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ISSUE 8

HOW ANALYTICS ARE BEING USED IN HR

CLOUD OR ON PREMISE

ADS TO MAKE YOU BEAUTIFUL How are Yahoo! using ads to make their sites beautiful?

Which is best for a company looking to implement a new data strategy?

BIG DATA

UNLOCKING BIG DATA FROM PRIVACY MARKETINGCONCERNS

How are companies using Big Data to make their Marketing great?


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Editor’s Letter

Letter From The Editor Welcome to Issue 7 of Big ful as well as profitable. Data Innovation. Twitter has long been the One of the aspects of Big Data launching pad for music acts that has held most resonance and a new record label, 300, with companies has been its are using big data to look for potential in marketing. Com- new acts on this platform, panies maintaining a knowl- Roisin O’Flaherty reports on edge of their customer base is this new form of finding talent. one of the most important as- How are industries personpects of any data programme, alizing your experiences and this is likely to ring true for the what data are they using? foreseeable future. Daniel Miller takes a look. This is why this issue is themed around how companies are using Big Data in their marketing and some of the discussions that have surfaced from this.

Chris Towers talks us through how knowledge is power in marketing and how Big Data has given marketers more power than ever.

Away from marketing we also have Damien Saunder (formerly Demaj) showing us how he is using an innovative visualisation technique to show player movements in tennis.

Hadoop has long been the foundation on which Big Data initiatives are built, we look at how a cloud based service compares to a bare-metal We look at a recent report option after a recent Accenthat suggests that marketers ture report. are not doing enough in their If you are interested in conBig Data efforts. Heather tributing to the magazine or if James investigates and gives you have any feedback please us her take on why this is the get in touch. case and what can be done to improve the situation. As Yahoo! has found it’s feet again, Simon Barton looks at how Eric Bax from the company is using advertising to make their sites more beauti-

George Hill Managing Editor

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Managing Editor George Hill Assistant Editors Simon Barton President Josie King Art Director Gavin Bailey Advertising Hannah Sturgess

hsturgess@theiegroup.com

Contributors Chris Towers Simon Barton Heather James Daniel Miller Roisin O’Flaherty Damien Saunder (Formerly Demaj) General Enquiries ghill@theiegroup.com


Contents

Contents

4 11 15 18 21 24 BD London half.pdf

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11/02/2014

Damien Saunder (formerly Demaj) shows us his unique way of visualizing player movement data in Tennis We look at Hadoop-as-a-Service versus bare-metal offerings and the recent Accenture report on them Knowledge is power in marketing, Chris Towers looks at how Big Data is giving marketers power Are marketers doing enough with Big Data? Heather James examines How are Yahoo! making their sites beautiful with advertising and Big Data? Simon Barton investigates Roisin O’Flaherty looks at how a new record label are using analysis of Twitter Data to find new acts

16:13

Big Data Innovation Summit May

14 & 15 London, 2014 SPEAKERS INCLUDE:

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Movement Visualization

Presenting a Diorama of Player Movement in Sport Damien Saunder (Formerly Demaj) Geospatial Designer


Movement Visualization

Visualizing and understanding player movement in sport has enormous advantages in relation to an athlete’s match day performance, training and recovery. Automated player tracking in recent years has become a must-have tool for sport scientists, coaches, and analysts. The spread of player tracking now spans a multitude of sports from the Australian Football League (AFL) to the NBA. From the EPL to the ATP. Sport scientists and coaches are monitoring a player’s every move, both on and off the field. However, preparing easy-to-understand visualizations of space-time data, like player tracking, that support analysis and decision-making provides an ongoing challenge to data scientists and cartographers alike [1]. In this article I present a Diorama of Player Movement using a Space Time Cube. The Space Time Cube is a 3D visualization method introduced by Swedish geographer Torsten Hägerstrand in the 1970’s. Space Time Cube visualizations present users with the full spatio-temporal data set in a single, comprehensive view [1]. By contrast, traditional 2D representations of spatio-temporal information require multiple maps, animations, or time sliders to display how the data changes over time. A Space Time Cube pulls the temporal component of the data apart, stretching it along the

