Introduction

In prescribing a Big Data Analytics Process using Cloud Computing, we will explore these applications and processes for retail enterprises. The use case scenario of the retailer requires an analysis process to produce insights using big data this retailer produces.

The retail domain-specific dynamics will be accounted for in the recommendations that will be offered to GDey. The research starts with a description of the GDey business problem that needs to be addressed using big data and cloud computing. Next, a big data analytics process that uses big data and cloud computing will be prescribed. Specific data-driven insights that will be produced from the adoption of big data and cloud computing will be discussed in relation to how they align with GDey’s strategic goals and objectives. The implications of GDey engaging in different practices that are commonly found in the retail industry will also be covered.

Business Problem

Retailers today are facing key challenges due to the exponential expansion in the industry as well as the information available to customers. And are finding ways to use big data to make better decisions to improve their performance (Fisher & Raman, 2018). Shoppers are savvier now researching their purchases before committing. This has resulted in a decline in GDey’s customer loyalty and presented a huge problem for the enterprise as they find innovative ways to appeal to their buyers. Some of the key challenges this retailer is facing are customers exploring multiple buying experiences where they move between online and offline experiences. GDey’s customers are leaning towards other retailers who are great at facilitating these transactions seamlessly. Also due to the siloed marketing infrastructure that GDey currently have, it makes it expensive and awkward for them to get their message across to their consumers.

With the emergence of modern marketing, it has become necessary for GDey enterprise to engage with its customers across many different channels. These channels range from social media to SMS, and emails. This has required the need for multi-channel communications to engage purchasers and offer them a perfect buyer experience. However, the number of separate channels has resulted in GDey’s customer data becoming siloed. If the marketing department is not communicating efficiently and working together, GDey’s clients can become overwhelmed with conflicting or repeat messages. In addition, the flood of marketing communications can easily have the opposite of the intended effect and drive customers to competitors who have a clearer and more consistent message.

Big Data

Joshi (2015) describes big data as the latest trend in today’s time due to its exponential growth and its high demand for smart management. Big data is typically in one of these three varieties – structured, unstructured, or semi-structured (Joshi, 2015). Due to the variety of datasets in big data, the algorithm necessary for processing the data is dependent on the data type to be processed. Structured datasets are very organized and formatted data that are usually classified as quantitative data with rows and columns. This allows quick manipulation when working with structured data. Structured query language (SQL) is the programming language that is often used for managing structured data. Unstructured data on the other hand is qualitative in nature in the form of text, audio, or video files. The unstructured dataset is unorganized, can be stored using a NoSQL database, and can be challenging to analyze due to the absence of a predefined data model.

Big Data Analytics Process And Cloud Computing

Big data analytics: It involves using advanced analytical techniques to explore datasets from different sources and in different formats. The format could be structured, unstructured, or semi-structured (Sicen Liu et al., 2021). Whilst the size of the data could range from terabytes to zettabytes. Big data analytics promotes faster, better decision-making whilst ensuring cost reduction and operational efficiency. With flexible data processing and storage tools, companies can save costs in storing and analyzing big data to discover insights and patterns for better decision-making.

Cloud computing: It is the on-demand accessibility of computer system resources, especially data and cloud storage and computing power without direct active management by the client. According to Yang et al (2017) study on Big data and cloud computing, it provides basic support to address the issues surrounding shared computing resources including computing, storage, networking, and analytical software (Yang et al., 2017). To store files in distant databases, cloud-based storage is utilized instead of a local storage device.

Both public and private cloud computing services exist. At a fee, public cloud providers are able to offer their services through the Internet. There are some limited users who access private cloud services or opt for a hybrid. The private cloud services comprise a networked infrastructure that offers hosted services. The hybrid service option combines aspects of both public and private services. Although cloud computing is a new technology, it is very popular among corporations. Advantages and Disadvantages of Cloud Computing: Some of the advantages of cloud computing include enhanced productivity, cost saving, speed and efficiency, performance, and security. Cloud computing also presents major solutions for Big Data. Due to the accessibility nature of cloud-based software, either through the browser or native apps on any device, it allows companies from all industries to profit from using it. Transfer of data and settings from one device to another device by users becomes seamless.

Despite all the positives that come with cloud computing, security is a major security concern. Most important data are encrypted for protection but upon losing the key or upon a hacker accessing the set of machines, all the data is gone, and critical data would have been stolen (Inukollu et al., 2014). There is also a susceptibility of cloud-computing servers to power shortages and natural disasters. As team members access and alter data through a single gateway on the cloud, unintentional errors might be introduced across the system. To prevent this disaster from occurring, different encryption keys should be used on different machines. The key information should be stored centrally behind very strong firewalls

Use of Cloud Computing in Big Data Analytics Applications and Processes: Cloud computing plays a critical role in big data analytics in several ways. First, the cloud permits seamless scaling of resources based on the volume and size of the data being processed (Sandhu, 2022). Scalability allows flexibility which is critical for big data processing of vast amounts of data. Most cloud platforms have robust data security measures in place to ensure secure storage and processing environments for big data. Accessibility issues are addressed with cloud computing because the cloud allows big data access from any location at any time. This enhances collaboration among teams and contributes to more informed decision-making.

