Introduction: That Analyst Life

I am putting some of my essays into a book, to be published very soon. From the book’s introduction:



 My jump from a cozy liberal arts college to the gray felt walls of corporate finance did not go so well.

After receiving a bachelor’s in economics, I even took a “gap year” of sorts, getting a master’s degree in finance. I wanted some shot at success.

It still wasn’t enough. Like many analysts, I was passed through a baptism by fire of lousy spreadsheets, unrealistic report requests, and late nights doing work that certainly didn’t require a graduate degree from a leading business school.

In the meantime, I began a blog at I had a couple of motivations.

First, I needed a creative outlet. The lack of creativity at my job frustrated me. A liberal arts grad, I need to write.

But there was a more utilitarian reason. I wanted help.

I thought blogging would connect me with those who could help. I just wanted some spreadsheet answers. I got much more.

Not only have I connected with authorities in business analytics and spreadsheet modelling. I have also developed an idea of what makes a good analyst.

A liberal arts grad who loves spreadsheets? Madness!

From the beginning, my blog has focused more on discursive analysis than heavy Excel tips and tricks. Specifically, I wrote with data-addled liberal arts grads in mind.

That meant essays on the nature of education and how to network. How to build a model like Abraham Lincoln and how to build a following on Twitter.

With each of these posts a picture of a good analyst emerges.

I’ve grouped them into three categories. The best analyst has used education to his advantage. He is data literate. And he has built a following.

First, education. Why are graduates so unprepared for the working world? I argue that we have failed to integrate academia into society at large.

The internet has allowed people to build viable businesses from nearly any passion. Yet we are still arguing whether college should focus on the humanities or the trades. It’s a meaningless distinction.

Second, data literacy. Often the first complaint about office reporting is “this shouldn’t be in Excel.” Sometimes, that’s true. The problem runs deeper: better software alone is not the answer. Data literacy comes first. This chapter is about how to think about and use data.

Third, a following. Analysts don’t leave their cubes much. This makes it easy to see work as something to leave at the office and pick up tomorrow.

Fortunes are now made not by accumulating capital but by sharing ideas. Analysts too need to build a following online — something bigger than just what goes on at the office.

I hope these essays get you thinking about data, networking, and career development.

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