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vertical axis of the cube, which enables the users to clearly see changes in the data over time. This unique 3D visualization helps better understand the interaction of the spatial and temporal components of player tracking data. In this article I will demonstrate some of the advantages of Space Time Cubes for visualizing and understanding the DNA of player movement in sport. Hawk-Eye Player Tracking Data Hawk-Eye began tracking player movement in tennis in 2011. The player tracking system utilizes its core ball tracking technology (Figure 1). Recently I was granted access to official HawkEye player tracking data from the Roger Federer v Paul-Henri Mathieu match at the Swiss Indoors in Basel, 2012, which Federer won 7-5, 6-4, in 1hr 26min.

Figure 1. Hawk-Eye uses eight cameras situated around the court to track the path of each player in real-time. Picture: Sam Rosewarne. Source: Mercury


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Movement Visualization

The Hawk-Eye Data format Hawk-Eye collects its player tracking data at 0.05 sec intervals. The data is collected at the point level of a match. In the Federer v Mathieu match there were 198 points played resulting in 53,440 data points for the match. Each point in the match is stored in the database as a single xml file. The variables collected by the system are x,y,z co-ordinates plus time (Figure 2). The time variable resets itself at the start of each point. The file name of each xml file represents the set, game and point number, and whether the point is a first or second serve. Visualizing Player Movement in Tennis Player movement in tennis is typically summarized by distance covered, direction, and speed of movement of players. For the purpose of this example, I created a player velocity map using a static 2D representation. In order to create the player velocity map I classified the data into four categories. Figure 4 is a simple way of presenting relative speed using a green to red color scheme for each point in the dataset. However the representation makes it difficult to see how the path of the player and their velocity is changing over time. We also only see a portion of the data at any one

Figure 2. An extract from the player tracking data collected by Hawk-Eye.

time. In this case the most recent player movement ‘paths’ are drawn on top of the earlier ‘paths’, making it difficult to identify the distribution and frequency of player velocity. In order to improve the representation we might consider animating the data, or, create a series of small static maps which each present a time period from the match. We may also consider introducing an interactive element to the map like a time slider. Each of these methods have the potential to enable us to see how the data is changing over time, and therefore eliminate the issue of overlapping data. Whatever approach is taken the fundamental issue of viewing the data in a two-dimensional plane remains. Animation, small multiples or time sliders all allow us ways to slice through the data and see different moments but none give us clarity when trying to look at the match as a whole.

Introducing a Diorama of Player Movement for Tennis Perhaps a more suitable, but rarely seen method for visualizing spatio-temporal data in sport is to use a Space Time Cube. By building a Space Time Cube we are able to disaggregate the overlapping player movement lines by using the y axis of the cube to represent time. The min y value represents the start of the match and the max y value the end of the match. Along the base of the cube represents the x, y movement of the players - the planar court (Figure 5). The Diorama of Player Movement enables us to see the spread and frequency of the four player velocity categories more clearly. By using the third-dimension we can be more confident about making judgments about movement patterns in the match because we have a full view of the dataset.


Movement Visualization

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form means the visualizations can then be shared amongst players, and other stakeholders (Figure 6).

A drawback of the Space-Time cube has been the presentation of a single view as a static image (as in Figure 5). The inherent problems of trying to understand the data on a perspective view mean that in some respects it creates a mass of data points that are difficult to visually disentangle, much like a 2D static map. Therefore orientation, navigation and human interaction of the cube are cenFigure 3. The player velocity classi- tral to its appeal and usability. fication used in Figure 4 – walking, The rapid advancement of web jogging, running and sprinting. technology, in particular WebGL means these complex visualizations can be rendered directly in the browser to create an interactive version. This gives analysts and sport scientists an opportunity to explore the scene by panning, zooming and titling from any viewpoint and overcomes the drawbacks of a static view. Using the web as a plat-

Figure 4. Creating a static 2D map of player velocity. White = walking, green = jogging, orange = running, and red = sprinting.

