Problems Of Using Big Data Help Us Understand What Customers Want?

1510 WordsAug 30, 20157 Pages
What are the problems in using big data to help us understand what customers want? Introduction Big data, defined as “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, personnel, 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…show more content…
Velocity. Massive data should be dealt with in time otherwise the quality of data will lose. Probably, Big Data can be the future of marketing. However, there are some problems in using Big Data because of these characteristics. If we don’t know how to address problems, the investment will be wasted but enterprises still search around in the haystack for the needle. Therefore, I will illustrate the problems and find out some solutions for Big Data. Problems Firstly, the main problem is deciding which data should be selected. The data, explaining customers’ desire and need, is important to be collected while most of the businesses are confusing 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 didn’t 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 complexity. Secondly, the result predicted by Big Data probably isn’t true at last. (MARCUS, & DAVIS. 2014).This phenomenon doesn’t happen by accident in many companies. Take Google Flu Trends as an example, they predicted that the Disease Control and Protection Center wasn’t able to control the flu spread quickly and effectively as time went on. Later, this conclusion was proved wrong. Hence, it means that future prediction contains inconsistencies
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