So here’s the deal – I haven’t taken a math class since high school. Although in my defense, I did pretty well. My college required me to take one math or science class. I took intro to computer programming, but the majority of the material went straight over my head.
Yet here I am in a data-driven company, where I look at data all day and then write about it them (I recently learned that from a data scientist’s perspective, “data” is plural, though it sounds a bit strange).
I’ll admit that before I started, I was freaking out. What qualifications do I have to look at data? “Big Data” and “Data-Driven” are daunting words. The world is starting to revolve more and more around them, and with my math background, or lack thereof, I had my doubts about how much value I could add. Maybe I should have taken an extra math class…or four.
Little did I know that working with data isn’t impossible for the math-adverse like myself. I say that as a front-end user, and not the data scientist behind the numbers. They might say otherwise.
In my few months so far, here are a few personal and professional lessons that I’ve learned.
1) Assumptions and hypotheses can’t be trusted
As we’ve written about, 84% of people believe in trusting their gut. At the risk of sounding like a terrible person, trusting your gut is not always reliable. Sometimes before I even run a question through our system, which is then answered by thousands of people, I immediately assume what the results will be and begin to write. When the answers come back around, I’m often dismayed at the fact that I have to start over again. I repeat, all over again.
As humans, we err constantly. We’re hardwired and “soft-wired” with ideas of how things and people work, and we too often cling to those ideas even when proven otherwise.
Though data aren’t always objective, they provide a safeguard against those assumptions. They enable people to act – or in my case, write – on the correct information. This has enormous relevance for both personal and business decisions.
If I always had a way to quantitatively prove (or disprove) my assumptions throughout my life, I would have been spared many embarrassing moments, wasted friendships, and yes, even heartbreak. Who knew data could get so personal?
2) Phrasing is everything.
One wrong word can botch what the numbers actually reveal.
As an example, let’s imagine that I notice 56% of adults prefer vanilla ice cream. Could I say:
- Adults are more likely to prefer vanilla ice cream?
- Adults are most likely to prefer vanilla ice cream?
- If someone likes vanilla ice cream, they’re more likely to be an adult?
- Adults are more likely than children to like vanilla ice cream?
Hopefully you get the gist. This is where data can get a little tricky, or maybe it’s just me. I’m still getting the hang of all of this, after all.
Words – ugh. Am I right?
3) Creativity Is An Inherent Part of Data
I always had this mental image that working with data was dry – and that’s just not me. Eccentricity is my middle name.
To my surprise, I’ve found working with data to be creatively stimulating. It may not be the same as developing photos in the darkroom or working with film, but it has a long list of pros.
There are millions of correlations that can be made, but which ones to start with? How do the numbers need to be displayed, illustrated and narrated?
In our database, we have asked over 60,000 questions. When looking at one question, there are infinite possible stories to uncover – and it takes a little imagination to put the pieces together.
4) The Internet Isn’t Always Invasive
Though invasion of privacy is a growing concern, and many companies collect information without us even knowing it, that’s not always the case. There are opportunities to collect data in ways that are non-intrusive. For example, CivicScience doesn’t collect any personally identifiable information. We only collect data that someone is willing to provide freely. That’s something I really appreciate.
Faith in the internet – check.
I Will Conquer You, Numbers
Though I may never be a data scientist, it’s impossible to negate the importance of data and making data-driven decisions in the world. Learning how to navigate all of this information that we have at our disposal is crucial.
Imagine what all of this information could mean for businesses, small and large. Which assumptions are misleading and costly? What correlations are missing? An example, you ask?
Recently, we discovered that there was an insanely high correlation between fans of Kia cars, and fans of Gary Busey. Soon after, a local Kia dealership ran an ad with the notorious actor. Starting to see the practicality?
I’m genuinely excited to continue learning about data, and to share these ideas, insights, and at times, frustrations, as they come. I hope this might compel at least a few readers, who come from more liberal arts and artistic backgrounds like myself, to think again about numbers. Give ’em a shot!