The 360° view of the scene means we can also quickly compare patterns between both players at any angle. For example, from behind each player we can visualize over time the extent of his or her lateral movement throughout the match (Figure 7). There are extended periods of time in the 3rd and 7th game of the 2nd set where Federer’s movement is clearly trending to the left side of center, most likely as a result of Mathieu targeting his backhand during these games. Through games 4 to 9 in the 1st set Mathieu’s movement was often short in both time and length, which perhaps implies the points in each game were short to due to successful serving, or unsuccessful return of serves. We are also able to analyze who is attacking and playing on the baseline. We can very quickly see the player position change over time during the match (Figure 8). The Diorama of Player Movement diagram shows us that Mathieu spent more time playing inside the baseline than Federer did. Up until the 7th game, Federer was playing mostly from behind the baseline,


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Movement Visualization

ables us to see the spread and frequency of player movement more clearly. Using the third-dimension of the cube to disaggregate the data means we can be more confident about making judgments about movement patterns because of the full view of the dataset. The web is providing teams, coaches, and analyst with a powerful platform to view, share and collaborate their Figure 5. The Diorama of projects. Browsers are fast bePlayer Movement. A Space Time Cube visualization. coming very capable of rendering large quantities of big data, rarely pressing forward for any meaning that representations of extended periods of time. Nei- data being collected from Optither player appear to be playing cal sensors and GPS, like player on average deeper than usual (> tracking can be viewed and in3m) behind the baseline. teracted with on mass. From the side perspective we can also see the frequency of forward movement over time by each player, whether it is in attack or defense. In the 1st set Federer moved deep inside the court only 4 times in the first 11 games, where Mathieu was much more active in this area of movement, moving forward 11 times. This trend was reversed in the second set. Federer was far more active in his forward movement (9 times) compared to Mathieu (3 times).

There is little value in focusing on singular variables in sport. The interaction with other variables is where the real value lies. For example, in tennis it is not about how fast you move, it is how fast

Conclusion The Diorama of Player Movement presents a unique way of visualizing player movement in a three-dimensional space. The single, comprehensive view offered by a Space Time Cube en-

Figure 6 The interactive Diorama of Player Movement application. The three-dimensional scene offers an unrivalled viewing experience of player movement data.


Movement Visualization

Figure 7. A side-by-side comparison of each player’s lateral movement. The 1m distance markers can be used as a reference for court position. The line surrounding the cube roughly half way up represents the end of set 1, start of set 2.

you move relative to the ball [3]. The Space Time Cube has the potential to manage and display this second tier of contextual information. Thus opening up opportunities of linking player movement to other variables like ball speed, direction and success of shot making. Maps have been used to stimulate visual thinking about geospatial patterns, relationships and trends for centuries [4].Optical sensors, GPS, and other wearable technologies are collecting never seen before geo data. Different datasets force a different view, different questions force a different representation, and different audiences force a different approach. It is not uncommon to see multiple representations of the same theme in order

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Figure 8. The baseline walls allow us to monitor the players position change over time in relation to the baseline. The (Federer - right, Mathieu - left).

to fully understand the pattern, relationship or trend. However the Space Time Cube is a powerful stand-alone visualization, that reigns supreme for its ability to convey complex spatio-temporal patterns.

Damien Saunder (formerly Demaj) is a Geospatial Designer at Esri where he designs and builds online interactive maps. He is continually rethinking spatial analytics for tennis via GameSetMap.com.

References

library/papers_2005/msc/gfm/xia.pdf

[1] Per Ola Kristensson et el, “An Evaluation of Space Time Cube Representation of Spatio Temporal Patterns,” IEEE Trans. Visualization andComputer Graphics, vol. 15, no. 4, pp. 696-702, July/Aug. 2009.

