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There is no denying that we live in an exciting time. The blurring of consumption and production in an online context is leading to never before seen possibility. Historically (in the context of modern western civilisation), the average individual who did not have a publishing contract could...

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New technologies and the globalised nature of communication and information are shifting paradigms and altering the ways in which we, as a public, consume entertainment. People are demanding their content instantaneously and in a personalised format (Bruns, 2012). This has reconstructed the manner...

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New technologies and the globalised nature of communication and information are shifting paradigms and altering the ways in which we, as a public, consume entertainment. People are demanding their content instantaneously and in a personalised format (Bruns, 2012). This has reconstructed the manner...

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A few weeks ago I went on a beach trip with friends. While in the ocean, I noticed a few teenage girls walk down to the flags and proceed to whip out their phones and take some photos. Nothing wrong with that I thought naively. They’re just capturing this nice moment in their collective...

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A few weeks ago I went on a beach trip with friends. While in the ocean, I noticed a few teenage girls walk down to the flags and proceed to whip out their phones and take some photos. Nothing wrong with that I thought naively. They’re just capturing this nice moment in their collective...

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Thanks to the Internet and an ever-growing number of social media sites the amount of online data that is being churned out is growing exponentially creating ‘Big Data.’ Big Data is information that is too diverse, fast-changing or massive for conventional technologies, skills and...

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Firstly I think that I should say that the development of new media is something that I have always found interesting. Not so much the technological side of it but more where it fits in a social context. I first thought that the prevalence of new media, particularly social media, was bad, that it...

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Big Data: Seeing the Future

I think many of us are naive about data, it’s stored, it takes space and there’s a lot of it. But it’s how we use these nebulas of code and information that intrigues me. The idea of prediction based of results from analyzed and sifted data is exceptional, predicting crime hotspots based of countless accounts of activity on the internet, predicting sickness in areas, predicting stock prices and advertising, Big Data is making it easy, set the algorithms, and let the machine do the work, what it spits back out is likely extremely valuable information which is aiding almost every aspect of human life.

 What’s more about Big Data is the fact that it’s simply not disappearing anytime soon, the internet will continue to grow and prosper, harboring more and more useful information which, It will be interesting to note the role Big Data plays 10 or 20 years from now. As technology and medical knowledge advances it naturally becomes digital, and with algorithms sifting through this information and making connections it’s likely cures and innovation will come from it. If we cast aside the wonders Big Data is doing for economies, big businesses and irritable advertising we can all agree that Big Data is a very good thing. It’s also interesting to look at the value of data itself, Big Data is almost a currency in itself. Imagine companies selling their clumps of data to other companies, where they subsequently sift through it for diamonds, a crazy concept. 

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Liam Devonshire

References

Woodford, Darryl, Katie Prowd and Axel Bruns. (n.d.). “Telemetrics: Towards Measuring Social Media Engagement with Television.” Accessed May 10, 2014. http://blackboard.qut.edu.au/bbcswebdav/pid-5234702-dt-content-rid-2118244_1/courses/KCB206_14se1/Woodford%2C%20Prowd%20and%20Bruns%20-%20Telemetrics%20Towards%20Measuring%20Social%20Media%20Engagement%20with%20Television.pdf

Eggers, W, Hamill, R 2013 “Data as the New Currency”. Accessed May 3, 2014 http://d2mtr37y39tpbu.cloudfront.net/wp-content/uploads/2013/07/DR13_data_as_the_new_currency2.pdf

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Big Data, Big Predictions

‘Big Data’, sounds like something technical and boring to most people, but what they aren’t acknowledging is the big affect it actually has. “Whenever you click on a website, whenever you make a purchase, whenever a light switch is turned on and off, it’s generating a piece of data” (The Economist, 2012). All this data is the reason why all the advertisements, youtube video and facebook page suggestions you come across online are all in your interest; because big data assesses what you like and makes sure you’re continually engaged with it.

  Image sourced from: http://blog.joelrubinson.net/2014/04/big-data-big-research-possibilities-emerge-at-arf-conference-2014/

Today I clicked onto an ad that was of my interest suggested to me through email. It was a deal on OurDeal and as I was reading more information on it, there was a pop up to say “Someone purchased this one hour ago in Bracken Ridge.” I didn’t really understand why they’d think I’d care, but it was interesting to see how someone’s activity near my location was shared with me. This made me recognise how clever big data is, and the potential it has to take control.