Importance of Big Data Analytics to GDey:

With the help of big data analytics, GDey enterprise can utilize their data to identify new trends that could help them acquire prospects. This could also lead to intelligent business moves, efficient operations, higher profits, and happier customers. Cloud-based analytics which are big data technologies can significantly reduce costs for big data storage whilst big data analytics increases GDey’s efficiency in doing business. The pace of in-memory analytics which analyzes new sources of the retailers’ data like streaming data from IoT supports corporations’ exploring the information immediately to help the company make fast, informed decisions. Big data analytics can also help estimate purchasers’ requirements and customer satisfaction. This will enable GDey to offer their customers what they want and when it is needed.

Prescription of Big Data Analytics Process That Uses Big Data and Cloud Computing:

While cloud computing is an IT service delivery infrastructure that is available in different service and deployment models, data analytics is a framework for processing data from multiple streams to create analytical models for delivering insights. The characteristics of cloud computing include the flexibility it offers organizations, its availability, and its scalability to sustain a variety of IT needs within corporations. The concept of big data analytics utilizes techniques in data mining, machine learning, statistics, and mathematics to build models that analyze data from big data sources.

The insights drawn from the analysis help companies attain a competitive advantage, understand consumer behavior, improve operational efficiency, and monitor organizational performance. By creating an awesome customer experience across all channels, GDey can use the right customer big data in the cloud to create an omnichannel customer experience that allows consumers to interact wherever and however they wish by incorporating real-time feedback across channels and devices engaging the customer on purchasing journey. The enterprise can create these seamless experiences where regular online customers still feel treated like regular customers when they visit onsite locations. And in-store systems already have a record of their online purchase earlier in the day.

This could be achieved through loyalty programs that allow relevant information about the customer to be collected. With the loyalty program, GDey can reward customers, and deliver relevant content. They can integrate data across all interaction points like online, in-store, and home service technicians thereby creating a joint multiple-channel customer experience. In addition, the right technology and communication procedure can ensure all arms of the marketing team are on the same page. Having a clear strategy will ensure all GDey channels are working together instead of against one another to save time and money for the organization.

Description of GDey Enterprise Data-Driven Insights

Specific data-driven insights were produced from the adoption of big data and cloud computing for GDey retail industry. Some of these benefits include keeping a 360-degree view of each customer. This could be achieved through the creation of a personal engagement that customers have come to expect by knowing each individual. The next benefit is price optimization. Here the insights gathered from upcoming trends offer the GDey the opportunity to know when, and how much they could decrease off-trend product prices.

Also, the insights retrieved from real-time big data analytics and cloud computing help them streamline back-office operations and maintain perfect stock levels throughout the year. Lastly, the voluminous customer service big data in the cloud from GDey recorded calls, in-store security footage, and social media comments could unlock ways retailers could enhance the customer service journey. Eventually, data-driven insights like hidden patterns, unknown correlations, market trends, and purchaser preferences, predict emerging trends, target the right customers at the right time, and cuts down on marketing costs whilst increasing the quality of customer service.

Conclusion: Implications of GDey Engaging the Retail Domain Practices

With the availability of big data about customers, and access to the right tools, retailers get to employ these to create new strategies to keep shoppers coming back for more. And big data gives the retailer’s industry trends to compare against. In the retail sector, both cloud computing and big data analytics applications complement each other in performance and importance. Cloud computing services will provide GDey with the appropriate and adequate environment such as network bandwidth capacities, and storage for managing big data processes as big data grow rapidly in the retail industry. The insights gathered from the analytics of big data will forge GDey in a positive direction to meet its strategic goal of increasing its customers’ lifetime value as the organization strives to increase its annual sales revenue.

Author: Adwoa Osei-Yeboah

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References

Fisher, M., & Raman, A. (2018). Using Data and Big Data in Retailing. Wiley. 10.1111/poms.12846

Inukollu, V. N., Arsi, S., & Rao Ravuri, S. (2014). Security Issues Associated with Big Data in Cloud Computing. Academy and Industry Research Collaboration Center (AIRCC). 10.5121/ijnsa.2014.6304

Joshi, P. (2015). Analyzing Big Data Tools and Deployment Platforms International Journal of Multidisciplinary Approach and Studies, 2(2), 4806-4810. 10.22214/ijraset.2018.4787

Sandhu, A. K. (2022). Big data with cloud computing: Discussions and challenges. Tsinghua University Press. 10.26599/BDMA.2021.9020016

Sicen Liu, Xiaolong Wang, Yongshuai Hou, Ge Li, Hui Wang, Hui Xu, Yang Xiang, & Buzhou Tang. (2021). Multimodal data matters: language model pre-training over structured and unstructured electronic health records. Paper presented at the 480-483. 10.1109/ICPICS52425.2021.9524249 https://ieeexplore.ieee.org/document/9524249

Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. Taylor & Francis. 10.1080/17538947.2016.1239771