[3] Hassan, F “Movement: An Essential to Shot Making”, http://www.itftennis.com/shared/medialibrary/ pdf/original/IO_8551_original.PDF

[2] Li, X, “New Methods of visualization of multivariable spatio-temporal data”, 2005, http://www.itc.nl/

[4] Kraak, M. “The Space Time Cube Revisited from a Geovisualization Perspective,” Proc. 21st Int’l Cartographic Conf., pp. 1988-1996, 2003.


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BIG DATA INNOVATION S U M M I T APRIL 9 & 10 2014 SANTA CLARA SPEAKERS INCLUDE:

For more information contact Sean Foreman +1 (415) 692 5514 sforeman@theiegroup.com theinnovationenterprise.com/summits

#datawest14


Hadoop

Hadoop-as-a-Service Versus Bare-Metal George Hill Managing Editor

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Hadoop

As Big Data has grown over the last few years one trend that we have seen is the growth in cloud based Hadoop platforms.

The Hadoop Deployment Comparison Study compares the Amazon EMR Hadoop-asa-Service platform against a bare-metal platform made The increase in this platform up of a variety of standard has been in response to the hardware. often expensive ‘bare-metal’ The two platforms were asoptions and the flexibility that sessed on their total cost of this kind of platform offers ownership, which takes into companies who are looking at consideration a variety of big data platforms with the metrics to assess the effectiveness of each option. The future in mind. It has been a widely held be- study used Accenture’s Data lief that operating Hadoop in Platform Benchmark, to asthese kinds of virtual condi- sess the Total Cost of Owntions means that it runs slowly ership for both solutions. This and thus hinders the perfor- method of analysis has thrown mance of the analysis. There up a more useful conclusion have also been few studies than if it had been looking at that have compared the two, more limited metrics. outside of companies with a The outcome of the study was Hadoop-as-a-Service clear bias towards one side or that was a better investment than the other. Accenture have conducted a the bare-metal option. study making the comparison between these two implementation options which has debunked many of the myths surrounding the two systems.

aspects of the platform. As we are seeing an increase in the amount of data both produced and collected, this kind of scaling and management of costs is important given the unpredictable nature of data moving forward. Another aspect that the study was quick to debunk was the belief that Hadoop ran considerably slower in virtual environments than in a bare-metal capacity. The reasoning behind this is that although the hardware does have the capacity to run faster, in reality with the multiple uses that bare-metal technologies needs to fulfil, operating across several different tasks, this is often not the case. This is because the pay-as-yougo nature of Hadoop-as-aService, means that nodes are used and optimised for a single task rather than being used to perform multiple tasks.

One of the key reasons behind this was the flexibility in pricing. Given the variability of data needs, this was seen as one of the most important One of the aspects of Hadoop-


Hadoop

as-a-Service platforms that the study was also keen to point out was that with the need for optimisation to create the best results. This process is simpler in the bare-metal systems, however, one of the aspects of the cloud based systems that they are quick to point out, is that there are many opportunities to use automated tuning services that will make the process less time consuming and much simpler. Overall, this study has shown that the use of Hadoop-as-a-Service is not only viable, but a good choice for many companies. With the speed issues that many have believed, shown to be false and the scalability options that this plaform provides, the future of Hadoop may well be in the cloud.

