Disadvantages Of Big Data

Big data, “information of extreme size, diversity and complexity”, is becoming one of the most significant technological trends that have the potentials for intensely altering how organisations make use of information to have customer experience enriched and their business models transformed. Big data is now seen everywhere. It has been applied in a wide range of fields of business and technology, and many organisations are starting to benefit from this innovation.

This report aims to investigate the Big Data phenomenon, including its characteristics, its current and possible trends as well as limitations and problems in the use of Big Data. The paper also examines how today business can benefit from Big Data and analyse the particular case

Big data analysis is currently overstated by many researchers and scientist, misleading about this predictive approach. One disadvantage of big data is that it still possesses inherent biases in how all data are collected and interpreted. Chris Anderson (2008) claimed that large enough data sets can uncover actionable correlation without deep understanding of the problem and the numbers “can speak for themselves”. However, this claim is not true and misleading readers about the innovative approach of Big Data. According to Crawford (2013), data is not objective by nature as they are defined and interpreted in accordance with human logic and interferences, so there are hidden biases in both the collection and analysis stage, presenting considerable risks in researching. This weakness leads to the fact that data mining in big data analysis cannot be replaced by hypothesis-driven research. Another disadvantage is that the larger the data set, the more likely spurious correlations which are not useful will be identified, making big data analysis more time-consuming and much less cost-saving than originally planned. Big data analysis also presents challenges on how to protect the individual’s

3.2 PROBLEMS IN THE APPLICABILITY

The implementation of Big Data analysis in organisations offer some challenges. Firstly, it requires special computer power. The traditional data management software cannot handle the large amount of data, so new software and hardware infrastructure will be needed to establish to process the information.

Secondly, the limited supply of data scientists and people who are capable of working with Big Data also creates personnel problems for oganisations. According to research by McKinsey Company, the US alone face a shortage of 140,000 to 190,000 people with deep analytic skills as well as 1.5 million managers and analysts to analyse big data and make decisions based on their findings. As Big Data is a new and developing area of research and studying, the current academic training and education are not aligned with required skills on working with messy sets and business questions. Higher education remains to be the most potential and only place for producing talents that have necessary qualifications (Silver,