Telematics: Transmitting data over long distances

Telematics. It’s a field you don’t often hear about, but it’s there. And you likely have a touch of it in your life whether you realise it or not. You definitely do if you have a navigation board in your car.

What is telematics? What can it do for you? Is it intrusive to your privacy? What are the major hurdles in the field? Let’s start with what telematics is and how it’s used.

Telematics is…

Big Data. Telemetrics. These terms are daunting not just because they sound like they come from a dystopian sci-fi movie set in the distant future, but also because of what they represent. Philosophically, I feel humbled by the fact that my clicking and typing can be reduced to a few bytes of data and sold for real money to advertisers. I can’t help but be a little concerned with how easy it is for the world to have access to such a vast array of information about me, and so accurate! Yes I do want to see that suggested video, well done Youtube, you know what I do like Pringles, thanks Facebook. Although I could go without the suggested dating sites, yeah I’m single, stop reminding me.

When I use New Media, especially social networking sites, Youtube and such, I don’t feel like I am putting all that much about myself out there. I don’t write posts that often, comment rarely and I am by no means a frivolous liker. It is because of this that I absolutely marvel at some of the advertising I am bombarded with, especially on Facebook. Sometimes I will look at an ad and just try to figure out exactly what it was that I did to see this particular one. Just look at how hard this lady had to try to keep her personal life personal. It’s easier to keep track on Youtube, the “Recommended” videos all make sense and directly relate to what I’ve seen before. Since learning more about big data I have been more conscious of some of the more subtle ways it effects my usage of new media. For example my favourite comedy article and video site Cracked.com will keep me procrastinating for hours with its incredibly accurate prediction of what I’ll want to see next. In fact it now doesn’t even bother to ask if I want to watch the next video, it just gives me a 5 second countdown then on to the next one I go, and if that trend continues to even more extremes I wonder where we could get to in the future… I’m not a big online shopper but the couple of sites I might browse very irregularly even know what I want. I believe it is well within the realms of possibility that the analysis of all of this data will get so accurate that we, the consumer, will be able to put our trust in these programs to go ahead and buy the stuff for us anyway. In the future why bother with the ads, just bypass them and take me straight to the checkout, software will know me so well it can make my wardrobe decisions for me. Maybe even take a peek into my bank account, work out a budget, and get me a surprise gift ever now and then of exactly what I want. It’ll save me tons of time, which I can spend watching even more cats do cute and funny things for way too many hours.

In the not-so-distant future it also occurred to me that with the introduction of Hoyts streaming service (similar to Netflix) to our homes here in Australia we won’t have to think too much about what TV show or movie we want to see next. Undoubtedly it’ll programmed to suggest similar things to what we’ve seen before and with great accuracy. So I can just turn it on sit back and let it run, clicking next every hour or so until I inevitably need snacks, then after my trip to the pantry we begin the cycle again. Wall-E future here we come, all we need is those comfy looking floating chairs… okay, went off on a bit of a tangent there.

These ones.

References:

Siegel, Eric. 2013. Predictive analytics: the power to predict who will click, buy, lie, or die. Hoboken: Wiley.

What was interesting about this weeks topic was the science (or psychology) behind our favourite social media platforms, namely Bruns descriptive breakdown of Twitters layers of communication (Bruns, A. 2013).  We were introduced to Bruns three layers of communication on twitter, these being firstly the Micro level, which I summarised as being the person-to-person close and direct communication, the Meso Level where he described this as the follower-followee networks, and finally the macro level, where hashtags bring peoples common interests together and in a way can create communities of similar minded people.  

(Image care of Bruns, A. 2013 reading, reference below)

Darryl Woodford in his lecture discussed the Twittersphere map (see below) created by QUT and based on user generated data retrieved from Twitter usage (Woodford,D. 2014).  This map could not of been possible if not for the trail of data left behind from key aspects of the communication layers discussed in the previous paragraph.  By analysing the data retrieved from Twitter ‘following’ patterns, #hashtag topics (topical and non-topical) and @replies (Bruns, A. 2013) computer programming allowed the developers of the Twittersphere (Woodford, D. 2014) to map topic trends and community discussions, this being information that will be very helpful for many different parties.  This is where Telemetrics (Woodford, D. 2014) comes into play.  