BD Toronto half edited.pdf

1

11/02/2014

16:46

Big Data Innovation Summit

June

4&5

Toronto 2014

SPEAKERS INCLUDE:

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14

Marketing

Big Data In Marketing


Knowledge is Power

Knowledge is Power in Marketing Chris Towers Big Data Leader

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Knowledge is Power

creating the content that their audience wants based on their viewing preferences and their viewing habits. This has given them a more agile approach to programme making and a more Prior to the internet this was personalised approach to mardone through surveys and focus keting their programmes to indigroups with a careful analysis of viduals. this limited pool of information Understanding the viewing habfollowing shortly after. A few its of their viewers means that weeks later you would know the when new programmes that results and could base market- are of interest are added to the platform, notifications can be ing campaigns from them. Today we are seeing a different sent out to those with the most interset of rules. est. This is Gone are the days when a focus no longer group would be made up of a t a k i n g few people who would come to t h e an office and discuss whether or not they liked something. Instead every person who visits a website or who see’s an advert online, is now the focus group, giving insights into the effective- data ness of a product or campaign. f r o m What makes the best marketer? It isn’t the use of the newest technologies or making a video go viral, it is the ability to know their audience better than their audience knows themselves.

This connectedness has also led marketing campaigns to be judged not in weeks, but in minutes and hours. If something doesn’t take hold on Twitter within a few hours, the chances of it succeeding after this are slim.

the limited number of people in the US who represent thousands, but instead taking the data from the individuals who make up their audience.

Netflix is a prime example of this,

a big bet for Netflix," Joris Evers,

Take the recent Netflix release of ‘House of Cards’. This was a move We are seeing this not only in away from their normal model the ways that banner ads are of licensing TV shows and films shown, but actually creating from other companies and the products that the audience instead saw them creating wants to see then marketing it to the show themselves. them at the right time. "House of Cards was obviously


Knowledge is Power

Netflix Spokesman said. "But it was a calculated bet because we knew Netflix members like political dramas, that they like serialized dramas. That they are fans of Kevin Spacey, that they like David Fincher."

become major contenders in the entertainment industry, no longer just a streaming site, but a producer of content too.

It is a prime example of how the data you hold on your customers can have a knock on effect This showed that people liked beyond showing which email certain aspects of the show and they open that these aspects were likely to and what make people want to watch the b a n n e r show in the first place. Another they click on. aspect that they saw was that It allows for when people wanted to watch a deep dive inshow, it would be in a bingeing sights that can fashion, not watching one epi- go beyond optisode then waiting for a week be- mising advertising fore watching the next. Instead potential and make it would be one episode after their way into creating more marketable another. One of the most impressive ex- products. amples of this was the user who “finished [House of Cards] with just three minutes longer than there is content. So basically, three total minutes of break in roughly 13 hours.�. A 13 hour bingewatch is uncommon, but is testament to the general habits of users. They want to watch multiple episodes in one sitting. This kind of agile use of data has allowed Netflix to

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Doing Enough?

Are Marketers Doing Enough with Big Data? Heather James Big Data Leader


Doing Enough?

the respondents believed this to be the case is both testament to the skills gap and the almost inconceivable amount of data that these companies can choose from. 54% believed that the amount of data created management issues and 49% cited a difficulty in recruiting people The availability of the data has with the correct qualifications also increased and we are see- and skills. ing existential growth in the plat- These two things however are forms where data can be drawn not unconnected and show both from. Offer a competition on the issue going forward and the Facebook in exchange for a like, potential solution at the mouse sentiment analysis on Twit- ment. ter or even search professional Marketers are not data scientists interests on Linkedin and sud- and visa versa, it will always be denly you have more informa- difficult for those who are ‘uninition on your customers than you tiated’ in the data world to get ever could through focus groups. their heads around the amount Big Data has been a buzz word amongst marketers for the past two years. The idea that through the actions of customers, you can find out more about them and therefore target them more effectively is highly impressive and something that every marketer in the world wants.

of data available. The truly skilled in big data realise that the huge amount of data is not in fact the issue, but the breadth of it. The ability to create small data from big data is the basic job of any data scientist. Nobody would be able to notice patterns in a database with petabytes of inforHowever, there are issue’s with mation, instead it is the ability of this that many are currently data scientists to drill down that finding. And this in the manage- gives the true results. This skills gap is something that ment and analysis of the data. Following a survey by The World we have been discussing since Federation of Advertisers, it the first issue of the magazine transpires that despite 93% of and even though this skill gap respondents see big data as ‘vi- has shrunk, in reality it is still tal’ in the next three years, 74% very much there and it is holding were currently unprepared to many back from achieving their take full advantage of the data business objectives. So perhaps this question should be less boom. about recruitment of staff with That close to three quarters of the correct skills and instead The analysis of this data and creating smaller and smaller silos means more targeting and placing more important products in-front of key people at the right time. Therefore this use of data should increase sales whilst also allowing for in depth ROI analysis.

