Big Data - Challenges

Big Data

Welcome back guys, I haven't seen you in a while😬. Today we will be speaking about Big Data.               

                           

Recently Big Data has come to the fore of the world of digital economy. While incredibly helpful for a wide variety of businesses and consumer experience, as the technology continues to grow exponentially it has begun to pose worrying challenges...

Ethical and Social Challenges

Privacy and Security 

In my opinion, privacy and security pose the biggest threat regarding Big Data. In an increasingly virtual world, more and more sensitive personal information is required such as name, age, phone number address and credit card details to name just a few. This constant demand for sensitive information begs the question of how much information Big Databases actually have. As big data continues to grow traditional privacy operations may be unable to keep up. This opens up the floodgates to potential leaks, breaches and cyberattacks. 

Accountability

Due to Big Data's complex nature and its involvement in many different technological facets such as prediction, detection and decision-making if it makes a drastic mistake who is there to be held accountable? Similarly, in the case of a cyber-attack, the question looms: who shoulders the responsibility for the breach?

Ownership

Navigating ownership issues in Big Data poses a significant challenge. Legislation within the subject of ownership of content within big data previously stated that you don't actually own it and thus cannot be stolen. The legislation regarding ownership is very contentious and varies from country to country. Notably, there is no unified EU legislation addressing ownership, adding a layer of complexity to this already intricate issue. 

Data Bias

(↑Honestly, guys I had never even heard of this challenge but I found it really interesting that this can happen🤔)

According to Statice biases in big data "occur when certain elements of a dataset are overweighted or overrepresented and can be unintentional or intentional." This leads to inaccurate and skewed results as well as extenuating stereotypes and even discrimination. For example, if the hiring data has a history of favouring white males, using that data might accidentally lead to more of the same outcomes, unintentionally replicating these patterns.

Thanks again for reading guys. I hope you found today's blog informative and interesting. Make sure to always keep your personal information safe and secure!!!! And I will see you next week.😃