(Image from http://mappingonlinepublics.net/2012/04/01/many-maps-of-the-australian-twittersphere/)

For and example close to my field of observation I will focus on how media companies are using this data retrieved from social media users and tailoring their marketing plans around it. What many television shows are doing now is incorporating social media into their programming, using techniques that have both a direct effect on the outcome of the show and a more non-direct observational effect, both still giving the user a sense of  connectivity with the program.  As Stephen Harrington mentions “undoubtedly, the single most important aspect of 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 (Harrington, S. 2013).  This therefore means that by incorporating Twitter handles and hashtags throughout a broadcast, they are no only increasing a consumers sense of relationship with a show, they are also increasing advertising due to use of the shows handles and hashtags across social media, but also and most importantly through these hashtags and handles are increasing there data being provided by consumers.  As Eric Siegel states in his writings about predictions “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 (Siegel, E. 2013).  This meaning that the data is there but its what is done with the analysis of this data that is where the real value lies.  

References

Bruns, A. et al, (2013). Chapter 2 : Structural Layers of Communication on Twitter pp. 15-28; Chapter 17 : The Perils and Pleasures of Tweeting with Fans pp. 221-236; Chapter 18 : Tweeting About the Telly : Live TV, Audiences, and Social Media pp. 237-248. In Weller, Katrin et al (eds),Twitter and society, (p.-). New York: Peter Lang.

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. 

Week 9 of Internet, Self, and Beyond focussed on big data and telemetrics. As has been discussed over the semester, new media has provided a multitude of opportunities to share, communicate, and connect with others.

According to Siegel (2013, 3 – 5), “data embodies a priceless collection of experience of which to learn”, and that each digital experience “can be encoded as data and warehoused”. New media technology has a newfound capacity for intelligence, and it is giving us the ability to understand ‘big data’, visualise and make predictive notions based on analysis of this data. From this, we can learn more about how consumers and society operate. 

The Dark Side of Data - posted by The Economist

It is large corporations and companies that have access to this data, mainly because they have both the financial and technological resources to store this information. I must say that the idea of ‘big data’ concerns me quite greatly. It seems unethical for large corporate entities to have access to private user information, which subsequently gives them the ability to manipulate this data for their own financial gain. As the video The Dark Side of Data posted by The Economist explained, over 1,800,000,000 people have access to your information on Facebook – and that’s under default privacy settings (2012). In addition to this, approximately 72% of privacy policies allow third parties to track our Internet activity and online behaviours (The Economist, 2012).

Siegel noted, big data “uncovers what drives people and the actions they take – what makes us tick and how the world works…with the new knowledge gained, prediction is possible” (2013, 4). An example of utilising personal data for predictive analytics is online pop up and sidebar advertisements. These ads are selected by online advertisers to reflect previous sites you have personally visited, making you more inclined to click on their ad. Large corporations such as Google, Facebook or Twitter collect a significant amount of users’ personal data, and then sell this to online advertisers.

  An email I received from Coles recently about my 'top in-store specials' - data that was received and analysed via my Flybuys account (which is creepy...)

As Darryl Woodford (2014) mentioned in the lecture, big data is frequently used as business intelligence to help with target advertising towards individual consumers – it is “a currency across industry”. This is achieved through analysing the data regarding the items or products a customer has purchased previously, commonly referred to as a ‘purchase history’. I have personally experienced this, through Coles Supermarkets. I am often sent emails by the chain informing me which of my ‘favourite’ products were on sale or suggesting special deals I would be interested in. When it first happened, I found it extremely creepy, namely because I don’t have an online account with Coles (for online shopping) and I had never done a survey or something to give them this information. After a while I realised that Coles had retrieved and then analysed this data from my Flybuys account, which records purchases whenever I scan my Flybuys card. Whilst Coles probably intends systems like this to be efficient and helpful for consumers, it is quite worrying knowing they have all this data about my consumer behaviour that I didn’t give them.

REFERENCES:

The Economist. 2012. “The Dark Side of Data” YouTube video, posted June 26. Accessed May 11, 2014. https://www.youtube.com/watch?v=ahZGEusG13A

Siegel, Eric. 2013. “Introduction: The Prediction Effect”. In Predictive Analytics: The power to predict who will click, buy, lie or die, edited by Eric Siegel, 1 – 16. Hoboken: Wiley. Accessed May 11, 2014. https://qutvirtual3.qut.edu.au/qv/olt_material_search_p?p_unit_code=KCB206

  http://londoncalling.co/2014/01/uk-retailer-waitrose-takes-a-different-approach-to-in-store-loyalty-cards-big-data/

Okay, all jokes aside, it is a bit amazing and even scary that big data can be used to create such a staggering response in the real world.

