Exploring Data

Exploring Data Is Fun!

The Data Inspired Professional

When you work in the information industry, you’re expected to be “data inspired”. If you do not get excited by analyzing data and creating insights, you’re in the wrong industry. Just like a car dealer is expected to love cars. That’s how you stay motivated at work: you enjoy it.

For data scientists this is even more intense. Often enough I’ve heard of data scientists having taken a job that might not have paid best (i.e. they had higher offers) but it offered them an exciting playground: vast amounts of data to explore; data that they believed could hold many yet unknown insights. Discovery, exploration: that’s what attracts data professionals to such jobs.

An Intern Project

I’m currently supervising an intern that works in my team. I hired him to perform a task of exploring large datasets. As he started his internship I presented his task to him. This task, however, was just a starting point, I said. As we progress we will see where the journey takes us. I was confident that there are interesting insights hidden in the data, and I had some ideas, but more than having these ideas, I was confident that there’s more!

Data + Curiosity = Insights!

After spending time to explore and understand the large amounts of data, the intern had presented to me results. The data showed that my hypothesis was correct in some cases, and was incorrect in some cases; just as we wanted to show. We just did not know in which cases it would be correct, and in which cases it would not. Now we know. We have gained valuable insights. For me the goals have now been achieved, but the intern is a curious person. Remember: data scientists enjoy “playing” with data, exploring it. And so the intern decided to perform yet another analysis with the same data, aiming to explore other types of correlations! Beautiful curiosity!

Unexpected Results for Data ANalysis

The intern examined correlation between two variables, with the aim to use this correlation to perform another analysis when there is insufficient data about one of the variables. Interesting idea. But once I had reflected on it more, I came to the conclusion that this won’t work because this other analysis already took into consideration the correlation between both variables. And thus no new insights would be gained. Disappointing? Definitely not. Namely, as we were talking I realized that while this correlation does not add extra insights to the analysis that the intern has intended, it does add new insights to totally different insights that we can generate from the datasets. Insights that neither I nor the intern had initially thought of. And that how we found new insights, new knowledge, by “playing” with the data. Creativity in data analysis pays off!

 

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Ziv Baida

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