Data Science: Both Big & Thick Data

During my drive home fighting through traffic I listened to the latest episode of the Talking Code podcast. This episode was all about data science; what it is and how you can get some. It brought me back to the original reason why I wanted to learn to code in the first place.

I wanted more tools in my anthropological toolkit. I wanted to use my skills of analysis and assessment in a larger way. Being and anthropologist taught me how to understand every data point in context. Being an analyst taught me how to visualize this data and query this data in ways that companies could respond to. Learning to code would complete the trifecta that would prime me to be an awesome data scientist, and therefore innovator.

It all started with a little love for statistics.

I first found statistics in high school. I was good at math growing up, loved algebra (even taking my algebra class in summer school so that I could do it early). Then came geometry and trigonometry, and that love affair started to turn sour. Statistics was my saving grace. It was math in context of something real and I loved learning about it. That love for statistics was one of the reasons why I studied Psychology undergrad. It gave me a way to learn how to ask really good questions, and analyze the answers in an interesting and meaningful way. The same with Anthropology, but in an even broader scale. Now I could study everything from genomics to marriage rituals.

When I started working in the corporate world, I looked for the natural overlap between my studies and my work. I found it in analysis. I had some statistical background from my social science degrees, as well as a holistic view of how systems worked. Being an analyst was a natural fit.

I knew that analysis was all about the quality of the data coming in, and the ability to understand that data when it comes out. Context is everything. You have to be able to ask the right questions. You have to be inquisitive and question the obvious. Thick data is all about context and not always so easily digitized. As an anthropologist I am trained to perceive and analyze this data along with everything else. It is akin to what is called domain knowledge in business. It is the info about the info. The stuff that you just know but don’t always have a way to describe. When you marry that skill to technical analysis, great things can happen. This should be how we do data science in the future.

I am not the only one who thinks so.

Although Big Data is a newer phenomenon, Thick Data is not. There are many social scientist out there who are talking about how bringing these two together is not just a cool thing, but even an necessary one. There are articles like this, this,this, and this are popping up to talk about the impact of using all of these skills to analyze data. The impact is real.

I am all about making an impact. And I love my data. Its beautiful how it all comes together when you sprinkle a little code in the mix.