In the last of our blog series taking data from sources you wouldn't expect and bringing it to life with data visualizations in Oracle Analytics Cloud, we take a look at some data pertaining to one of the favorite summer pastimes – attending festivals. And what bigger better music festival to go with than the UK's Glastonbury?
As it is 'fallow year' (aka official Glastonbury break year) and the fields are getting some much-needed respite, we decided to look back at previous festivals—spanning 1970 to 2017—to see what can be learnt (and predicted) for the Glastonbury's to come. As usual, we worked with Ismail Syed, Oracle UK Intern, to run our analysis and create data visualizations.
Ticket sales and attendance
First, we considered ticket sales and attendance over time and discovered a few highlights worth sharing. The times have certainly changed since the first event in 1970, when tickets were just £1 GBP, and entry came with free milk!
The highest attendance of any Glastonbury was in 2005, but this started to reduce after the fallow year in 2006, with fewer tickets sold since 2007, perhaps due to increasing health and safety reasons. The number of attendees has stayed at the same level since then. It's also interesting to note the big jump in attendance between 1999 and 2000, likely that's the year the festival awareness rocketed up to become the spectacle we know it to be today.
Who goes to Glastonbury?
Taking data about festival goers in 2016 as a sample, we found over half of people attending are in a relationship or married. Music festivals are clearly not just for singles looking to hook-up! Surprisingly, the overall average age is 39, though the age group most highly represented are 21- to 25-year-olds (18 percent), but very close second were those between 41 and 50 (17.5 percent). Who was it that said life doesn't begin until 40? Perhaps they also went to Glastonbury later at this later age.
Attendees flock to Glastonbury from all over the UK, but the North West and South East of England were the most popular places to travel from. Interestingly, as many people came from overseas to visit the festival as they did from the capital, London.
Overwhelmingly, lager beer was the most popular drink at the festival in 2016, followed by cider and vodka—though neither came especially close to lager. This is probably due to the fact that you can watch an act while sipping on a pint of lager throughout the day, rather than doing vodka shots at the bar, where you're missing the action! Interestingly, people also tend to drink much less wine and cocktails during the festival. When it came to food, festival-goers overwhelmingly opt for pizza as their cuisine of choice, naturally due to the more sociable nature of sitting down to share one together. Burgers came in second, followed by wraps.
Whether to bring boots or sunglasses?
With so many people outside camping, the weather has a big impact on the festival, so we wanted to look at weather trends. Glastonbury is known for getting muddy at the best of times, but is it fair to think attendees will need to take their wellies (aka wellington boots) for most of the next festival?
Taking data from the last five years and using our forecasting feature, we wanted to see what visitors can expect in 2019. The visual above looks both at rainfall and temperatures throughout 2019. Focusing in to the end of June, which is when Glastonbury will take place, you can see that it looks like it's going to rain a fair amount—more in 2019 than in 2017—and it also won't be as warm. So, we'd advise anyone who gets a ticket to be prepared!
Fields of data to explore
As you can see, with some simple data sets, you can get some great insights into the running of one of the world's largest festivals. From a business perspective, as well as being good information for event organizers to see who is attending the festival, visualizing data like this is great for all Glastonbury affiliates, vendors and other third-party sellers
Space is now limited at the festival, so vendors need to choose the stock they take with them carefully. With the above information, imagine how a clothing vendor at the festival can predict the levels and kinds of stock to take with them – should they take more wellington boots, more plastic coats—or more sun cream? Or, say a limited food vendor wanted to expand their offerings – they could see easily from this information that there's a market for more burger sales…
Do you use a lot of data and want to move on from trying to make sense of it all in spreadsheets? Consider creating data visualizations, included in Oracle Analytics Cloud. You can find out more and view a short demo here.