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Doing Enough?

taking staff who are already employed and converting their skills to allow them to understand big data. Another aspect of the survey that is interesting is that 70% of those asked believe that an improved understanding of ROI was the most important aspect of a big data programme. Alongside this, 64% say a deeper understanding of customers and 47% say precision marketing are important factors. These three figures however are not mutually exclusive ideas and in the landscape of big data should perhaps be viewed as three combined factors in the same process. Precision targeting occurs due to an increased understanding of customers which in turn provides an improved ROI. The ROI and analytics associated with it allows for an increased understanding of customers and most importantly their reaction to different marketing activities which then provides deeper insights and therefore more accurate targeting. The process here is a continuous circle that is self perpetuating and ever expanding. And there-in lies the issue. Those marketers who adopt this kind of process as early as possible will undoubtedly reap the benefits earlier and begin the process earlier. Therefore even though 74% believe that they have not got the skills or cannot deal with the amount of data

available, the longer they go without, the further ahead the other 26% will be. Without quick and timely investment into this area there is every chance that the gap between those with and those without will become too large to bridge.


Yahoo!

Yahoo! and Advertising: How Big Data is Playing a Big Part Simon Barton Assistant Editor

Is Your Advertising Beautiful?

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Yahoo!

When we think of the spread of the internet over the past twenty years, the expansion has been phenomenal. The reasons have been given as additional connectivity, increased knowledge and the ability to create effective networks. In reality the reason that the internet has expanded to the size that it has is because it is very easy to monetise. This is not just done through e-retailers and online shoppers but also through the use of advertising on websites. It has changed the face of publishing forever, meaning that we are seeing newspapers becoming exclusively online and others that are creating more revenue through online advertising than in print. This kind of shift has allowed companies to create marketplaces for selling this web real-estate. One of the earliest users of this kind of platform was Yahoo!, a web giant that is finding it’s feet again after

a few rocky years. I was in the audience when Eric Bax, Director, Marketplace Design at the company spoke at the Big Data for Marketing Summit in Miami. In July of 2013 Yahoo! were looking on the up having had more US visitors to their sites than Google sites (according to Comscore). This meant that they must have been doing something right and I was interested to see what Eric had been doing to help with this. One of the most interesting aspects of site design mentioned by Eric was that advertising holds a key role in how sites are perceived. Ugly ads are going to create ugly sites that people are less likely to come back to. He gave the analogy of a barn by the side of the road. If it’s just a regular barn people will largely ignore it and it will be forgotten, paint an old Coca Cola ad on the side and the value and beauty of the barn


Yahoo!

iour whilst on the site. In Eric’s words ‘We don’t have over 40 servers for nothing’. This powerful analysis means that YaThe idea behind this is that hoo! can place ads that peobeautiful advertising can en- ple want to see, meaning their tice the audience to a site and sites are more pleasant for this is something that Eric is a users and ads are more likely big advocate of. He realises to get clicks from users. that in the short term it can This is a win-win for both the be more profitable to place advertisers and the website uglier ads that provide more owners, all thanks to the use of in PPC, but this may damage big data in the analysis of the the amount of visitors to the ads. It also transcends sites in the long term. But what does this have to do the issues with both with big data? forms of There are two different ways ad submisto submit advertising. The sion, creatblack box method, which es- ing enough sentially allows the site to insight to place the ad where it is like- m a x i m i s e ly to get the most clicks, and ROI whilst the more customised version not restrictallowing advertisers to tar- ing discovget based on categories and ery through demographics. One allows for tight catemore information to the ad- gorisation. vertisers (customised) whilst kind the other allows Yahoo! to use This it’s knowledge of the audi- of use of ence but allows less feedback big data is s o m et h i n g (black box). that goes What Big Data allows is to beyond the show that X thousand adverts t r a d i t i o n were shown to somebody and al thoughts how they reacted to these of data sciadverts. This goes beyond entists. The simple click throughs and in- use of anvestigates their return visits, alytics and frequency of visits and behav- data to dewill increase. The theory behind this is seldomly seen on the internet, but it is something that can be achieved.