  The knowledge big data generates is so informational, that it is often being used to predict answers. Predicting answers and information is a golden tool for companies. They can predict how people consume, think and do things, which pretty much hands over the answers for companies and their marketing schemes. The prediction can sometimes be so detailed they can literally discover how to best influence you to buy more (Sieger, 2013). Big data isn’t just predicting answers to people’s actions however, but answers to many wonders. For example, Australian energy company Energex was able to predict the best location to build its’ power grid. Another platform the use of prediction through big data is very popular is on television.

  Various television shows, particularly reality series, often engage with their audiences through social media. Shows such as Big Brother, X-factor, The Voice and My Kitchen rules encourage their audiences to engage with the show through the social media platform, Twitter. An example of how to measure TEI (Twitter Excitement Index) of a show was done by Woodford, Prowd and Burns (2014), through the premiere episode of My Kitchen Rules. Lasting 115 minutes and gaining over 150 tweets per minute that used the hash-tags and/or account names and with the use of algorithms, a TEI of 1.09 was made. Not only is the amount of audience engagement intriguing, but also what the companies can do with all this information. As researchers are capable of observing and analyzing exactly what users are saying, they can then use this information to make the show exactly how the audiences want it.

  To sum up, big data is a big deal that most people aren’t acknowledging. It’s able to predict such powerful strategies, especially for companies in need of a marketing tool. Overall, big data is always somewhat following you, whether it be suggesting sites in your interest online, or eliminating the guy you don’t like on My Kitchen Rules.

References:

  Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc. Accessed May 10, 2014. (Available on CMD)

  The Economist. 2012. The Dark Side of Big Data. Accessed May 10, 2014. https://www.youtube.com/watch?v=raJOkguPrH4

  Woodford, Darryl, Katie Prowd and Axel Bruns. (n.d.). “Telemetrics: Towards Measuring Social Media Engagement with Television.” Accessed May 10, 2014. http://blackboard.qut.edu.au/bbcswebdav/pid-5234702-dt-content-rid-2118244_1/courses/KCB206_14se1/Woodford%2C%20Prowd%20and%20Bruns%20-%20Telemetrics%20Towards%20Measuring%20Social%20Media%20Engagement%20with%20Television.pdf

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Huge data!

Everything we do online (and in some cases offline) somewhere is recorded and made into a database that is known as Big Data. Big data can be utilized for any number of uses,

“Everyday, we create 2.5 quintillion bytes of data–so much that 90% of the data in the world today has been created in the last two years alone.” This interesting insight is provided on the IBM.com website. For those of you who don’t know, IBM are a massive international technology and consultation company, one of the many companies interested in utilizing big data. IBM being the huge company they are would be foolish to not investigate the use of this new method.

Big data can covers 3 dimensions having expanse variety of data categories, including structured and non-structured data (eg audio, text, streams etc), it has velocity of streaming at high speed even though it’s crunching huge numbers of data. Lastly there is a large volume of data, an extremely large volume of data, hence the name big data. These three dimensions that big data span over give it the edge of any other type of data analysis. SAS considers another two extra dimensions, complexity and variability. Complexity refers to the multiple sources and platforms that the data can originate from and be developed on. Variability refers to trends in social media that big data crosses. This is where the velocity and variety intersect at certain periodic points.

There are several advantages to businesses that utilize big data, they can use it to better understand their customers and in turn market their products or services better to reach them and grow their business. As well as determine the cause of failures and consequently address such failures.

SAS are an analytics software company, one of the world’s largest private software companies in the world. They posted this video almost a year ago 

A company that used SAS’s big data analysis techniques is UPS (United parcel service) they have already capitalized on these strategies and are reaping the rewards. UPS tracks 16.3 million packages a day for 8.8 million customers, averaging 39.5 million requests for tracking every day. This incredibly large number translates to 16 petabytes of data. This diagram shows the size of one petabyte. These 16 petabytes would be impossible to sort through until the alleviation use of big data.

(source: http://hwzone.co.il/hwzone.co.il/originals/news_images/PB-disc5.jpg)

Using tracking telematics sensors in over 46,000 vehicles, data is gained from the vehicles. Including speed, direction, braking, navigation and driver trainer performance. In 2011 the company saved over 8.4 million gallons of fuel by cutting 85 million miles from every day routes. This information would not have been possible without big data.

