Why Do Big Data And Product Design Are All Important...

Introduction Big data, defined as â€Å"a popular term used to describe the exponential growth and availability of data†(What is Big Data? , n. d. ), has attracted considerable interest in many fields as it promises to offer a level of analytic detail that has not been reached so far. Whilst it is often promoted as the solution to many marketing problems, it has some significant disadvantages. Cost, data selection, problems relating to the interpretation of the data and difficulties deciding how to apply the new knowledge to existing products and product design are all important problems. Situation There is a line chart above from Google Trends which illustrates the searching record (Big Data) from 2005 to 2015.As can be seen, Big Data has†¦show more content†¦Probably, Big Data can be the future of marketing. However, there are some problems in using Big Data because of these characteristics. If the enterprises do not know how to address problems, the investment will be wasted while enterprises still search around in the haystack for the needle. Therefore, some problems will be illustrated followed by the solutions for Big Data. Problems Firstly, the main problem is deciding which data should be selected. The data, explaining customers’ desires and needs, is important to be collected while most of the enterprises are confusing about what data they should concentrate on. A recent Gartner report (2014) stresses that 64% of firms raced to plan or launch a Big Data project, though they did not have enough professional knowledge yet. To understand what customers need through Big Data possibly turns into the core of companies’ target. The large data volumes and different varieties of data lead to data complexity. Secondly, the result predicted by Big Data probably may not be true at last. (MARCUS, DAVIS. 2014).This phenomenon happen frequently in many companies. Take Google Flu Trends as an example, they predicted that the Disease Control and Protection Center was not able to control the spread of flu quickly and effectively as time went on. Later, this conclusion was proved wrong. Hence, it means that future prediction contains inconsistencies compared with reality.