Lecture 11 – Big Data

For the lecture this week we focused on the use and limitations of big data. Collecting big amounts of data can be useful but it’s full of challenges in terms of physically collecting it, storing it, the purpose of collecting/having big data, distributing big amounts of data etc. There’s also privacy and security issues – the big data we’re talking about isn’t in reference to the football scores this year, it’s peoples’ geographical movements, election results, social media usage etc.

Big data has been classified using the ‘three V’s’ – high volume, high velocity and high variety. These are the three variables that need to be considered when talking about big amounts of data however, they only form half of the definition put forward by Laney. The other half focuses on the usage of the big data once it’s been collected. Not only are there technical problems when collecting big data, there are also operational meaning problems – information has to be useful and then implemented in the right way. Jacobs states that big data demands “cost-effective, innovative forms of information processing for enhanced insight and (big data-driven) decision making”. He also notes that the key pathologies of big data, are those of analysis. (Jacobs, 2009: 39).

Within the current technological age, big data is seen as a commodity – high amounts of data on certain things can influence decisions. This information can be used to make profit, effect politics and more. Although we may not realise it, most of us see the direct effects of big data everyday and how those who use it make it manageable and productive. For example, Facebook, Google etc. use computer algorithms to provide us with information we would find useful – showing us posts from people with interact with most over those we don’t talk to much, showing us results on Google based on our location to limit the amount of results our searches have. This is done so we have a better user experience but, these sites need to gather information about the user before it can hone big data down to a more manageable size for us, and how does it do that?

This is where issues around privacy and security come in. Legislation such as the Investigatory Powers Act and the Right to be Forgotten now form an important part of security measures being enforced all over the world. Internet service providers and mobile phone service providers are being forced to store customers browsing and usage history. This data can then be accessed by police and government department which could lead to profiling of specific people in society, there are also security issues with this big data as it will need to be stored in server facilities – making it vulnerable to thieves and hackers. It’s also concerning what governments and agencies can use this information for in the long-term as they don’t need to tell us when and why it’s being accessed. The Right to be Forgotten is also important to consider as it means we have the right to delete personal information from the records however, it’s not an easy thing to do and comes with complications – does it make it look as though we have something to hide?

The security and privacy issues are definitely something I want to consider for my approach to Assignment 2 on this module, where I will be writing a report based on a thesis of my design and answering a specific question of my choosing in relation to the subject matter discussed on the module and beyond.

 

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