Big Data can be defined as large, complex sets of data, that are of such high volume traditional data processing software can't manage it. Examples of big data include GPS navigation, social media content, credit card details, and public health data.
Many of the concerns with big data arise from the fact that it contains a large amount of personal information. Issues over privacy are one of the biggest issues with big data. Some data is collected by manually imputing our details online, however, our data is also being collected when we are not aware of it, such as through algorithms on social media, tracking what type of content you interact with.
It is impossible for people to have 'informed consent' when it comes to generating big data, as it is not possible to know where the data you are producing will end up or how it will be used. You can listen to this Podcast on the ethics of big data to find out more about consent with big data.
The risk of personal data being leaked, or accessed by unauthorised people is a huge concern for many people. There are also concerns about personal data being misused once it has been imputed, such as through identity theft and fraud.
The issue of equity is also a challenge in terms of big data. Low and middle-income countries lack having the same access to big data analytics, that more developed countries with better resources can benefit from. Access to big data is essential for innovation, and this is particularly noticeable in healthcare when it comes to responding to a health crisis such as Covid-19. Governments of countries without equal access to big data find it difficult to have access to adequate data on their entire population, which is critical for development and policymaking.
Below is a map showing the percentage of the population in each country who have access to some form of internet, as can be seen in countries such as Somalia are as low as 2%
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