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Abstract
In many research projects and surveys, the proprietors of the data are not the direct user for the advertising function. Sometimes organizations pay attention on a floating myth that the correct records of their venture exists, but the collected data got buried deep in a forgotten part of the customer aspect (generally the primary party data) and will take two hundred emails and a variety of lobbying to achieve it back again. Marketing use collectively information from various resources (first party/client data, third party data, real time data, historical data and surveys) which tend to don't have any universal key. There is not any common taxonomy in advertising, the only backend data which marketing can have are diverse datasets such as from supplier, internal team and customers. Other complicated thing is selecting the proper analytical framework, fashions & statistical approach, parameter tuning, deciphering outcomes, coding challenges, using myriad APIs and complicated SQL, scaling on large datasets , dash boarding the output, making reusable code, working on the linux command line , Etc. For a majority of these data, marketing department depends on reference books, blogs, public repos, mentors, ML APIs and boot camps. Marketing industry has to maintain updating coding & technical talents however more importantly, start speaking the marketing language, learn how to ask the right commercial enterprise questions, practice running via the enterprise structure to become aware of and reap the right statistics. These often ignored talents will serve us better in the long run for a company.