Data analyst might sound like a forbiddingly technical job title, but the truth is most of us do a little data analysis every day. Ever check to see how many likes your latest selfie got on Instagram? You’re using data analytics! You probably have some kind of ballpark idea in your mind about what constitutes a “good” amount of likes. How you arrived at that ballpark figure is a little subconscious pattern recognition – egopowered data analytics.
How do you know whether or not you can afford, say, an expensive new outfit, or tickets to a concert? You know how much money you have currently, but you probably don’t know, exactly, what your income and expenses will be in the near future. Instead you have a general idea, based on how much you usually make, and how much you usually spend. When you’re figuring out what you can and cannot afford, you’re using data analytics to make an educated guess about the state of your finances.
For brands, there is a lot of money in figuring out what kinds of content will get the most likes. For just about any company, there is a lot of value in figuring out likely expenditures, of being able to project costs or revenue – or model any number of other data sets that might inform business decision making. BrainStation’s Data Analytics course helps you take the basic data analytics skills that you probably already use, and apply them in a structured manner for personal and professional development.
Breakthroughs in communication technology have made it possible to access an incredible amount of data quickly and in expensively. There’s a word for this: Big Data. In the age of Big Data, data analytics has become such a varied field that listing out everything it touches would be a Herculean project. I have, though, created a short list of some ways data analytics is changing the world. Some of them might surprise you:
Data analytics has long been used to determine which shows survive, and which ones get cancelled . Networks use the Nielsen Rating, as core that uses statistical sampling to determine what people are watching, who is watching it, and for how long. The rates that advertisers pay to air commercials on TV programs is based on Nielsen’s data – next time you get angry about the unjust cancellation of your favorite show, you have data analytics to blame!
Data analytics is actually starting to influence not just whether or not shows survive, but what shows get made in the first place. Streaming video gives content distributors far greater access to data than telephone surveys can, which is in turn opening up new ways to utilize data. Netflix famously used a data driven approach when deciding to produce House of Cards.
To some extent, sports fans have always been data analytics freaks. Famous athletes are remembered for their stats as much as they are for individual games. A recent example of this is Stephen Curry – fans might have favourite Curry moments that they treasure, but it’s all of the records he’s broken that will cement his place in sports history. The rise of fantasy sports – for the uninitiated, a game where virtual teams use the stats based performance of real players to determine the outcome – is another example of how numbers obsessed sports fans can get.
Data analytics isn’t just for the fans, though. Movies like Moneyball have done a lot to popularize the importance of data analytics in sports. Major decisions, such as which athletes are drafted, are made using data analytics that factor in not only on court statistics, but the biometrics of the players.
In much the same way that streaming video changed how much data was available to distributors, and what they could do with it, the music industry has turned to big data to find ways to make money in the face of dropping record sales. The business model for streaming services like Spotify hinge on being able to use data to predict what users want to hear. Shazam’s business model involves gathering data on user locations and shopping habits, as well as by acting as an iBeacon – a consumer access point to the Internet of Things, which allows for, among other things, an unprecedented level of targeting by advertisers.
There are fun applications for data too – personally I love this visualization of “The Largest Vocabulary in Hip Hop.” It’s actually a pretty good primer on what a data analyst does – the author takes a look at the numbers, examines different factors that might have informed the data, like geographic region, and makes educated conjectures about what his findings might mean.
Data analytics plays a big part in which ads are shown to you, and on which sites. Ads on Google and Facebook use profiling models and ad targeting to show you things your data implies you might like. It considers your preferences, browsing history, the things you like on facebook, the things your friends like on facebook, and so on.
Recommendation engines, like the ones used by Amazon, act in a similar way. Instead of tracking your specific data, though, it looks at the things you’re browsing, and the purchases you’ve made, and shows you other things that people with similar purchase histories have also bought. That’s why when you buy, say, a phone, all of a sudden you will see things like phone cases and glass protectors in your feed.
That’s just the tip of the iceberg. There are so many ways that we can use data today. Organizations in every sector are finding ways to use data analytics to their advantage. Unfortunately, there is a real shortage of qualified data analysts. That’s where bootcamps like BrainStation come in. Our mission is to help individuals and organizations learn how to harness the opportunities Big Data is opening up.
If you’re interested in learning more, swing by one of our events or workshops.
If you think you’d make a good data analyst or would like to incorporate best practices into you and your team’s work flow, consider taking our data analytics course.
Jason Field is the founder of BrainStation.