Four Things About Being An Industry Data Analyst That You Should Know

Then you go at it, using your analytics skills and knowledge of the database(s) to craft a well-tuned response to the question at hand. 

It’s fairly go-at-your-own-pace though there are some deadlines that you definitely need to meet.

Once you’re done, you submit the final product (e.g., an excel spreadsheet, a graph, a page of text) to whoever you report to or look to for guidance and they double check your work.

If everything is good, you submit the deliverable and - barring any errors you made or additional items that the OG requestor needs - then you’re done.

Another day, another data request, another dollar. 

2. It’s Easier Than You Think But You Will Have to Grow

If you are familiar with data analysis from grad school or beyond (particularly quantitative data analysis), you can do the work of a data analyst. 

However, you probably won’t show up on Day 1 ready to accomplish everything that might get thrown at you as a new industry data analyst. 

You will inevitably need to gain additional skills. Here are some examples of skills I had to learn and continue to learn daily: 

• Before even applying, I taught myself the basics of SQL just to be competitive in the data analyst job market. Honestly if you want to become an industry data analyst you’ll probably need to learn that language (it’s basically Excel - but without the GUI - for big businesses). There’s a handful of other languages and tools that I had to learn (most of which I actually learned on the job, not before) but SQL is probably the biggest one that’s worth pointing out. 

• I’m learning large database management as we speak. I interact with at least a dozen very, very large databases everyday, which is very new to me as a former social scientist. I’m used to hundreds to thousands of rows’ (or participants’) worth of data. These days I’m typically interacting with tens to hundreds of thousands if not millions of rows. It’s a different beast than running a moderation analysis on 300 participants and it requires a whole new way of thinking about data (plus a lot of patience in waiting for analyses to complete).

Sidebar: 

I know you might be feeling a little anxious or even discouraged after reading about the skills I learned and am learning to be an industry data analyst - ugh, I have to learn something new? But I’ve already learned so many things??

I don’t want you to be discouraged. You can do it. You learned to analyze data for grad school, and you can learn a new way of doing it for a new job. And trust me, you’ll thank yourself for it the day you get that first paycheck that’s free of academic toxicity and overwork. 

3. The Research Questions We Ask: It’s Not About Interesting, It’s About Useful 

I heard this from a YouTube video months ago when I was actively applying for jobs.

Data work in industry differs from academia in one core piece of philosophy:

It’s not about interesting, it’s about useful.

Academic work - though often touted to be about “saving the world” in some way - typically is really about doing something so interesting (see “novel”) with data that you woo reviewers into letting you publish that hot little piece of data-ass in a nice journal. Voila, world saved!

Industry data work really doesn’t care about “interesting” that much. 

It’s much more focused on the bottom line for the organization. 

For example, an industry data analyst might be working to answer a question with data in order to:

• Deliver a better product 

• Ensure customers are satisfied 

• Find errors in workflows 

• Identifying trends in who-purchases-what 

Sometimes useful pieces of information are interesting, but interesting findings aren’t always useful - something that academics should probably take to heart.

Which brings me to my last point…

4. Just Because Your Work Doesn’t Feel Impassioned Doesn’t Mean it Isn’t Accomplishing Your Goals

Do I feel the same sense of passion when I am crunching these large healthcare databases for information compared to when I was in academia and working on research problems I selected?

No, it’s not the same.  

But that’s not necessarily a bad thing if you see the bigger picture.

I liked the work in academia because it was intellectually stimulating. But the main reason that I was drawn to it was because I wanted to make a difference.

I particularly wanted to make a difference with the population that I saw as the most down-trodden: abused and neglected youth. 

And I think in the few years of clinical work I did, I probably did make some difference.

But did my publications make a difference?

Probably not.

And herein lies the wrinkle - the work I do now in healthcare probably has just as much or more of a direct impact on the population I set out to serve than my research in academia ever did.

Does it feel like it? No. But feelings can be deceptive. 

And I know that the reason that many folks continue to try to make a career in academia work for them is because of that feeling of wanting to help. 

But let’s be honest: very, very little research actually has a direct positive impact on the world. 

At some point I realized that the bigger picture is more important than my feelings.

Conclusion

The world needs data analysts and you likely need a job.

A real job. One with a paycheck. Not some dream of notoriety or prestige. 

The need for data analysts in industry continues to grow along with the pay, meaning you can likely have job security for a long, long time (until the aliens come or robots destroy us all) should you take this route. 

It’s a route I’m glad I took.

-Matt