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Six Big Data Concerns Moving Forward

5 min read

Future concerns have become a focal point of the modern big data course curriculum. The opportunities that big data currently provides and will in the years ahead are exciting and yet somewhat unnerving. Canada and other countries are already discussing and even implementing legislation aimed at ensuring that corporations and even governments themselves use big data in a responsible manner.

1. Privacy

Misuse of personal information is a legitimate concern and perhaps the issue that receives the most attention in the mainstream. As our ability to collect, store and analyse information continues to expand, it will be increasingly difficult as an individual to keep secrets. Algorithmic profiling is already used to know you as you shop online, surf the Web and send emails and text messages.

2. Discrimination

Another issue that receives mainstream attention and one that is an extension of loss of privacy is discrimination. With accurate profiles who we are as individuals, service providers and even employers may opt to discriminate against us based on that knowledge. There’s also the concern of targeting vulnerabilities, such as a high-profile example in which a mailing list of rape victims was used to market specific products and services.

3. Security

Data breaches are a very real concern within the world of big data and an aspect of modern big data course curriculum that receives great attention. Even if you volunteer data, there’s no guarantee that the information doesn’t fall into the wrong hands. The world has already experienced a number of high-profile breaches in which fingerprints, social insurance numbers and financial information leaked.

4. Political Manipulation

The concept of fake news has received great attention recently. There is real anxiety worldwide over filter bubbles, bots and other mechanisms being able to cause political and social harm. Big data can be used against us to misinform and manipulate simply through the sheer volume of information.

5. Data Errors

Big data is so big that use of that information must often be automated. The concern here is that data errors and system errors do occur. There was a high-profile case in Australia in which an automated debt recovery system was misidentifying and unfairly targeting people, and because the data was so big and the system automated, it continues to prove quite difficult to correct.

6. Scientific Applications

Many a big data course is now focused on scientific applications as well. Research projects are relying on big data more and more, but not all are comfortable with this trend. A significant number of scientists have voice concerns that huge amounts of data present the serious risk of bias. There are formidable analytical and logistical challenges as well that can and are changing how clinical studies are conducted.

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