therefore, in order to create an integrated dimensional data within the BI system, the data user of Dominos can easily make use of the tool of data integration for integrating master disparate data in the system of multiple operations for building dimensions. Therefore, it can be said that BI stands as the place for integrating and consolidating master data. 

5.     Support of NoSQL Databases in Big Data Analytics 

As commented by Wamba et al. (2017), NoSQL stands as the database technology that is driven by Web, Big Users, Cloud Computing as well as Big Data. The NoSQL is basically made for overcoming the drawbacks that are faced by different organizations by the use of RDBMS (Relational Database management System). Some examples of the companies using RDBMS are Amazon, Google, Facebook and so on. In the present case, Australian Food Company, Dominos can make use of NoSQL database as it stands alternative to database SQL. Wamba et al. (2015) noted that NoSQL database does not make use of any table schemas. SQL requires table schemas and are not structured. Dominos can focus only on NoSQL as it scales horizontally as well as avoiding join operation of data. The present mentioned database is highly structured that generally consist of the relational database.

NoSQL is used by Dominos for Big Data Analytics are Cassandra or Couchbase, HBase, instead of the traditional RDBS. Sivarajah et al. (2017) commented that Big Data requires a flexible model of data that requires a structured database. Dominos gets the benefit of messaging infrastructure by using NoSQL database. In addition to this, NoSQL database within Big Data can be used for gathering, storing, monitoring as well as logging data by the data user. Dominos can use the present data for processing various data as well as monitoring the entire operation of the organization. The NoSQL database is beneficial in exploding the volumes of data, a variety of data as well as the increasing velocity of data. Furthermore, NoSQL database should be using for the analytics of the Big Data as it helps in scaling horizontally with extra nodes addition (servers of commodity database) to resource pool for easily distributing the load of Big Data.

Domino's big data

Figure 2: Domino’s Big Data

(Source: Sivarajah et al. 2017)

6.     Use of Different NoSQL Databases 

Database NoSQL aids in making the database solution more reliable and easily available. Different types of the mentioned database are as follows:

Key-Value Store

According to Bello-Orgaz, Jung and Camacho (2016), table of a big hash of values as well as the table are contained in the present type of NoSQL. A different example that can be used in the present case is Amazon S3, Riak and so on. In this type of storage, like Riak, it is important for the person to properly decide what he or she wants to store. In the present case, Dominos, operating in Australia can use the present key as it can be auto-generated and synthetic and the value stands as the large object, such as BLOB, JSON, Sting and so on. Cascetta et al. (2015) noted that in the present type of NoSQL, the hash table has the unique key as well as the pointer for the particular data item. In addition to this, identical keys are found in the different buckets. The present organization, Domino's, Australia can make use of the Key-value store as it would help to improve the performance of the food company due to its cache mechanism, which accompanies mappings. In order to read the value of the present NoSQL, it is important for the mentioned company to know the bucket as well as the key as the actual key stands as the hash. Business strategy can be enhanced by using the present type as it aids Dominos to identify data sources, deal with each type of data, forecast trend and analyze the collected data. 

Store of Document-based

According to Cheng et al. (2018), the document-based type of NoSQL aims at storing documents that are made of different tagged elements. CouchDB stands as the example in the present case. The data stands as the collection of the pair of key values. The mentioned company can use the Document-based type of NoSQL to store the collected document which would represent names of the specific food products of the stores. Moreover, the present type of NoSQL can aid the company to embed the attribute metadata that is associated with the stored content. The data sources include social media, demography, GPS, IOT, local events, traffic details, LOB and so on (De Mauro, Greco and Grimaldi, 2015). The coding that can be used by Dominos operating in Australia in the present case is JSON, XML as well as BSON.

Store of Column-based

As opined by Eichorn (2018), in the store of Column-based NoSQL, documents are stored in the cells, arranging the data in columns, instead of rows. It is easy to read as well as write the big data in a column, rather than in rows. The present organization can use the mentioned type of NoSQL as it would help the company to easily search any data, which are stored earlier. The columnar database has the capability to store data in all cells making the search faster. The data model in the present type includes column family, key, keyspace and column. BigTable of Google, Cassandra and so on stands as the example which is found to be inspired from the BigTable.   