In a world with so much data, that is exponentially increasing, we need big data analytics to better understand the vast numbers of data. Without big data analytics the data will be rendered almost useless.

  References

Eric Lundquist. 2014 "Big Data Can Solve Small Problems." http://www.eweek.com/database/big-data-can-solve-small-problems.html Accessed 3rd May 2014

Author Unknown. “IBM” Accessed 3rd May 2014 http://en.wikipedia.org/wiki/IBM

Thomas H. Davenport and Jill Dyche, "Big Data in Big Companies," May 2013.

SAS. 2013. "Big Data, What is it and why it matters" www.sas.com/en_us/insights/big-data/what-is-big-data.html Accessed 3rd May 2014

IBM, "Bringing big data to the Enterprise" Accessed 3rd May 2014 www-01.ibm.com/software/au/data/bigdata/

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Post Eight: New Media and Big Data

Due to the introduction of the internet, social media, webpages- anything that creates data is contributing to big data. “Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured.” (Laney 2001) Without data, there would be no saved content, which would result in computers being useless, similar without big data, the internet would not exist.  Every time a website is opened or a status is updated, it generates data; big data is being used to make sense of all this data.

  Big data is often broken down into the three v’s; volume, variety and velocity. Volume focuses on the size of volume of the data, how big a status update or tweet is in terms of data. Variety looks at data coming from a range of sources now instead of just one, it can be structured or unstructured, internal or external and/or from a whole multitude of places. The velocity focuses on the speed data is stored and processed. Big Data was original used to reflect on previous results e.g. a company’s sales over the past year, however now it is being used to predict what will happen. Predicting failures occurring before they even happen, (Pearl 2012)

    While the evolution of new media is great, it also has a down fall in terms of big data. In 2011, 1.8 zetabytes of data was created and replicated to make that simpler, it would take 57,500,000,000 32GB iPods to hold that much data. (The Economists 2012) It is almost concerning that the humans are creating this much data at such a rapid pace, but what’s even more concerning is that it’s only going to increase more and more. With the constant update of technologies, data is going to get created and replicated a lot more frequently. It’s not just the increase of data that is being stored that’s the issue though; privacy is no longer available when using the internet. Without changing settings, anyone with Facebook can see your personal details and more product policies are allowing third party companies to track internet activity. If you post something online, it is going to be stored as data and accessible even if is deleted. (The Economists 2012)

  Though there are two sides to big data, I believe that without the proper introduction of privacy and tight security networks, the internet could eventually become too revealing to use. If it’s possible to pull out images and statuses that were deleted from the archives, then what could that mean for the future generation. Everyone has put up their fair share of silly information or imagery up online, but could this data come back to haunt us in our later lives?

Laney, Doug. 2001. 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group Inc. Accessed 11th May 2014.  http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf

  Pearl, Daniel. 2012. Volume, Velocity and Variety of Big Data. Accessed 11th May 2014. https://www.youtube.com/watch?v=FidKQsJdIG0

  The Economist. 2012. The Dark Side of Big Data. Accessed 11th May 2014. https://www.youtube.com/watch?v=raJOkguPrH4

Woodford, D. 2014. “KCB206 Internet, Self & Beyond.” Week 9 Lecture Notes. Accessed 8 May 2015. https://lecturecapture.qut.edu.au/ess/echo/presentation/11dcf106-e7c9-44eb-a753-74dfff689043?ec=true

Josh Hanson n8871124

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Efficiency and Big Data

On face value, Big Data plays out like some nightmarish dystopian vision: Every click of a button and every tap of a button is recorded and then...well the vision (at least the nightmarish part) stops there. What follows is the intriguing question of what all this data is being used for. One could argue that it's being used to help tailor the individual needs of consumers shopping online, synthesize information about the world around us and help researchers with otherwise incalculable evidence. However I would argue that one of Big Data's most important uses is creating efficiency in a variety of contexts.