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cipher not only ROI for advertisers, but to also create websites that are likely to increase visits due to their ads is something else completely. This work shows that with lateral thinking and dynamic approaches, big data can truly have beneficial impacts for both those advertising and those who need advertising to monetise.

Dutourdumonde Photography / Shutterstock.com


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Sentiment in Music

Using Sentiment Analysis to Find The Next Superstar Roisin O’Flaherty Big Data Leader


Sentiment in Music

When you think of music executives, you think of people sat in rooms listening to new CD’s or MP3’s that have been sent across to see if the artist singing through the speakers to them is good enough to be signed. However, it is not always the case that brilliant music will relate to the success of an artist. For instance, Sixto Rodriguez who was wildly successful in South Africa but was unknown in the US despite many music executives initially believing him to be the next big thing. The brand appeal of the artist is also a major factor and this can be seen in the use of TV talent shows, who utilise their large audiences and monopoly over public perception to sell the image of the artist as much as any talent. However, one of the aspects of this approach is a short term-ism for the majority of acts who come through the system. But when you look at the biggest selling music brands in the world at the moment, Justin Bieber and One Direction, one came from nowhere (Bieber) and the other were losers in a talent contest (One Direction). So what caused them to become worldwide phenomenons? Both were loved on social media. Just as Bieber’s initial use of YouTube and the cultivation of a fanbase on Twitter is what has made him into a teenage icon,

One Direction’s fanbase did the same thing after they had lost in a talent contest. Now big data has come into play to find the next big thing. Lyor Cohen is a former Warner Bros executive who has created the 300 music label, which is using big data and analytics to find the next big things in music through the use

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Sentiment in Music

of sentiment analysis and popularity on social media. An agreement announced at the MIDEM music business conference, allows 300 to organize Twitter’s music data, including location tags that are not visible by the public. They are also planning on software development around the information that might also be useful to musicians, record companies and publishing companies. This innovative use of data by a music label is also a jump forward for Twitter, who despite now having on the world’s largest collection of user generated data, generated a disappointing earnings report last year. This move by an innovative and well backed company (Google are the primary investor in 300) is the first step in the monetisation of the data, a move that took Facebook from a powerful network to an impressively profitable company a few years previously. This kind of move opens up opportunities for others looking to mine data in both music and other areas on Twitter. It follows similar work conducted in the past where Twitter has granted access to it’s historical data with Tweets being mined back to it’s inception in 2006, but this is the first time that it is being viewed as something truly monetizable.

Katy Perry, who has the largest Twitter Following in the world. 30 of the top 50 most followed accounts represent musicians Image: Jaguar PS / Shutterstock.com


&

Predictive Analytics Innovation Summit 27

Big Data Innovation Summit

March

27 & 28 Hong Kong, 2014 SPEAKERS INCLUDE:

For more information contact Ryan Yuan +852 8199 0121 ryuan@theiegroup.com theinnovationenterprise.com/summits

PREDICTIVE ANALYTICS

BIG DATA


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Do You Have The Spark? If you have a new idea that you want to tell the world, contact us to contribute an article or idea ghill@theiegroup.com

Big Data Innovation, Issue 7  

Big Data Innovation, Issue 7

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