Week 15- Project B Rationale

My iTunes Data Vis
http://public.tableausoftware.com/views/ProjectB/ProjectBDataVisualisation?:embed=yes&:toolbar=yes

I decided that I was interested in seeing how my music taste has changed over the last four years. But to put it together I needed to put my data set together from Itunes. This proved to be quite time consuming as I built a new computer late last year so I had two different music libraries fixing the data in my spreadsheet was half the battle.

When it came to decided how to display this data I thought charts and graphs. So I did a search online for anything that I could use to design my data visualisation. I stumbled on to some software called Tableau which was exactly what I needed. With it I was able to import my data set from excel and create the fields and measure. This also had some really neat features but I had to do a little digging to get them working. Tableau has an online community and a knowledge base so with a few tutorials I was able to start constructing my data visualisation.

In my data visualisation I have created two graphs. One looks at an albums popularity according the time of year and further breaking up the results into the last four years. The bar graph itself is interactive as you can click or hover over the results in the graph to see how many time I listened to songs on that album. The months are colour-coded according to a legend I created. The user can select a particular month and see all the results for that particular month over the last four years. The second graph is a line graph. This looks at the way I listened to the music over time and has a trend line that allows the user to predict future listening habits. This trend line has also allowed me to see which bands are experiencing a long tail effect. Linkin Park is one band that is experiencing this effect. Although I don’t listen to them as often now I continue to go back to them unlike Johnny Cash which seems to have been a fad only lasting a month back in December ’09. I also incorporated a control sheet which has all the artist’s names so that you can select which artist you want to compare. I also attempted to incorporate hyperlinks for artist's myspace pages when you click their name in my bar graph however I wasn't able to work out how to assign each with a different url so at the moment so you can see what I wanted to achieve all names go to the same link. 

I have found related works which shows that society and certainly industry is interested in how people listen to their music. The link below shows how much money the record industry has lost over the last ten years due to the arrival and popularity of the digital format. Modern artists are selling less physical units as we as a society look to the internet and programs like iTunes for our music.

 http://www.tableausoftware.com/blog/record-industry's-lost-decade

This next link looks at an artist album and how many units it sells as a full CD or the individual tracks over a range of online music stores. This I think is an interesting approach of seeing the drop in sales.

http://www.tableausoftware.com/learning/examples/cd-sales-analysis

I followed convention with using graphs to show this data as it is simple and the results can be easily read and interpreted. I could have used the data is several other ways however I wanted it to be easily compared to what the previous two example. Sometime data visualisation can be just so wonderfully graphical and pretty that the actual results and meaning is lost. I wanted to make a point with my data so I kept it simple.

My data visualisation I think is just a little representation to what needs to be achieved on a large scale. I think that these examples are great at showing what units are selling or not selling. What they need to do now is look to the listeners sales don’t make them hits- it’s the way that the consumers listen to them that is. That is what my data visualisation is trying to achieve and that is what makes it different.