In my first year at university, I was constantly frustrated because I always found myself rushed in the mornings, leading to an unnecessarily stressful day. Waking up earlier wasn't an option because I really, really loved spending time with my bed and setting an alarm early would guarantee the hitting of the snooze button...a lot. So I decided to time myself doing every single activity which I might do on a particular morning for seven days. I calculated the averages, and found myself with a bunch of statistics which helped me measure down to the second how long I would need to get ready in the morning. I can now set my alarm so that I will be on time to whatever hideously early class I have that morning, but my brain can't trick my sleepy self into hitting that snooze button, because I will either be late or have to skip breakfast (and a day without bacon and eggs is a sad day indeed).

(Source: http://i.ebayimg.com/00/s/MjgxWDUwMA==/z/o5IAAMXQRDxREd9F/$T2eC16VHJGIE9nnWqrI7BREd9E4F1g~~60_1.JPG?set_id=8800005007)

In a similar but much more complicated way, Big Data can help all humans become more efficient. Of course, it's not about the data itself, but how that data is used. Consider UPS, who have installed telematic sensors in their vehicles to collect data. Information such as vehicle speed, direction, braking and drive train performance are monitored in conjunction with online map data to optimise the drivers' route structures. In 2011, UPS saved over thirty two million litres of fuel (SAS, 2014).

It seems that Big Data comes with some seriously big results, and the potential is endless. Some estimates predict that the correct implementation of Big Date could save the United States healthcare system up to three hundred billion dollars annually (Saracino, 2013).

We live in an exciting time, and with any luck, Big Data can be used to optimise the efficiency of everything we do.

References:

Harrington, Stephen. 2013. “Ch 18 Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt & Cornelius Puschmann, 237-248. New York, NY: Peter Lang.

Saracino, Adria. 2013. "Interesting Ways Business Use Big Data To Improve Personalisation". http://www.clickz.com/clickz/column/2263262/interesting-ways-businesses-use-big-data-to-improve-personalization. (Accessed 11/05/2014)

SAS. 2014. "Big Data". http://www.sas.com/en_us/insights/big-data/what-is-big-data.html. (Accessed 11/05/2014)

Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.

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We Watch, We Tweet and We Discuss

Image obtained: http://www.huffingtonpost.com/2013/10/15/life-before-smartphones_n_4100477.html

For the final blog post for this semester, we have been asked to discuss about new media, big data and telemetrics. It feels like not long ago, watching a television program was just a singular activity. However, it seems so hard to recollect when was the last time I actually watched a television program without simultaneously being on my laptop or smartphone. I remember I was 9 when the first season of American Idol premiered. It was one of the first few programs on television that encouraged audience members’ participation. By calling a designated number or sending a text message, audience members had the power to save a contestant from elimination.

In recent years, we have observed how social networking sites have slowly become an integral component in audience member participation with television programs. Twitter in particular, has become one of the main platforms utilized on television programs to engage with audience members. One of the main reasons behind it is that, Twitter was the first platform whereby hashtags were invented and became a method to stimulate discussion. According to Harrington (2013, 240), “Twitter's relationship with television concerns the opportunities the platform affords users for connecting with other viewers in real time, and engaging in a live, effectively unmediated, communal discussion of television programs.”

Image obtained: http://rebloggy.com/post/my-gif-gif-american-horror-story-ahs-frances-conroy-balenciaga-fasion-coven-amer/75014104387

One of my favourite television programs was the American Horror Story series. Each season takes place in a different setting. For the recent season, the series focused on characters and stories related to covens. Every Wednesday, the creator of the show Ryan Murphy, posts tweets on Twitter to encourage audience participation and stimulate discussions. In order to keep track of the tweets sent out by each audience member, Ryan Murphy urged them to accompany each tweet with the hashtag, #AHSCoven.  Personally, I rarely feel the need to participate on Twitter, unless I am contacted by one of my friends. However, there was one particular episode of the show that I felt was so epic that I had to share the scene (as seen above) on Twitter. I included the hashtag, #AHSCoven and managed to engage in discussion with other Twitter users/ AHSCoven followers. 

Image obtained: Woodford, Darryl, Katie Prowd and Axel Bruns. (n.d.). “Telemetrics: Towards Measuring Social Media Engagement with Television.” 

In this week’s lecture, we learned about the importance of big data and telemetrics in measuring audience members’ engagement with television. Woodford, Prows and Burns (n.d.), discussed the advantages of using Twitter Excitement Index over traditional volume metrics. One of the main advantages is that tools such as the Twitter Excitement Index provide more in-depth understanding regarding audience members, rather than just measuring the size of audience members tuning in to a particular show.  Harrington (2013, 245) supports that statement by stating, “Twitter enables researchers to observe the activities of audience members at real-time, analyse the type of discussions they are engaged in.”