  Think about every app you use, every camera you walk or drive past, every tweet you send, every status update you post, every switch you flick on or off, every button you press. The data that every one of these interactions produces quantifies you; tells the story of where you’ve been, what you’ve been watching, how you work out, what you buy and much more, and this happens constantly. Every day this data is propelled into the world and into the hands of researchers, companies, governments and for some of it any interested member of the public.

So what happens with this data?

Woodford (2014) depicts a topical and insightful example of how this data reflects human behavior. Via Twitter, instead of just observing the number of people watching a television show, they go so far as to measure an ‘excitement factor’, which gauges audience engagement with a show throughout its season. They use big data to elaborate on the measurements of human behavior. I’m sure you are all, as I am, just thinking about the innumerable applications for this sort of research.  

In the reading by Harrington (2013) he brought up the idea of a ‘virtual lounge-room’ where our interaction with social media, while consuming media through the television, mimics that of our interaction with other people in the lounge-room when we are watching a show. This behavior suggests that, contrary to popular belief, watching tv is often a social practice. 

These insights that big data and telemetrics afford are changing the understanding of human behavior and mean that researchers are on the precipice of new breakthroughs across so many fields.  

With the use of big data comes ethical dilemma though. Once again we are faced with an evolution in technology that ethical guidelines and legislation cannot keep pace with.  A fine balance between ensuring privacy and maintaining freedom and democracy must be struck; though it is difficult to decide where to draw the line between protection and oligarchy.

When power is held by the few, issues surrounding their prerogatives for big data arise. As corporations and governments utilize the data that we make, it begs the question: do the benefits for humanity they discover outweigh the breach of privacy and opportunistic behavior they may foster?

However they are used and to whatever value, big data and telemetrics are set to boom in the next few years and we are ever-fuelling them. It will be interesting to see, both the discoveries that they afford, and how regulation tries to salvage the seemingly distant concept of privacy. 

    References:

 Harrington, Stephen. 2013. “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.

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.

Source: http://healthworkscollective.com/matthieublit/60636/big-data-overview

I don’t think it’s an exaggeration when I say everyone uses the internet to find information. Whether you’re a student researching for assignments or a 70 year old housewife hunting for recipes, we all search online. But at the same time, the internet is finding out information about you. It is recording your online activity and creating an in-depth profile about you as a person.

  You’re likes, dislikes, online shopping habits and movie preferences, the internet knows it all. And big businesses are using this big data. Siegel (2013, 11) coins this movement as ‘the prediction effect,’ when “value is derived from identifying a group of people who – in aggregate – will tend to behave in a certain way.” This is basic marketing at a complex level, allowing businesses to target their marketing efforts at very specific consumer groups. Computers can predict which consumers are most likely to respond to and engage with direct marketing, AdWords, banner ads and social media marketing (Siegel, 2013). A real-time example of big data usage is product recommendations offered by online shopping websites, which are generated through the analysis of user actions on the website. It is used particularly well on music sites and fashion apparel sites, where genres/preferences are easily established (Senecal and Jacques Nantel 2004; Yang, Liu and Cai 2013).

  There are inherent risks associated with such information gathering which have been previously discussed in terms of privacy and thw blurring of public and private spaces. Is your information being sold? Who is it sold to and what are they using it for? Do you even know it’s being gathered?

Big data can also be used to monitor consumer engagement through social media participation. Harrington analyses the relationship between television and social media through the use of hashtags to establish and publicise TV programs. Social networks are becoming big data factories to both create data and announce data. The Block: Fans vs Favs used social media to “create a more dialogical relationship with viewers, which itself helps to create a sense of 'live-ness', even in a show which may have been recorded many months prior to being screened" (Harrington 2013, 242). The producers furthered this relationship by posting selected viewers tweets throughout the show. One of my personal favourites is television commentary through social media, for example state of origin. I can almost watch the game through Facebook status updates.

