A white paper by International Data Corporation (IDC) reports that the global data volume has grown exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. It also predicts that the volume of data will reach 175 zettabytes by 2025. Zettabyte is a unit that is used to describe the amount of data, and 1 zettabyte equals 1021 bytes (that is, 1,000,000,000,000,000,000,000 bytes!). The world has officially entered zettabyte era and data is still being generated at a staggering speed. For example, in retail Walmart processes more than 1 million customer transactions every hour; in tech, Facebook users upload more than 350 million photos every day. With such an amount of data and the speed at which data is being generated, comes the desire to analyse and extract information from the datasets. Often the information that is extracted from a big dataset as a whole is much more useful than the collection of information extracted from small individual datasets. This desire gives rise to big data, which, as a field, studies the techniques that analyse and systematically extract information from datasets that are too large or complex to be processed by traditional data processing techniques.
Big data has attracted much research in the recent years and has seen a wide range of applications across various sectors. For example, big data techniques have been used to analyse current or historical data to make predictions about future or other unknown events, which is particularly useful to facilitate decision-making in health care, crime, security and natural disaster management. Big data has also been used to reveal relationships and dependencies or infer laws embedded in large sets of data. Such information can be very valuable and can inform business decision making. For instance, big data might reveal that customers who bought item A are significantly more likely to buy item B. This information would enable businesses to make more customised and personalised recommendations to their customers.
SkyMall is one of the world’s largest online marketplaces with over 100 million active users globally and over 2 billion product listings on their platform. SkyMall’s business model allows individuals or small business owners to open stores online and to sell products to customers worldwide. Online retail has had a phenomenal growth in the past decade – such a business model challenged the traditional offline retail industry, and many have chosen to move their businesses online for reduced labour cost, reduced operational overhead and much greater reachability to potential consumers. In 2019, SkyMall reported a transaction volume of 0.5 trillion dollar from their worldwide operation.
SkyMall is a platform provider. While it does not sell their own products directly on the platform or generate profits from the sale proceedings, it charges online store owners a monthly subscription fee and charges a small percentage from their sale transactions. SkyMall has the following responsibilities:
SkyMall has felt pressure from the heated global competition, especially competition from other online retail platforms that specialise in particular regions or countries. Those platforms do not have a global presence, but they understand their local markets and understand their consumers’ preferences and bahaviours much better than a platform like SkyMall which targets global consumer markets.
The Chief Information Officer (CIO) of SkyMall decided to incorporate big data techniques in their global operations. She would like to investigate how SkyMall can leverage big data to facilitate decision makings in daily operations as well as to gain a competitive edge against other competitors. One staff proposed the following two solutions:
You have been engaged by SkyMall to write a report that will be submitted to the CIO for deliberation.
Your report should address the following:
You have to complete this investigation and write a report for the CIO in the next four weeks. Since this is an initial investigation, the report should not contain in-depth technical details.
Please note that you might need to make some assumptions about the company in order to write this report. These assumptions should match the information in the case study and not contradict with the objectives of the report. They should be incorporated in the introduction of your report when you describe the organisation and outline the problem to be solved. Relevant assumptions should be incorporated when addressing tasks 2, 3 and 4 above. To avoid loss of marks, do not make assumptions that are not relevant or contradictory, or will not be used in your report discussion.
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