Rigid form of the SQL, column, and table representation are not found in the Graph-based type of NoSQL (Huda et al. 2018). In the present type, flexible representation of graphs is used for addressing the scalability concerns. The graph structure makes use of nodes, edges as well as properties that aims at providing adjacency of index-free. In the current type of NoSQL, data are found to be transferred from the one model to another.

Use-case of Big Data

According to Lee et al. (2015), the use of big data is done by making use of graph database. The current organization, Dominos in Australia can make use of Infinite Graph and InfoGrid as it would be beneficial for the present company to represent complex information as well as hyperlinked information. Therefore, Graph-based NoSQL stands most appropriate for the present company to capture and generate huge data. The data can be then capitalized by Dominos for improving the marketing efficiency.

7.     Role of   social media in decision making 

Social Media plays a great role in aiding an organization to enhance their organizational productivity by drawing the attention of a large number of customers at a time (Matthias et al. 2017). In the present case, Dominos is consistently pushing its brand on the developing tech, which makes the company accept an order of Pizzas on smartwatches, TVs and so on. Facebook is also used by the Australian customers to order Pizza of Dominos. Sun, Sun and Strang (2018) noted that social media is making the food companies quick-service traditional restaurant to get involved in digital e-commerce. Australian foodies get aware of the new products, such as Chocó pizza, burst pizza and many more, which are being launched by Dominos, watching the ads on social media pages. Dominos also use social media for localizing the existing products. Vassakis, Petrakis, and Kopanakis (2018) stated that organizations use social media for advertisement purpose, besides improving the communication channel. The feedback on the social media aids the mentioned company to understand the customer’s habits and needs. Dominos offers are provided on social media for attracting new Australian customers, besides encouraging the existing ones. Social media, therefore, provides the chance for the customers of Dominos to easily get hold of the delicious and mouth-watering Pizzas of Dominos by scrolling the pages of Facebook, Twitter and so on. Therefore, internet increases the sales of the present organization, besides providing a quality experience to the customers. Moreover, social media aids the present company to analyze its competitors in the Australian competitive market. 

Domino's Competitors

Figure 3: Dominos competitors 

(Source: Chen and Zhang, 2014)

8.     Discussion on the process of Big Value Data creation 

Establishing a proper link between enterprise and Potential Big Data

As opined by Wamba et al. (2015), the mentioned process helps in driving the effort of the Big Data on targeting the existing processes as well as procedures. In addition to this, it helps in providing the recommendation in improving the decision quality of the data user. This would help in improving the precision of making the internal decision. The mentioned process would even help in improving the experience of the Domino's customers, besides reducing the IT support cost for business intelligence (BI) warehouse environment. The creation of the Big Data can aid Dominos, operating in Australia look at the monetization opportunities.

Assess interplay of technologies of Big Data

Once the link is established between the enterprise business and Big Data, it is important for the organization to navigate the BI or DW traditional technologies (Chen and Zhang). Dominos, the current organization can break the technology ‘Big Data' into four different buckets. The buckets include a layer of source data, Extract or ETL, Load Layer and Transform. In addition to this, the other layers are visualization layer and analytics layer. The technologies requires for creation of Big Data are forecasting, machine learning, analyzing, transforming, storing, cleansing as well as generating reports (Wamba et al. 2017). 

Recognising that model of execution is different for Big Data

Execution Model stands as the major factor behind the successful creation of the Big Data. The mentioned model requires proper planning and consideration (Sivarajah et al. 2017). In the present case, Big Data needs the proper combination of the expertise in the business domain, modeling strategic skills and strong analytics. It is important to note that the mentioned skills are generally absent in present organization and it only focuses on the function of centralized planning. In order to bring success to the creativity of Big Data, the mentioned company needs to include both unstructured as well as structured data under the environment of centralized DW or BI. Proper creation of the journey of Big Data would, therefore, benefit Dominos to shape the experience of the customers, launch new food items, and increase spending for developing the culture of the organization and so on.


It can be inferred from the above discussion that the technology of Big Data provides Dominos, operating in Australia to get access to large volume of data. It would help the company to increae sales as well as customer’s experience. The importance of NoSQL has highlighted in the study that it is highly structured and is best suited for storing Big Data. Different types of NoSQL database is mentioned such as Key-value store, Document-based, column-based and so on. Social media role is mentioned that aids the manager of Dominos in decision-making. In addition to this, the process of the creation of successful Big Data is provided vividly in the current study.

Appendix 1: Use case


Source: dominos (2018)

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