Think Like A Data Scientist

30 Jan 2019 | Data Driven Thinking, Marketing

Data is literally everywhere these days and with it a new role: The Data Scientist. We're not suggesting you need one (or that you should become one), but it might help to think like one if you want to get the most from your arts data.

I am going to throw down a few caveats before I start this post. I am not a data scientist. I am an arts guy. Well, scratch that. I am a marketing guy. And, well, a sales guy, and a box office guy and hell I’ve even been known to be a tech guy in my time.

If my multi-hat wearing life sounds familiar (and if you work in the arts sector - I bet it does), then you’ll also understand why I say I am a data guy too - but not a data scientist.

As arts organisations, we gather a lot of data (and of course we are GDPR compliant). That's why we have talked a lot about datadata-driven thinking and helped loads of organisations in the Ticketsolve community get the most out of their data to help them better engage with their audiences.

And as we get more and more involved with our data, I think it means we need to start thinking a little differently.

What is a Data Scientist?

Data science has been around since the 1960s - at least that is when it first got its label. But as a discipline - specifically as a legitimate department in larger organisations - that is much more recent. Alongside the rise of the Internet has come data - mountains of data. The people tasked with understanding and interpreting all of this complex digital data are data scientists - and they are becoming more and more indispensable to institutions and decision making.

So ultimately, all this data is analysed by people. People that have a very particular set of skills (that have nothing to do with Liam Neeson sadly). So while you won’t be tracking down any kidnappers using our post, you will most certainly be able to get more out of your data and data analysis and hopefully think more like a data scientist.



While your first thought when it comes to data might not be empathy - it should. Data is only as good as how you use it to help your customers and engage with them. Of course, data can help you sell more, but our ultimate goal is building relationships with our community and patrons. We cannot do that without putting ourselves in our customers' shoes. The best way for your data to have meaning is to have empathy at its core and think like your customers.

Think Insight Not Information


You might think that data is all about information, but it is really about the insight you can pull out from that information. Information is just that - data points. Insight is what drives action - it makes that data useful! But how do you do this? There are two key things to think about when looking at your data:

  1. What are the main takeaways (stick to a max of three if you can)?

  2. What actions can we make based on the above?

Adopt a Beginners Mindset


Okay, we are going to get Zen for a minute - literally. A beginner’s mindset (from Zen Buddhist philosophy) forces you to “have an attitude of openness, eagerness, and lack of preconceptions when studying a subject, even when studying at an advanced level, just as a beginner in that subject would.”

So rather than thinking that you already know the answer your data will give you, keep an open mind to what the data is telling you. Imagine yourself as a beginner in your job looking at this data for the first time. This technique of a beginner’s mindset is especially good when you are trying to solve a problem or get creative; beginners often have limitless possibilities in mind, experts are often limited by what they already know.

Challenge Your Assumptions

The notion of challenging your assumptions is about questioning everything, and it goes hand in hand with adopting a beginners mindset when considering your data. Be ruthless here. Talk to your team. Dig down into what you and your team deeply hold to be true - and see if the data reflects that. And don’t be afraid to change tack if what you are doing doesn’t work. A good data scientist is ready to shift gears if the data is leading them elsewhere.

Know What You Don’t Know

With all this data and analysis you would assume that you would have a nice full picture of your audiences. But chances are you won’t. There are gaps, black spots in an otherwise gorgeous audience picture. Knowing what those blind spots are can help you formulate a plan to get the information you need to develop a better picture of your customers.

So there you have it! If you want help tackling your data give us a shout - we’d love to help you gain insight into your audiences.

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