In retrospect, I feel that the inclusion of new media, big data and telemetrics have helped revolutionised television programming for the better.  Instead of being perceived as just numbers and figures, the entertainment industry now has the ability to view their audience members as individuals with characteristics which fit into a category. This knowledge would help change the landscape of television programming and enable companies to predict the success and failures of a particular television program. 

Hui Zen Lim

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References

Harrington, Stephen. 2013. “Ch 18 Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society, edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt & Cornelius Puschmann, 237-248. New York, NY: Peter Lang. Accessed May 10, 2014. http://www.qut.eblib.com.au.ezp01.library.qut.edu.au/patron/FullRecord.aspx?p=1632466&echo=1&userid=c3Xw17fqmwlt3ZS7F8tSrg%3d%3d&tstamp=1399782147&id=C49936EA245F6FFF0B151DE95DA82DD5C87CF8BC

Woodford, Darryl, Katie Prowd and Axel Bruns. (n.d.). “Telemetrics: Towards Measuring Social Media Engagement with Television.” Accessed May 10, 2014. http://blackboard.qut.edu.au/bbcswebdav/pid-5234702-dt-content-rid-2118244_1/courses/KCB206_14se1/Woodford%2C%20Prowd%20and%20Bruns%20-%20Telemetrics%20Towards%20Measuring%20Social%20Media%20Engagement%20with%20Television.pdf

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#data #twitter

Stephen Harrignton states “A significant amount of attention in the media industry over the last decade has been directed towards the idea of 'convergence’” (237, 2013). If the past 10 weeks have taught us anything, it’s that we are living in an era of convergence. No longer do we stick to a structured set time of viewing a TV show. Now we can access television shows at any convenient time and on whatever device we choose. With this idea of convergence comes the ubiquitous new media site, Twitter. It is, arguably, the most influential social networking site. Several TV shows targets audiences persuading them to tweet along while watching the episode. They set up and use specific hashtags and even go so far as to get the cast from the show to tweet along during the episode. Twitter is a valuable tool to get information from a wide range of people and audiences.  It is best to analyse the different levels within Twitter to get a better understanding of how this information, or data, can be best used.

Axel Bruns and Hallvard Moe state that there are “three key layers of communication on Twitter: the micro level of interpersonal communication, the meso level of follower-followee networks, and the macro level of hashtag-based exchanges” (16, 2013). This macro level of hashtag-based exchanges are what most TV shows rely on for engaging audiences. Axel Bruns and Hallvard Moe also suggest “The communicative flows which result from the establishment of active hashtag exchanges, at least in the short term, are usually less predictable than those enabled by follower-followee networks-but they are also amongst the most visible phenomena on Twitter, and most accessible to research” (18, 2013). Research is essential to collecting big data on audiences to enable websites and different media to specifically and accurately target audiences with new content. This data is not only used to target audiences but to also predict new trends. Eric Siegel suggests “Learning from data to predict is only the first step. To take the next step and act on predictions is to fearlessly gamble” (15, 2013).

This is an interesting quote because it can accurately explain how not every bit of data can be spot on. Essentially every click, every sent message, every searched item is a piece of information that create big data that can be analysed. Eric Siegel states “as data piles up, we have ourselves a genuine gold rush. But data isn't the gold. I repeat, data in its raw form is boring crud. The gold is what's discovered therein” (4, 2013). So from every single traceable movement everyone makes there is critically important information hidden within. The best part for data analysts is that Hashtags on Twitter, which are constantly growing, can make getting data and information easier and even produce more accurate results, even if they can be a little less predictable. 

Harrington, Stephen. 2013. “Ch 18 Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt & Cornelius Puschmann, 237-248. New York, NY: Peter Lang

Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc. 

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Is New Media Beneficial to the Public?

