Data science relies on software programs to manage and analyze large datasets. Big data has become a buzzword, helping both industry and academia to be data-centric. Even researchers have begun to use data science because they need to analyze complex datasets. Ranjit Kumar, a researcher in Cambridge, started using big data to monitor the chances of depressions in the high seas turning into hurricanes in 2013. By 2016, his model was adopted by many countries to predict these natural calamities and increase the warning time, thus saving more lives.
The case of Kumar emphasizes the fact that various types of researchers, from unrelated backgrounds, are currently working in the field of data science. It also shows that many research and commercial organizations are currently generating vast amounts of data. To analyze this enormous stack of raw data and glean meaningful information from it, all organizations in every sphere of work are turning to Big Data.
Are we living in a data-driven world?
Data science has applications in many fields, which means that data scientists are in demand in academia as well as industry. According to IBM, more than 2.7 million jobs are likely to be created in data science and analytics by 2022. According to the European Data Science Academy, there have been over 3 million ads running for data scientists since 2015.
It also often drives positive change. For example, researchers at the University of Chicago developed a computational model for medical research called the Research Opportunity Index. This tool measures the difference between the resources used for illness and the relative burden on society. It provides a fair database assessment of where investment is needed to meet unmet medical needs. This index estimates the social burden of 1,400 illnesses. But that is merely the tip of the iceberg. Data contained in digital photos can determine the photographer’s location and periphery of the movement. Oceanographic data can change the land risk profile which affects asset values.
With great power, comes great responsibility
Being able to make data-driven decisions is important and useful. Often, big data includes personal information like social media activity, medical records, or patterns of movement. In most cases, data is collected in the background by smart devices. For somebody mining through all that data, it would not be overly difficult to piece together crucial information about the daily life of a user. It is for this reason that Big Data should be used ethically.
However, since ethics is still mostly an abstract concept when it comes to data security, enforcing it becomes difficult. In many cases, it is likely that ethics committees do not have the capacity or the means to address questions about the ethical use of new technologies like big data. Therefore, it is important to encourage open discussion in the community about potential risks. Digital social scientists agree that ethics in technology nowadays is all about making choices that safeguard user information, which anybody with access to that data, from individual researchers to large corporations, need to consciously make.
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