The demand for analytics in eCommerce data has increased significantly across markets as a result of the quick adoption of the ecommerce-based buying paradigm. But in the absence of complete end-to-end visibility and a clear understanding of consumer online behaviour, the ecommerce landscape has proven to be challenging. Business analytics on ecommerce platforms involves applying specialized methods and carrying out tasks to ascertain a companys requirements, then making adjustments and providing solutions that benefit stakeholders. Globally ecommerce platforms are enthusiastic about the promise offered by Big Data, Machine Learning, Artificial Intelligence and other new technologies, the misuse of these technologies could undermine their benefits. For example: Big Data algorithms raise serious concerns wherein they may discriminate consumers on the basis of age, gender, race, religion and other factors despite an increase in their accuracy. For example: Twitters simple AI image-cropping tool showed racial bias; and an Amazon AI-recruiting program showed bias against women. Targeted ads and dynamic pricing can be unfair and perpetuate bias. Images remain a significant avenue for bias as well. Businesses are prohibited by law from discriminating on the basis of gender, race, colour, religion or national origin in areas including credit, employment and housing.

a. What are the potential challenges in using data analytics on ecommerce platforms. Support your viewpoint with an example. (5 Marks)

b. Describe any six tools used for data visualization on ecommerce platforms. What are the benefits of using visualization tools? (5 Marks)

Answer each part in 500 words or less.