The literature surrounding new media paints the picture of a technological revolution encompassing a giant power shift between traditional media producers and ‘the people formerly known as the audience’. Traditional media producers are in agreement with the literature. Rupert Murdoch confirmed this when speaking to American newspaper editors, “They want control over their media, instead of being controlled by it”. The proverbial they in this case is; as Rosen, J. (2006) describes, “The people formerly known as the audience”.  Additionally, Ann Kirschner’s (vice president of programming and media development for the National Football League) view of the challenges facing traditional media producers, as a result of new technologies, is a simple one stating, “We already own the eyeballs on the television screen. We want to make sure we own the eyeballs on the computer screen”. Rosen describes Kirschner, Murdoch and co as delusional. Operators have an exaggerated sense of their power over viewers. New media is tearing these ideologies down. This means that the social practices surrounding media are changing. New media is reconstructing the very notion of self.

  Rosen describes the traditional process of digesting media as vertical. Wherein we were at the end of a one-way media system, completely isolated from one another, digesting content from a few firms paying high entry fees to “Speak very loudly” to audiences. In the earlier system, shooting, editing and distributing video content belonged to traditional media producers, the only ones with the financial capabilities to build a viewership in their own image. In the past people were individually attached to large central agencies but not across to each other. The current landscape of new media is greatly at variance to this traditional model. There is now a horizontal relationship occurring, between viewers and former viewers now turned contributors. According to Green and Jenkins (2011), these changes are shifting how we value audiences, how we understand what audiences do, and how they fit into the networks of capitol, both economic and cultural, that constitute the current media landscape.

  This paints a positive picture for the general public with regards to the landscape of new media, however, there is a down side to these shifts. Members of the public are uploading masses of personal information that commands a high price from corporations for the use of delivering more efficient marketing approaches. Essentially, the information we willingly upload to social media is sold to interested corporations for improved exploitation of the masses. 

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Big Data Application

  Thanks to the Internet and an ever-growing number of social media sites the amount of online data that is being churned out is growing exponentially creating ‘Big Data.’ Big Data is information that is too diverse, fast-changing or massive for conventional technologies, skills and infra-structure to address efficiently. i.e. The volume, velocity or variety of data is too great.” (Thomas 2014) Therefore, conventional methods of calculation and analysis cannot handle the sheer magnitude of data being produced. This video explains big data well. Though big data may seem impractical due to its size and difficulties it has many applications across a range of industries. 

Arguably big data’s most common application is in predictive technologies. This prediction that big data has allowed particularly serves big business “empowering [them] with an entirely new form of competitive armament.” (Siegel. 2013) By studying consumption patterns, large corporations have been able to predict patterns of sale and consumption in future markets, mitigating potential loss and maximising profit. This analysis “may lead to more confident decision making and better decisions can mean greater operational efficiencies, cost reductions and reduced risk.” (Thomas 2014) Consequently the value of big data and the difficulty that often comes with obtaining it has rendered it “a currency across industry.” (Woodford 2014) Brokers who have access to big data and the tools to analyse and interpret it can buy it from one organisation, add a mark up and sell it at a profit.”

One company that has successfully utilised the applications of big data is Walmart. As it might be expected from one of the world’s largest retailers, their system processes multi Terabytes of new and historical data on a daily basis, covering millions of products and 100s of millions users from internal and external sources. (Anon 2014) Walmart is using their access to “public data from the web, social data and proprietary data such as customer purchasing information,” (Anon 2014) to interact with their customers and friends of their customers and inform them of available products. Such is the accuracy of their analysis that they are able to refer their customers to exact products they have mentioned online, often including a discount or other incentive to buy these products.

However, with this kind of access and research comes ethical questions. Although many see that because this data is on a public domain it can be accessed without ethical dilemma and is no more than a form of textual analysis. (Woodford 2014) However, big data analysis also “raises a bunch of ethical issues related to privacy, confidentiality, transparency and identity.” (Richards & King 2014)  Take for example the research Walmart is doing. Are they crossing boundaries of privacy by targeting people specifically based on what they may have shared among friends on the internet? It’s a developing and grey area in ethics and although there’s no clear answer it seems to me that there may be some ethical implications and an exploitative angle with their methods.

Big data analysis is undeniably an indispensible tool for streamlining a business and maximising profit, however, to me there seems to be ethical issues involved with ownership, access and if using this information is exploiting the public.