  Source: http://www.troll.me/2012/05/22/brace-yourselves/brace-yourselves-state-of-origin-statuses-are-coming/ 

These social media mentions and trends can establish what Woodford, Prowd and Bruns (n.d.) terms an ‘excitement index,’ and can break down viewer’s thoughts towards, and engagement with, the television program in a minute to minute analysis. It can create a quantitative measurement of user opinion and feedback data can be generated in a big way.

  New Media has given rise to big data through extensive data collection. This data can be applied to many industries, including marketing and old media measurement.

    References

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

  Senecal, Sylvain and Jacques Nantel. 2004. “The influence of online product recommendations on consumers’ online choices.” Journal of Retailing 80 (2): 159-169. Accessed May 11, 2014, from http://dx.doi.org.ezp01.library.qut.edu.au/10.1016/j.jretai.2004.04.001

  Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics: t he power to predict who will click, buy, lie, or die, 1-16. Hoboken, NJ: John Wiley and Sons Inc

  Woodford, Darryl, Katie Prowd and Axel Bruns. (n.d.). “Telemetrics: Towards Measuring Social Media Engagement with Television.” Unpublished paper. Queensland University of Technology.

A major goal of television executives is to create content that people talk about so that they continue to watch the program, and the station can earn more through desirable advertising spots.  Memorable moments from the previous night’s episode of Friends, Seinfeld or the Sopranos were gossiped over so that viewers could disseminate and share their affection for the shows and it’s characters in what became called “water-cooler talk”.  

“History shows that new technology rarely result in the displacement of long-standing audience practices, but are typically blended into existing routines and activities instead” (Harrington 2013, 238).

With the rise in popularity of new media content such as the social media platform, Twitter, Harrington’s “long-standing audience practices”, ie water-cooler talk, are blended into the activity of watching a live television program with the phenomenon of live-tweeting, where Twitter users can make live comments and posts referring to what they are watching, that reach potentially millions of people.

HubShout (2013).

But these tweets offer more than just allowing fans to feel more connected.

The promotion from television networks to get audiences to join the conversation and live-tweet during programs is beneficial for the television program and the broadcasting networks that receive this free advertising, but also for market research companies that can analyse the digital data such tweets create and the data stores they contribute to.

Market researchers are now able to able to process information from the internet, and more specifically social media to understand audience behaviours and to identify target markets for products and services (Pérez-Latre, Portilla & Blanco 2011, 68). Similarly, networks can use this data to understand the activities of their viewers during certain programs, and allow them to make alterations that may attract more viewers of similar ilk. Through a process of “machine learning”, computers and analytics are able to use past data to provide a predictive analysis of future activities (Siegel 2013, 4).

Predictive analysis changes the game in that instead of providing forecasts, ie how many people will watch a program, it predicts, and says who will be watching the program (Siegel 2013, 12).

Highfield, Harrington and Bruns (2013,1) describe Twitter as a “technology of fandom” in that “it serves as a backchannel to television and other streaming audiovisual media, enabling users to offer their own running commentary on the universally shared media text of the event as it unfolds live”.

The activities such as water cooler talk that we took part in a decade ago aren’t dead, we’ve just converged them with new technologies, and adapted them to enable us to be a part of the conversation, wherever that may be. As Kevin Spacey said in his Edinburgh Television Festival address, “The water cooler has gone virtual, because the discussion is now online” (2013).

References

Francisco Javier Pérez-Latre, Portilla, I. & Blanco, C. S. 2011. “Social Networks, Media and Audiences: A Literature Review.” Communicacion Y Sociedad 14 (1): 63 - 74.

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.

Highfield, T., Harrington, S. and Bruns, A. 2013. ‘Twitter as a Technology for Audiencing and Fandom: The #Eurovision phenomenon’. Information, Communication & Society, 16 (3), 315-339.

Hubshout. 2013. "Nielsen Includes Twitter in TV Ratings: Impact on Social Media Marketing [VIDEO & INFOGRAPHIC]." Accessed May 1, 2014. http://hubshout.com/?Nielsen-Includes-Twitter-in-TV-Ratings:-Impact-on-Social-Media-Marketing-%5BVIDEO-&-INFOGRAPHIC%5D&AID=1039.

Spacey, Kevin. 2013. “James MacTaggart Memorial Lecture.” YouTube video, posted August 23. Accessed May 1, 2014. https://www.youtube.com/watch?v=P0ukYf_xvgc.