Reference List

1. Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc. (Available on CMD)

2. Woodford, D. 2014. "KCB206 Internet, Self & Beyond." Week 9 Lecture Notes. Accessed 8 May 2015. https://lecturecapture.qut.edu.au/ess/echo/presentation/11dcf106-e7c9-44eb-a753-74dfff689043?ec=true

3. King, J & Richards N. 2014. "What's Up With Big Data Ethics" Accessed 8 May http://www.forbes.com/sites/oreillymedia/2014/03/28/whats-up-with-big-data-ethics/ 

4. Thomas H. 2014. "What Is Big Data" Accessed 8 May 2014 http://www.sas.com/en_us/insights/big-data/what-is-big-data.html 

5. Anon. 2014. "Walmart Making Big Data Part of Its DNA" Accessed 8 May 2014 http://www.bigdata-startups.com/BigData-startup/walmart-making-big-data-part-dna/

7. 

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New Media, Big Data and Telemetrics (guest lecture by Darryl Woodford)

Big Data, Crime, and Free Will

“Big data” is large data sets of recorded facts and figures. Almost everything today is encoded as data (Siegel 2013). Big data is also relates to the “tools, processes and procedures” that allow an organisation to “create, manipulate and manage” massive amounts of collections of data (data sets) and storage facilities (Kusnetzky 2010). Some of the challenges involve are trying to make sense of big data and the huge amount of data that’s created every day, as well as processing and organising all the information and data that’s out there into knowledge and wisdom, and trying to actually utilise it for a purpose (What is Big Data? 2012).

See a video explaining Big Data here.

By analysing big data we’re now able to find correlations that “spot business trends, determine the quality of research, prevent diseases, link legal citation, combat crime, and determine real-time roadway traffic conditions” (Data, data everywhere 2010). Computers are now able to automatically develop new knowledge and capabilities by “feeding on” data (Siegel 2013). Data creates insights into things and insights then help machines develop predictive capabilities (Siegel 2013). Big data analysis can be used to predict a vast variety of things, such as stock prices, election outcomes, consumption behaviour, pregnancy patterns, student essay grades, school attendance, as well as the accidents that people have, hospital admissions, risk of death in surgery, as well as even predictions on murder for at risk people and the list goes on (Siegel 2013).

Big data analysis has obvious advantages in its benefits, but it isn’t without its dark side and its downfalls. For example with its use to prevent crime.

See a video on how Big Data and analytics technology has helped a police force reduce crime here.

Predictive analysis and its use in crime can only be helpful to a certain extent. We’ll never be able to predict with 100% certainty that someone’s going to commit a certain crime. But there are people who say that the government will eventually implement a program that intervenes and tries to “help” someone after they’re predicted to have a 90% chance they’ll commit a crime in the next year (Fighting [the Propensity for Crime] with Big Data 2014).  

Minority Report depicts a futuristic world where a police unit is able to prevent crimes before they happen and place people under arrest before they actually break the law. 

The problem about this is that big data only measures whether you have a propensity to commit a crime. It measures what you’re likely to do before you’ve done it. That’s something the criminal system has never had to deal with before – usually you commit a crime and you get punished. But if it’s found that someone is likely to, for example, shoplift in the next 12 months, they won’t be put in jail but what will happen is that someone will be sent around to intervene and to try and help and prevent this from happening (Fighting [the Propensity for Crime] with Big Data 2014). That could be done by trying to get you an afterschool job or talking to your parents and seeing if there’s a problem in your home life. But what will also happen because of that is that you’ll now be pigeonholed as a criminal, as someone who is going to shoplift, as someone who’s affiliated with crime. This in turn may affect the person’s mindset and may even end up leading to criminal behaviour (Fighting [the Propensity for Crime] with Big Data 2014).  

I believe, and so does Kenneth Cukier (a Data Editor for The Economist), that the idea of “human volition”, human freewill is what’s important and what needs to be emphasised and valued beyond the prediction of a person’s propensity for crime.

  References:

Data, data everywhere (2010), 25 February, [Online], Available: http://www.economist.com/node/15557443 [2014].

Fighting [the Propensity for Crime] with Big Data 2014, video recording, United States. Directed by Jonathan Fowler, Elizabeth Rodd, and Dillon Fitton

Kusnetzky, D. (2010) What is "Big Data?", 16 February, [Online], Available: http://www.zdnet.com/blog/virtualization/what-is-big-data/1708 [2014].

Siegel, E. (2013) 'Introduction: The Prediction Effect', in Siegel, E. Predictive Analytics: the power to predict who will click, buy, lie or die, Hoboken, NJ : Wiley.

What is Big Data? 2014, video recording, United States.